diff --git a/_data/members.yml b/_data/members.yml
index 5f67861d..b823e86e 100644
--- a/_data/members.yml
+++ b/_data/members.yml
@@ -15,9 +15,9 @@
position: Postdoctoral Researcher
avatar: /images/people/Marco-Giulini.jpeg
-- name: Victor Rey
+- name: Victor Reys
position: Postdoctoral Researcher
- avatar: /images/people/Victor-Rey.png
+ avatar: /images/people/Victor-Reys.png
- name: Raphaelle Versini
position: Postdoctoral Researcher
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+
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@@ -0,0 +1,2555 @@
+---
+layout: page
+title: "Low-sampling antibody-antigen modelling tutorial using a local version of HADDOCK3"
+excerpt: "A tutorial describing the use of HADDOCK3 in the low-sampling scenario to model an antibody-antigen complex"
+tags: [HADDOCK, HADDOCK3, installation, preparation, proteins, docking, analysis, workflows, sampling]
+image:
+ feature: pages/banner_education-thin.jpg
+---
+This tutorial consists of the following sections:
+
+* table of contents
+{:toc}
+
+
+
+
+## Introduction
+
+This tutorial demonstrates the use of the new modular HADDOCK3 version for predicting
+the structure of an antibody-antigen complex using knowledge of the hypervariable loops
+on the antibody (i.e., the most basic knowledge) and epitope information identified from NMR experiments for the antigen to guide the docking.
+
+An antibody is a large protein that generally works by attaching itself to an antigen,
+which is a unique site of the pathogen. The binding harnesses the immune system to directly
+attack and destroy the pathogen. Antibodies can be highly specific while showing low immunogenicity (i.e. the ability to provoke an immune response),
+which is achieved by their unique structure. **The fragment crystallizable region (Fc region)**
+activates the immune response and is species-specific, i.e. the human Fc region should not
+induce an immune response in humans. **The fragment antigen-binding region (Fab region**)
+needs to be highly variable to be able to bind to antigens of various nature (high specificity).
+In this tutorial, we will concentrate on the terminal **variable domain (Fv)** of the Fab region.
+
+
+
+
+
+The small part of the Fab region that binds the antigen is called **paratope**. The part of the antigen
+that binds to an antibody is called **epitope**. The paratope consists of six highly flexible loops,
+known as **complementarity-determining regions (CDRs)** or hypervariable loops whose sequence
+and conformation are altered to bind to different antigens. CDRs are shown in red in the figure below:
+
+
+
+
+
+In this tutorial we will be working with Interleukin-1β (IL-1β)
+(PDB code [4I1B](https://www.ebi.ac.uk/pdbe/entry/pdb/4i1b){:target="_blank"}) as an antigen
+and its highly specific monoclonal antibody gevokizumab
+(PDB code [4G6K](https://www.ebi.ac.uk/pdbe/entry/pdb/4g6k){:target="_blank"})
+(PDB code of the complex [4G6M](https://www.ebi.ac.uk/pdbe/entry/pdb/4g6m){:target="_blank"}).
+
+
+Throughout the tutorial, colored text will be used to refer to questions or
+instructions, and/or PyMOL commands.
+
+This is a question prompt: try answering it!
+This an instruction prompt: follow it!
+This is a PyMOL prompt: write this in the PyMOL command line prompt!
+This is a Linux prompt: insert the commands in the terminal!
+
+
+
+
+
+## HADDOCK general concepts
+
+HADDOCK (see [https://www.bonvinlab.org/software/haddock2.4](https://www.bonvinlab.org/software/haddock2.4){:target="_blank"})
+is a collection of python scripts derived from ARIA ([https://aria.pasteur.fr](https://aria.pasteur.fr){:target="_blank"})
+that harness the power of CNS (Crystallography and NMR System – [https://cns-online.org](https://cns-online.org){:target="_blank"})
+for structure calculation of molecular complexes. What distinguishes HADDOCK from other docking software is its ability,
+inherited from CNS, to incorporate experimental data as restraints and use these to guide the docking process alongside
+traditional energetics and shape complementarity. Moreover, the intimate coupling with CNS endows HADDOCK with the
+ability to actually produce models of sufficient quality to be archived in the Protein Data Bank.
+
+A central aspect of HADDOCK is the definition of Ambiguous Interaction Restraints or AIRs. These allow the
+translation of raw data such as NMR chemical shift perturbation or mutagenesis experiments into distance
+restraints that are incorporated into the energy function used in the calculations. AIRs are defined through
+a list of residues that fall under two categories: active and passive. Generally, active residues are those
+of central importance for the interaction, such as residues whose knockouts abolish the interaction or those
+where the chemical shift perturbation is higher. Throughout the simulation, these active residues are
+restrained to be part of the interface, if possible, otherwise incurring a scoring penalty. Passive residues
+are those that contribute to the interaction but are deemed of less importance. If such a residue does
+not belong in the interface there is no scoring penalty. Hence, a careful selection of which residues are
+active and which are passive is critical for the success of the docking.
+
+
+
+
+
+## A brief introduction to HADDOCK3
+
+HADDOCK3 is the next generation integrative modelling software in the
+long-lasting HADDOCK project. It represents a complete rethinking and rewriting
+of the HADDOCK2.X series, implementing a new way to interact with HADDOCK and
+offering new features to users who can now define custom workflows.
+
+In the previous HADDOCK2.x versions, users had access to a highly
+parameterisable yet rigid simulation pipeline composed of three steps:
+`rigid-body docking (it0)`, `semi-flexible refinement (it1)`, and `final refinement (itw)`.
+
+
+
+
+
+In HADDOCK3, users have the freedom to configure docking workflows into
+functional pipelines by combining the different HADDOCK3 modules, thus
+adapting the workflows to their projects. HADDOCK3 has therefore developed to
+truthfully work like a puzzle of many pieces (simulation modules) that users can
+combine freely. To this end, the “old” HADDOCK machinery has been modularized,
+and several new modules added, including third-party software additions. As a
+result, the modularization achieved in HADDOCK3 allows users to duplicate steps
+within one workflow (e.g., to repeat twice the `it1` stage of the HADDOCK2.x
+rigid workflow).
+
+Note that, for simplification purposes, at this time, not all functionalities of
+HADDOCK2.x have been ported to HADDOCK3, which does not (yet) support NMR RDC,
+PCS and diffusion anisotropy restraints, cryo-EM restraints and coarse-graining.
+Any type of information that can be converted into ambiguous interaction
+restraints can, however, be used in HADDOCK3, which also supports the
+*ab initio* docking modes of HADDOCK.
+
+
+
+
+
+To keep HADDOCK3 modules organized, we catalogued them into several
+categories. However, there are no constraints on piping modules of different
+categories.
+
+The main module categories are "topology", "sampling", "refinement",
+"scoring", and "analysis". There is no limit to how many modules can belong to a
+category. Modules are added as developed, and new categories will be created
+if/when needed. You can access the HADDOCK3 documentation page for the list of
+all categories and modules. Below is a summary of the available modules:
+
+* **Topology modules**
+ * `topoaa`: *generates the all-atom topologies for the CNS engine.*
+* **Sampling modules**
+ * `rigidbody`: *Rigid body energy minimization with CNS (`it0` in haddock2.x).*
+ * `lightdock`: *Third-party glow-worm swam optimization docking software.*
+* **Model refinement modules**
+ * `flexref`: *Semi-flexible refinement using a simulated annealing protocol through molecular dynamics simulations in torsion angle space (`it1` in haddock2.x).*
+ * `emref`: *Refinement by energy minimisation (`itw` EM only in haddock2.4).*
+ * `mdref`: *Refinement by a short molecular dynamics simulation in explicit solvent (`itw` in haddock2.X).*
+* **Scoring modules**
+ * `emscoring`: *scoring of a complex performing a short EM (builds the topology and all missing atoms).*
+ * `mdscoring`: *scoring of a complex performing a short MD in explicit solvent + EM (builds the topology and all missing atoms).*
+* **Analysis modules**
+ * `alascan`: *Performs a systematic (or user-define) alanine scanning mutagenesis of interface residues.*
+ * `caprieval`: *Calculates CAPRI metrics (i-RMSD, l-RMSD, Fnat, DockQ) with respect to the top-scoring model or reference structure if provided.*
+ * `clustfcc`: *Clusters models based on the fraction of common contacts (FCC)*
+ * `clustrmsd`: *Clusters models based on pairwise RMSD matrix calculated with the `rmsdmatrix` module.*
+ * `contactmap`: *Generate contact matrices of both intra- and intermolecular contacts and a chordchart of intermolecular contacts.*
+ * `rmsdmatrix`: *Calculates the pairwise RMSD matrix between all the models generated in the previous step.*
+ * `ilrmsdmatrix`: *Calculates the pairwise interface-ligand-RMSD (il-RMSD) matrix between all the models generated in the previous step.*
+ * `seletop`: *Selects the top N models from the previous step.*
+ * `seletopclusts`: *Selects the top N clusters from the previous step.*
+
+The HADDOCK3 workflows are defined in simple configuration text files, similar to the TOML format but with extra features.
+Contrary to HADDOCK2.X which follows a rigid (yet highly parameterisable)
+procedure, in HADDOCK3, you can create your own simulation workflows by
+combining a multitude of independent modules that perform specialized tasks.
+
+
+
+
+
+## Software and data setup
+
+In order to follow this tutorial you will need to work on a Linux or MacOSX
+system. We will also make use of [**PyMOL**][link-pymol] (freely available for
+most operating systems) in order to visualize the input and output data. We will
+provide you links to download the various required software and data.
+
+Further, we are providing pre-processed PDB files for docking and analysis (but the
+preprocessing of those files will also be explained in this tutorial). The files have been processed
+to facilitate their use in HADDOCK and to allow comparison with the known reference
+structure of the complex.
+
+If you are running this tutorial on your own resources _download and unzip the following_
+[zip archive](https://surfdrive.surf.nl/files/index.php/s/ts2kMjBFxjaNeId){:target="_blank"}
+_and note the location of the extracted PDB files in your system_.
+If running as part of the EU-ASEAN HPC school see the instructions below.
+
+_Note_ that you can also download and unzip this archive directly from the Linux command line:
+
+
+wget https://surfdrive.surf.nl/files/index.php/s/ts2kMjBFxjaNeId/download -O HADDOCK3-antibody-antigen.zip
+unzip HADDOCK3-antibody-antigen-BioExcel.zip
+
+
+
+Unziping the file will create the `HADDOCK3-antibody-antigen-BioExcelSS2024` directory which should contain the following directories and files:
+
+* `pdbs`: a directory containing the pre-processed PDB files
+* `restraints`: a directory containing the interface information and the corresponding restraint files for HADDOCK3
+* `runs`: a directory containing pre-calculated results
+* `scripts`: a directory containing various scripts used in this tutorial
+* `workflows`: a directory containing configuration file examples for HADDOCK3
+
+
+
+
+### EU-ASEAN 2023 HPC school
+
+We will be making use of the Fugaku supercomputer for this tutorial.
+Please connect to Fugaku using your credentials.
+
+The software and data required for this tutorial have been pre-installed on Fugaku.
+In order to run the tutorial, first copy the required data into your home directory on Fugagku:
+
+
+unzip /vol0300/share/ra022304/LifeScience/20231213_Bonvin/HADDOCK3-antibody-antigen.zip
+
+
+This will create the `HADDOCK3-antibody-antigen` directory with all necessary data and scripts and job examples ready for submission to the batch system.
+
+HADDOCK3 has been pre-installed on the compute nodes. To test the installation, first create an interactive session on a node with:
+
+
+
+pjsub \-\-interact \-L \"node=1\" \-L \"rscgrp=int\" \-\-sparam \"wait-time=600\" -L \"elapse=01:00:00\"
+
+
+Once the session is active, activate HADDOCK3 with:
+
+
+source /vol0300/share/ra022304/LifeScience/20231213_Bonvin/miniconda3/etc/profile.d/conda.sh
+conda activate haddock3
+
+
+You can then test that `haddock3` is indeed accessible with:
+
+
+haddock3 -h
+
+
+You should see a small help message explaining how to use the software.
+
+
+
+ View outputexpand_more
+
+
+(haddock3)$ haddock3 -h
+usage: haddock3 [-h] [--restart RESTART] [--extend-run EXTEND_RUN] [--setup]
+ [--log-level {DEBUG,INFO,WARNING,ERROR,CRITICAL}] [-v]
+ recipe
+
+positional arguments:
+ recipe The input recipe file path
+
+optional arguments:
+ -h, --help show this help message and exit
+ --restart RESTART Restart the run from a given step. Previous folders from the
+ selected step onward will be deleted.
+ --extend-run EXTEND_RUN
+ Start a run from a run directory previously prepared with the
+ `haddock3-copy` CLI. Provide the run directory created with
+ `haddock3-copy` CLI.
+ --setup Only setup the run, do not execute
+ --log-level {DEBUG,INFO,WARNING,ERROR,CRITICAL}
+ -v, --version show version
+
+
+
+In case you want to obtain HADDOCK3 for another platform, navigate to [its repository][haddock-repo], fill the
+registration form, and then follow the [installation instructions](https://www.bonvinlab.org/haddock3/INSTALL.html){:target="_blank"}.
+
+
+
+
+### Local setup (on your own)
+
+If you are installing HADDOCK3 on your own system, check the instructions and requirements below.
+
+
+#### Installing HADDOCK3
+
+To obtain HADDOCK3 navigate to [its repository][haddock-repo], fill the
+registration form, and then follow the [installation instructions](https://www.bonvinlab.org/haddock3/INSTALL.html){:target="_blank"}.
+
+
+#### Installing CNS
+
+The other required piece of software to run HADDOCK is its computational engine,
+CNS (Crystallography and NMR System –
+[https://cns-online.org](https://cns-online.org){:target="_blank"}). CNS is
+freely available for non-profit organizations. To get access to all
+features of HADDOCK you will need to compile CNS using the additional files
+provided in the HADDOCK distribution in the `extras/cns1.3` directory. Compilation of
+CNS might be non-trivial. Some guidance on installing CNS is provided on the online
+HADDOCK3 documentation page [here](https://github.com/haddocking/haddock3/blob/main/docs/CNS.md){:target="_blank"}.
+
+Once CNS has been properly compiled, you will have create a symbolic link or copy the executable to `haddock3/bin/cns` and make sure it is executable and functional. Try starting `cns` from the command line. You should see the following output:
+
+
+
+ View CNS prompt outputexpand_more
+
+
+ ============================================================
+ | |
+ | Crystallography & NMR System (CNS) |
+ | CNSsolve |
+ | |
+ ============================================================
+ Version: 1.3 at patch level U
+ Status: Special UU release with Rg, paramagnetic
+ and Z-restraints (A. Bonvin, UU 2013)
+ ============================================================
+ Written by: A.T.Brunger, P.D.Adams, G.M.Clore, W.L.DeLano,
+ P.Gros, R.W.Grosse-Kunstleve,J.-S.Jiang,J.M.Krahn,
+ J.Kuszewski, M.Nilges, N.S.Pannu, R.J.Read,
+ L.M.Rice, G.F.Schroeder, T.Simonson, G.L.Warren.
+ Copyright (c) 1997-2010 Yale University
+ ============================================================
+ Running on machine: hostname unknown (Linux,64-bit)
+ Program started by: l00902
+ Program started at: 16:34:22 on 06-Dec-2023
+ ============================================================
+
+ FFT3C: Using FFTPACK4.1
+
+CNSsolve>
+
+
+
+Exit the CNS command line by typing `stop`.
+
+
+#### Auxiliary software
+
+**[PDB-tools][link-pdbtools]**: A useful collection of Python scripts for the
+manipulation (renumbering, changing chain and segIDs...) of PDB files is freely
+available from our GitHub repository. `pdb-tools` is automatically installed
+with HADDOCK3. If you have activated the HADDOCK3 Python environment you have
+access to the pdb-tools package.
+
+**[PyMOL][link-pymol]**: In this tutorial we will make use of PyMOL for visualization. If not
+already installed on your system, download and install PyMOL. Note that you can use your favorite visulation software but instructions are only provided here for PyMOL.
+
+
+
+
+
+## Preparing PDB files for docking
+
+In this section we will prepare the PDB files of the antibody and antigen for docking.
+Crystal structures of both the antibody and the antigen in their free forms are available from the
+[PDBe database](https://www.pdbe.org){:target="_blank"}.
+
+*__Important:__ For a docking run with HADDOCK, each molecule should consist of a single chain with non-overlapping residue numbering within the same chain.
+
+As an antibody consists of two chains (L+H), we will have to prepare it for use in HADDOCK. For this we will be making use of `pdb-tools` from the command line.
+
+_**Note**_ that `pdb-tools` is also available as a [web service](https://wenmr.science.uu.nl/pdbtools/){:target="_blank"}.
+
+
+
+
+### Preparing the antibody structure
+
+Using PDB-tools we will download the unbound structure of the antibody from the PDB database (the PDB ID is [4G6K](https://www.ebi.ac.uk/pdbe/entry/pdb/4g6k){:target="_blank"}) and then process it to have a unique chain ID (A) and non-overlapping residue numbering by renumbering the merged pdb (starting from 1).
+
+This can be done from the command line with:
+
+
+pdb_fetch 4G6K | pdb_tidy \-strict | pdb_selchain \-H | pdb_delhetatm | pdb_fixinsert | pdb_keepcoord | pdb_selres \-1:120 | pdb_tidy -strict > 4G6K_H.pdb
+
+
+pdb_fetch 4G6K | pdb_tidy \-strict | pdb_selchain -L | pdb_delhetatm | pdb_fixinsert | pdb_keepcoord | pdb_selres \-1:107 | pdb_tidy \-strict > 4G6K_L.pdb
+
+
+pdb_merge 4G6K_H.pdb 4G6K_L.pdb | pdb_reres \-1 | pdb_chain \-A | pdb_chainxseg | pdb_tidy \-strict > 4G6K_clean.pdb
+
+
+The first command fetches the PDB ID, selects the heavy chain (H) (`pdb_selchain`) and removes water and heteroatoms (`pdb_delhetatm`) (in this case no co-factor is present that should be kept).
+
+An important part for antibodies is the `pdb_fixinsert` command that fixes the residue numbering of the HV loops: Antibodies often follow the [Chothia numbering scheme](https://pubmed.ncbi.nlm.nih.gov/9367782/?otool=inluulib){:target="_blank"} and insertions created by this numbering scheme (e.g. 82A, 82B, 82C) cannot be processed by HADDOCK directly (if not done those residues will not be considered resulting effectively in a break in the loop).
+As such, renumbering is necessary before starting the docking.
+
+Then, the command `pdb_selres` selects only the residues from 1 to 120, so as to consider only the variable domain (FV) of the antibody. This allows to save a substantial amount of computational resources.
+
+The second command does the same for the light chain (L) with the difference that the light chain is slightly shorter and we can focus on the first 107 residues.
+
+The third and last command merges the two processed chains, renumber the residues starting from 1 (`pdb_reres`) and assign them unique chain and segIDs (`pdb_chain` and `pdb_chainxseg`), resulting in the HADDOCK-ready `4G6K_clean.pdb` file. You can view its sequence by running:
+
+
+pdb_tofasta 4G6K_clean.pdb
+
+
+_**Note**_ The ready-to-use file can be found in the `pdbs` directory of the provided tutorial data.
+
+
+
+
+### Preparing the antigen structure
+
+Using PDB-tools, we will now download the unbound structure of Interleukin-1β from the PDB database (the PDB ID is [4I1B](https://www.ebi.ac.uk/pdbe/entry/pdb/4i1b){:target="_blank"}), remove the hetero atoms and then process it to assign it chainID B.
+
+*__Important__: Each molecule given to HADDOCK in a docking scenario must have a unique chainID/segID.*
+
+
+pdb_fetch 4I1B | pdb_tidy \-strict | pdb_delhetatm | pdb_keepcoord | pdb_chain \-B | pdb_chainxseg | pdb_tidy \-strict > 4I1B_clean.pdb
+
+
+
+
+
+
+## Defining restraints for docking
+
+Before setting up the docking, we first need to generate distance restraint files in a format suitable for HADDOCK.
+HADDOCK uses [CNS][link-cns]{:target="_blank"} as computational engine.
+A description of the format for the various restraint types supported by HADDOCK can be found in our [Nature Protocol][nat-pro]{:target="_blank"} paper, Box 4.
+
+Distance restraints are defined as follows:
+
+
+
+The lower limit for the distance is calculated as: distance minus lower-bound correction
+and the upper limit as: distance plus upper-bound correction.
+
+The syntax for the selections can combine information about:
+
+* chainID - `segid` keyword
+* residue number - `resid` keyword
+* atom name - `name` keyword.
+
+Other keywords can be used in various combinations of OR and AND statements. Please refer for that to the [online CNS manual][link-cns]{:target="_blank"}.
+
+E.g.: a distance restraint between the CB carbons of residues 10 and 200 in chains A and B with an
+allowed distance range between 10Å and 20Å would be defined as follows:
+
+
+assign (segid A and resid 10 and name CB) (segid B and resid 200 and name CB) 20.0 10.0 0.0
+
+
+
+Can you think of a different way of defining the distance and lower and upper corrections while maintaining the same
+allowed range?
+
+
+
+
+
+### Identifying the paratope of the antibody
+
+Nowadays several computational tools can identify the paratope (the residues of the hypervariable loops involved in binding) from the provided antibody sequence.
+In this tutorial, we are providing you with the corresponding list of residue obtained using [ProABC-2](https://wenmr.science.uu.nl/proabc2/){:target="_blank"}.
+ProABC-2 uses a convolutional neural network to identify not only residues which are located in the paratope region but also the nature of interactions they are most likely involved in (hydrophobic or hydrophilic).
+The work is described in [Ambrosetti, *et al* Bioinformatics, 2020](https://academic.oup.com/bioinformatics/article/36/20/5107/5873593){:target="_blank"}.
+
+The corresponding paratope residues (those with either an overall probability >= 0.4 or a probability for hydrophobic or hydrophilic > 0.3) are:
+
+
+
+We will now visualize the epitope on Interleukin-1β.
+To do this, start PyMOL and open the provided PDB file of the antigen from the PyMOL File menu.
+
+
+File menu -> Open -> select 4I1B_clean.pdb
+
+
+
+color white, all
+
+
+show surface
+
+
+select epitope, (resi 72+73+74+75+81+83+84+89+90+92+94+96+97+98+115+116+117)
+
+
+color red, epitope
+
+
+Inspect the surface.
+
+
+Do the identified residues form a well-defined patch on the surface?
+
+
+The answer to that question should be yes, but we can see some residues not colored that might also be involved in the binding - there are some white spots around/in the red surface.
+
+
+
+ See surface view of the epitope identified by NMRexpand_more
+
+
+
+
+
+
+
+
+
+In HADDOCK, we are dealing with potentially incomplete binding sites by defining surface neighbors as `passive` residues.
+These passive residues are added in the definition of the interface but do not incur any energetic penalty if they are not part of the binding site in the final models. In contrast, residues defined as active (typically the identified or predicted binding site residues) will incur an energetic penalty.
+When using the HADDOCK2.x webserver, `passive` residues will be automatically defined.
+Here, since we are using a local version, we need to define those manually.
+
+This can easily be done using a haddock3 command line tool in the following way:
+
+
+haddock3-restraints passive_from_active 4I1B_clean.pdb 72,73,74,75,81,83,84,89,90,92,94,96,97,98,115,116,117 --cutoff 0.15
+
+
+The command prints a list of passive residues, which you should save to a file for further use.
+
+We can visualize the epitope and its surface neighbors using PyMOL:
+
+
+File menu -> Open -> select 4I1B_clean.pdb
+
+
+
+color white, all
+
+
+show surface
+
+
+select epitope, (resi 72+73+74+75+81+83+84+89+90+92+94+96+97+98+115+116+117)
+
+
+color red, epitope
+
+
+select passive, (resi 3+24+46+47+48+50+66+76+77+79+80+82+86+87+88+91+93+95+118+119+120)
+
+
+color green, passive
+
+
+
+
+
+ See the epitope and passive residuesexpand_more
+
+
+
+
+
+
+
+
+The NMR-identified residues and their surface neighbors generated with the above command can be used to define ambiguous interactions restraints, either using the NMR identified residues as active in HADDOCK, or combining those with the surface neighbors.
+
+The difference between `active` and `passive` residues in HADDOCK is as follows:
+
+*__Active residues__*: These residues are "forced" to be at the interface. If they are not part of the interface in the final models, an energetic penalty will be applied. The interface in this context is defined by the union of active and passive residues on the partner molecules.
+
+*__Passive residues__*: These residues are expected to be at the interface. However, if they are not, no energetic penalty is applied.
+
+
+In general, it is better to be too generous rather than too strict in the definition of passive residues.
+An important aspect is to filter both the active (the residues identified from your mapping experiment) and passive residues by their solvent accessibility.
+This is done automatically when using the `haddock3-restraints passive_from_active` command: residues with less that 15% relative solvent accessibility (same cutoff as the default in the HADDOCK server) are discared.
+This is, however, not a hard limit, and you might consider including even more buried residues if some important chemical group seems solvent accessible from a visual inspection.
+
+
+
+
+### Defining ambiguous restraints
+
+Once you have identified your active and passive residues for both molecules, you can proceed with the generation of the ambiguous interaction restraints (AIR) file for HADDOCK.
+For this you can either make use of our online [GenTBL][gentbl] web service, entering the list of active and passive residues for each molecule, the chainIDs of each molecule and saving the resulting restraint list to a text file, or use another `haddock3-restraints` sub-command.
+
+To use our `haddock3-restraints active_passive_to_ambig` script, you need to
+create for each molecule a file containing two lines:
+
+* The first line corresponds to the list of active residues (numbers separated by spaces)
+* The second line corresponds to the list of passive residues (numbers separated by spaces).
+
+*__Important__*: The file must consist of two lines, but a line can be empty (e.g., if you do not want to define active residues for one molecule). However, there must be at least one set of active residue defined for one of the molecules.
+
+
+* For the antibody we will use the predicted paratope as active and no passive residues defined. The corresponding file can be found in the `restraints` directory as `antibody-paratope.act-pass`:
+
+
+
+* For the antigen we will use the NMR-identified epitope as active and the surface neighbors as passive. The corresponding file can be found in the `restraints` directory as `antigen-NMR-epitope.act-pass`:
+
+
+assign (resi 31 and segid A)
+(
+ (resi 72 and segid B)
+ or
+ (resi 73 and segid B)
+ or
+ (resi 74 and segid B)
+ or
+ (resi 75 and segid B)
+ or
+ (resi 81 and segid B)
+ or
+ (resi 83 and segid B)
+ or
+ (resi 84 and segid B)
+ or
+ (resi 89 and segid B)
+ or
+ (resi 90 and segid B)
+ or
+ (resi 92 and segid B)
+ or
+ (resi 94 and segid B)
+ or
+ (resi 96 and segid B)
+ or
+ (resi 97 and segid B)
+ or
+ (resi 98 and segid B)
+ or
+ (resi 115 and segid B)
+ or
+ (resi 116 and segid B)
+ or
+ (resi 117 and segid B)
+ or
+ (resi 3 and segid B)
+ or
+ (resi 24 and segid B)
+ or
+ (resi 46 and segid B)
+ or
+ (resi 47 and segid B)
+ or
+ (resi 48 and segid B)
+ or
+ (resi 50 and segid B)
+ or
+ (resi 66 and segid B)
+ or
+ (resi 76 and segid B)
+ or
+ (resi 77 and segid B)
+ or
+ (resi 79 and segid B)
+ or
+ (resi 80 and segid B)
+ or
+ (resi 82 and segid B)
+ or
+ (resi 86 and segid B)
+ or
+ (resi 87 and segid B)
+ or
+ (resi 88 and segid B)
+ or
+ (resi 91 and segid B)
+ or
+ (resi 93 and segid B)
+ or
+ (resi 95 and segid B)
+ or
+ (resi 118 and segid B)
+ or
+ (resi 119 and segid B)
+ or
+ (resi 120 and segid B)
+) 2.0 2.0 0.0
+...
+
+
+
+
+
+
+Refering to the way the distance restraints are defined (see above), what is the distance range for the ambiguous distance restraints?
+
+
+
+
+ See answerexpand_more
+
+The default distance range for those is between 0 and 2Å, which
+might seem short but makes senses because of the 1/r^6 summation in the AIR
+energy function that makes the effective distance to be significantly shorter than
+the shortest distance entering the sum.
+
+
+The effective distance is calculated as the SUM over all pairwise atom-atom
+distance combinations between an active residue and all the active+passive on
+the other molecule: SUM[1/r^6]^(-1/6).
+
+
+
+
+
+
+### Restraints validation
+
+If you modify manually this generated restraint files or create your own, it is possible to quickly check if the format is valid using the following `haddock3-restraints` sub-command:
+
+
+haddock3-restraints validate_tbl ambig-paratope-NMR-epitope.tbl \-\-silent
+
+
+No output means that your TBL file is valid.
+
+*__Note__* that this only validates the syntax of the restraint file, but does not check if the selections defined in the restraints are actually existing in your input PDB files.
+
+
+
+
+### Additional restraints for multi-chain proteins
+
+As an antibody consists of two separate chains, it is important to define a few distance restraints
+to keep them together during the high temperature flexible refinement stage of HADDOCK otherwise they might slightly drift appart. This can easily be done using the `haddock3-restraints restrain_bodies` sub-command.
+
+
+haddock3-restraints restrain_bodies 4G6K_clean.pdb > antibody-unambig.tbl
+
+
+The result file contains two CA-CA distance restraints with the exact distance measured between two randomly picked CA atoms pairs:
+
+
+ assign (segid A and resi 110 and name CA) (segid A and resi 132 and name CA) 26.326 0.0 0.0
+ assign (segid A and resi 97 and name CA) (segid A and resi 204 and name CA) 19.352 0.0 0.0
+
+
+This file is also provided in the `restraints` directory.
+
+
+
+
+
+## Setting up and running the docking with HADDOCK3
+
+Now that we have all required files at hand (PDB and restraints files), it is time to setup our docking protocol. In this tutorial, considering we have rather good information about the paratope and epitope, we will execute a fast HADDOCK3 docking workflow, reducing the non-negligible computational cost of HADDOCK by decreasing the sampling, without impacting too much the accuracy of the resulting models.
+
+
+
+
+
+### HADDOCK3 workflow definition
+
+The first step is to create a HADDOCK3 configuration file that will define the docking workflow.
+We will follow a classic HADDOCK workflow consisting of rigid body docking, semi-flexible refinement and final energy minimisation followed by clustering.
+
+We will also integrate two analysis modules in our workflow:
+
+- `caprieval` will be used at various stages to compare models to either the best scoring model (if no reference is given) or a reference structure, which in our case we have at hand (`pdbs/4G6M_matched.pdb`). This will directly allow us to assess the performance of the protocol. In the absence of a reference, `caprieval` is still usefull to assess the convergence of a run and analyse the results.
+- `contactmap` added as last module will generate contact matrices of both intra- and intermolecular contacts and a chordchart of intermolecular contacts for each cluster.
+
+
+Our workflow consists of the following modules:
+
+1. **`topoaa`**: *Generates the topologies for the CNS engine and builds missing atoms*
+2. **`rigidbody`**: *Preforms rigid body energy minimisation (`it0` in haddock2.x)*
+3. **`caprieval`**: *Calculates CAPRI metrics (i-RMSD, l-RMSD, Fnat, DockQ) with respect to the top scoring model or reference structure if provided*
+4. **`seletop`** : *Selects the top X models from the previous module*
+5. **`flexref`**: *Preforms semi-flexible refinement of the interface (`it1` in haddock2.4)*
+6. **`caprieval`**
+7. **`emref`**: *Final refinement by energy minimisation (`itw` EM only in haddock2.4)*
+8. **`caprieval`**
+9. **`clustfcc`**: *Clustering of models based on the fraction of common contacts (FCC)*
+10. **`seletopclusts`**: *Selects the top models of all clusters*
+11. **`caprieval`**
+12. **`contactmap`**: *Contacts matrix and a chordchart of intermolecular contacts*
+
+
+The corresponding toml configuration file (provided in `workflows/docking-antibody-antigen-CDR-NMR-CSP.cfg`) looks like:
+
+{% highlight toml %}
+# ====================================================================
+# Antibody-antigen docking example with restraints from the antibody
+# paratope to the NMR-identified epitope on the antigen
+# ====================================================================
+
+# Directory in which the scoring will be done
+run_dir = "run1-CDR-NMR-CSP"
+
+# Compute mode
+mode = "local"
+ncores = 50
+
+# Self contained rundir (to avoid problems with long filename paths)
+self_contained = true
+
+# Post-processing to generate statistics and plots
+postprocess = true
+clean = true
+
+molecules = [
+ "pdbs/4G6K_clean.pdb",
+ "pdbs/4I1B_clean.pdb"
+ ]
+
+# ====================================================================
+# Parameters for each stage are defined below, prefer full paths
+# ====================================================================
+[topoaa]
+
+[rigidbody]
+# CDR to NMR epitope ambig restraints
+ambig_fname = "restraints/ambig-paratope-NMR-epitope.tbl"
+# Restraints to keep the antibody chains together
+unambig_fname = "restraints/antibody-unambig.tbl"
+sampling = 50
+
+[caprieval]
+reference_fname = "pdbs/4G6M_matched.pdb"
+
+[seletop]
+# Selection of the top 40 best scoring complexes
+select = 40
+
+[flexref]
+tolerance = 5
+# CDR to NMR epitope ambig restraints
+ambig_fname = "restraints/ambig-paratope-NMR-epitope.tbl"
+# Restraints to keep the antibody chains together
+unambig_fname = "restraints/antibody-unambig.tbl"
+
+[caprieval]
+reference_fname = "pdbs/4G6M_matched.pdb"
+
+[emref]
+tolerance = 5
+# CDR to NMR epitope ambig restraints
+ambig_fname = "restraints/ambig-paratope-NMR-epitope.tbl"
+# Restraints to keep the antibody chains together
+unambig_fname = "restraints/antibody-unambig.tbl"
+
+[caprieval]
+reference_fname = "pdbs/4G6M_matched.pdb"
+
+[clustfcc]
+plot_matrix = true
+
+[seletopclusts]
+# Selection of the top 4 best scoring complexes from each cluster
+top_models = 4
+
+[caprieval]
+reference_fname = "pdbs/4G6M_matched.pdb"
+
+[contactmap]
+
+# ====================================================================
+
+{% endhighlight %}
+
+
+In this case, since we have information for both interfaces we use a low-sampling configuration file, which takes only a small amount of computational resources to run. From the sampling parameters in the above config file, you can see we are sampling only 50 models at each stage of the docking:
+
+The initial `sampling` parameter at the rigid-body energy minimization (*rigidbody*) module is set to 50 models, of which only best the 40 are passed to the flexible refinement (*flexref*) module with the *seletop* module.
+The subsequence flexible refinement (*flexref* module) and energy minimisation (*emref*) modules will use all models passed by the *seletop* module.
+FCC clustering (*clustfcc*) is then applied to group together models sharing a consistent fraction of the interface contacts.
+The top 4 models of each cluster are saved to disk (*seletopclusts*).
+Multiple *caprieval* modules are executed at different stages of the workflow to check how the quality (and rankings) of the models change throughout the protocol.
+
+To get a list of all possible parameters that can be defined in a specific module (and their default values) you can use the following command:
+
+
+haddock3-cfg -m \
+
+
+Add the `-d` option to get a more detailed description of parameters and use the `-h` option to see a list of arguments and options.
+
+
+In the above workflow we see in three modules a *tolerance* parameter defined. Using the *haddock3-cfg* command try to figure out what this parameter does.
+
+
+
+*__Note__* that, in contrast to HADDOCK2.X, we have much more flexibility in defining our workflow.
+As an example, we could use this flexibility by introducing a clustering step after the initial rigid-body docking stage, selecting a given number of models per cluster and refining all of those.
+For an example of this strategy see the BONUS 3 section about ensemble docking.
+
+
+
+
+### Running HADDOCK3
+
+In in the first section of the workflow above we have a parameter `mode` defining the execution mode. HADDOCK3 currently supports three difference execution modes:
+
+- **local** : In this mode, HADDOCK3 will run on the current system, using the defined number of cores (`ncores`) in the config file to a maximum of the total number of available cores on the system.
+- **batch**: In this mode, HADDOCK3 will typically be started on your local server (e.g. the login node) and will dispatch jobs to the batch system of your cluster (slurm and torque are currently supported).
+- **mpi**: HADDOCK3 also supports a pseudo parallel MPI implementation, which allows to harvest the power of multiple nodes to distribute the computations (functional but still very experimental at this stage).
+
+
+
+
+#### Learn about the various execution modes of haddock3
+
+
+
+ Execution of Fugaku using a full node (EU-ASEAN HPC School)expand_more
+
+To execute the workflow on Fugaku, we will create a job file that will execute HADDOCK3 on a node, with HADDOCK3 running in local mode (the setup in the above configuration file with `mode="local"`) and harvesting all core of that node (`ncores=50`).
+
+Here is an example of such an execution script (also provided in the `workflows` directory as `run-haddock3-fugaku.sh`):
+
+{% highlight shell %}
+#!/bin/sh
+#PJM -g ra022304
+#PJM -L "rscgrp=small"
+#PJM -L "node=1"
+#PJM -L "elapse=01:00:00"
+#PJM -x PJM_LLIO_GFSCACHE=/vol0004:/vol0003
+#PJM -s # Statistical information output
+
+source /vol0300/share/ra022304/LifeScience/20231213_Bonvin/miniconda3/etc/profile.d/conda.sh
+conda activate haddock3
+
+haddock3 docking-antibody-antigen-CDR-NMR-CSP.cfg
+
+{% endhighlight %}
+
+This file should be submitted to the batch system using the `pjsub` command:
+
+
+pjsub workflows/run-haddock3-fugaku.sh
+
+
+
+This run should take about 20 minutes to complete on a single node using 50 arm cores.
+
+
+
+
+
+
+
+
+ Local executionexpand_more
+
+
+In this mode HADDOCK3 will run on the current system, using the defined number of cores (`ncores`) in the config file to a maximum of the total number of available cores on the system minus one. An example of the relevant parameters to be defined in the first section of the config file is:
+
+{% highlight toml %}
+# compute mode
+mode = "local"
+# 1 nodes x 50 ncores
+ncores = 50
+{% endhighlight %}
+
+In this mode HADDOCK3 can be started from the command line with as argument the configuration file of the defined workflow.
+
+
+haddock3 \
+
+
+Alternatively redirect the output to a log file and send haddock3 to the background.
+
+
+As an indication, running locally on an Apple M2 laptop using 10 cores, this workflow completed in 7 minutes.
+
+
+
+haddock3 \ \> haddock3.log &
+
+
+_**Note**_: This is also the execution mode that should be used for example when submitting the HADDOCK3 job to a node of a cluster, requesting X number of cores.
+
+
+
+
+
+
+
+ Exection in batch mode using slurmexpand_more
+
+
+ Here is an example script for submitting via the slurm batch system:
+
+ {% highlight shell %}
+ #!/bin/bash
+ #SBATCH --nodes=1
+ #SBATCH --tasks-per-node=50
+ #SBATCH -J haddock3
+ #SBATCH --partition=medium
+
+ # activate the haddock3 conda environment
+ source $HOME/miniconda3/etc/profile.d/conda.sh
+ conda activate haddock3
+
+ # go to the run directory
+ cd $HOME/HADDOCK3-antibody-antigen
+
+ # execute
+ haddock3 \
+ {% endhighlight %}
+
+
+
+ In this mode HADDOCK3 will typically be started on your local server (e.g. the login node) and will dispatch jobs to the batch system of your cluster. Two batch systems are currently supported: `slurm` and `torque` (defined by the `batch_type` parameter). In the configuration file you will
+have to define the `queue` name and the maximum number of concurrent jobs sent to the queue (`queue_limit`).
+
+ Since HADDOCK3 single model calculations are quite fast, it is recommended to calculate multiple models within one job submitted to the batch system. The number of model per job is defined by the `concat` parameter in the configuration file. You want to avoid sending thousands of very short jobs to the batch system if you want to remain friend with your system administrators...
+
+ An example of the relevant parameters to be defined in the first section of the config file is:
+
+ {% highlight toml %}
+ # compute mode
+ mode = "batch"
+ # batch system
+ batch_type = "slurm"
+ # queue name
+ queue = "short"
+ # number of concurrent jobs to submit to the batch system
+ queue_limit = 100
+ # number of models to produce per submitted job
+ concat = 10
+ {% endhighlight %}
+
+ In this mode HADDOCK3 can be started from the command line as for the local mode.
+
+
+
+
+
+
+ Exection in MPI modeexpand_more
+
+
+
+HADDOCK3 supports a parallel pseudo-MPI implementation (functional but still very experimental at this stage). For this to work, the `mpi4py` library must have been installed at installation time. Refer to the [MPI-related instructions](https://www.bonvinlab.org/haddock3/tutorials/mpi.html){:target="_blank"}.
+
+The execution mode should be set to `mpi` and the total number of cores should match the requested resources when submitting to the batch system.
+
+An example of the relevant parameters to be defined in the first section of the config file is:
+
+{% highlight toml %}
+# compute mode
+mode = "mpi"
+# 5 nodes x 50 tasks = ncores = 250
+ncores = 250
+{% endhighlight %}
+
+In this execution mode the HADDOCK3 job should be submitted to the batch system requesting the corresponding number of nodes and cores per node.
+
+
+ {% highlight shell %}
+ #!/bin/bash
+ #SBATCH --nodes=5
+ #SBATCH --tasks-per-node=50
+ #SBATCH -J haddock3mpi
+
+ # Make sure haddock3 is activated
+ source $HOME/miniconda3/etc/profile.d/conda.sh
+ conda activate haddock3
+
+ # go to the run directory
+ # edit if needed to specify the correct location
+ cd $HOME/HADDOCK3-antibody-antigen
+
+ # execute
+ haddock3 \
+ {% endhighlight %}
+
+
+
+
+
+
+
+
+
+## Analysis of docking results
+
+In case something went wrong with the docking (or simply if you do not want to wait for the results) you can find the following precalculated runs in the `runs` directory:
+- `run1`: run created using the unbound antibody.
+- `run1-af2`: run created using the Alphafold-multimer antibody (see BONUS 2).
+- `run1-abb`: run created using the Immunebuilder antibody (see BONUS 2).
+- `run1-ens`: run created using an ensemble of antibody models (see BONUS 3).
+
+
+Once your run has completed - inspect the content of the resulting directory.
+You will find the various steps (modules) of the defined workflow numbered sequentially starting at 0, e.g.:
+
+{% highlight shell %}
+> ls run1/
+ 00_topoaa/
+ 01_rigidbody/
+ 02_caprieval/
+ 03_seletop/
+ 04_flexref/
+ 05_caprieval/
+ 06_emref/
+ 07_caprieval/
+ 08_clustfcc/
+ 09_seletopclusts/
+ 10_caprieval/
+ 11_contactmap/
+ analysis/
+ data/
+ log
+ toppar/
+ traceback/
+{% endhighlight %}
+
+In addition, there is a log file (text file) and four additional directories:
+
+- the `analysis` directory contains various plots to visualize the results for each caprieval step and a general report (`report.html`) that provides all statistics with various plots. You can open this file in your preferred web browser
+- the `data` directory contains the input data (PDB and restraint files) for the various modules
+- the `toppar` directory contains the force field topology and parameter files (only present when running in self-contained mode)
+- the `traceback` directory contains `traceback.tsv`, which links all models to see which model originates from which throughout all steps of the workflow.
+
+You can find information about the duration of the run at the bottom of the log file. Each sampling/refinement/selection module will contain PDB files.
+
+For example, the `09_seletopclusts` directory contains the selected models from each cluster. The clusters in that directory are numbered based
+on their rank, i.e. `cluster_1` refers to the top-ranked cluster. Information about the origin of these files can be found in that directory in the `seletopclusts.txt` file.
+
+The simplest way to extract ranking information and the corresponding HADDOCK scores is to look at the `10_caprieval` directories (which is why it is a good idea to have it as the final module, and possibly as intermediate steps). This directory will always contain a `capri_ss.tsv` single model statistics file, which contains the model names, rankings and statistics (score, iRMSD, Fnat, lRMSD, ilRMSD and dockq score). E.g.:
+
+
+
+If clustering was performed prior to calling the `caprieval` module, the `capri_ss.tsv` file will also contain information about to which cluster the model belongs to and its ranking within the cluster.
+
+The relevant statistics are:
+
+* **score**: *the HADDOCK score (arbitrary units)*
+* **irmsd**: *the interface RMSD, calculated over the interfaces the molecules*
+* **fnat**: *the fraction of native contacts*
+* **lrmsd**: *the ligand RMSD, calculated on the ligand after fitting on the receptor (1st component)*
+* **ilrmsd**: *the interface-ligand RMSD, calculated over the interface of the ligand after fitting on the interface of the receptor (more relevant for small ligands for example)*
+* **dockq**: *the DockQ score, which is a combination of irmsd, lrmsd and fnat and provides a continuous scale between 1 (exactly equal to reference) and 0*
+
+Various other terms are also reported including:
+
+* **bsa**: *the buried surface area (in squared angstroms)*
+* **elec**: *the intermolecular electrostatic energy*
+* **vdw**: *the intermolecular van der Waals energy*
+* **desolv**: *the desolvation energy*
+
+
+The iRMSD, lRMSD and Fnat metrics are the ones used in the blind protein-protein prediction experiment [CAPRI](https://capri.ebi.ac.uk/){:target="_blank"} (Critical PRediction of Interactions).
+
+In CAPRI the quality of a model is defined as (for protein-protein complexes):
+
+* **acceptable model**: i-RMSD < 4Å or l-RMSD < 10Å and Fnat > 0.1 (0.23 < DOCKQ < 0.49)
+* **medium quality model**: i-RMSD < 2Å or l-RMSD < 5Å and Fnat > 0.3 (0.49 < DOCKQ < 0.8)
+* **high quality model**: i-RMSD < 1Å or l-RMSD < 1Å and Fnat > 0.5 (DOCKQ > 0.8)
+
+
+Based on this CAPRI criterion, what is the quality of the best model listed above (emref_2.pdb)?
+
+
+In case where the `caprieval` module is called after a clustering step, an additional `capri_clt.tsv` file will be present in the directory.
+This file contains the cluster ranking and score statistics, averaged over the minimum number of models defined for clustering
+(4 by default), with their corresponding standard deviations. E.g.:
+
+
+
+
+In this file you find the cluster rank, the cluster ID (which is related to the size of the cluster, 1 being always the largest cluster), the number of models (n) in the cluster and the corresponding statistics (averages + standard deviations). The corresponding cluster PDB files will be found in the preceeding `09_seletopclusts` directory.
+
+While these simple text files can be easily checked from the command line already, they might be cumbersome to read.
+For that reason, we have developed a post-processing analysis that automatically generates html reports for all `caprieval` steps in the workflow.
+These are located in the respective `analysis/XX_caprieval` directories and can be viewed using your favorite web browser.
+
+
+
+
+### Cluster statistics
+
+Let us now analyse the docking results. Use for that either your own run or a pre-calculated run provided in the `runs` directory.
+Go into the `analysis/10_caprieval_analysis` directory of the respective run directory (if needed copy the run or that directory to your local computer) and open in a web browser the `report.html` file. Be patient as this page contains interactive plots that may take some time to generate.
+
+On the top of the page, you will see a table that summarises the cluster statistics (taken from the `capri_clt.tsv` file).
+The columns (corresponding to the various clusters) are sorted by default on the cluster rank, which is based on the HADDOCK score (found on the 4th row of the table).
+As this is an interactive table, you can sort it as you wish by using the arrows present in the first column.
+Simply click on the arrows of the term you want to use to sort the table (and you can sort it in ascending or descending order).
+A snapshot of this table is shown below:
+
+
+
+
+
+You can also view this report online [here](plots/report.html){:target="_blank"}
+
+*__Note__* that in case the PDB files are still compressed (gzipped) the download links will not work. Also online visualisation is not enabled. To overcome this disk space storge solution, consider adding the global parameter `clean = true` at the begining of your configuration file.
+
+
+Inspect the final cluster statistics
+
+How many clusters have been generated?
+
+Look at the score of the first few clusters: Are they significantly different if you consider their average scores and standard deviations?
+
+Since for this tutorial we have at hand the crystal structure of the complex, we provided it as reference to the `caprieval` modules.
+This means that the iRMSD, lRMSD, Fnat and DockQ statistics report on the quality of the docked model compared to the reference crystal structure.
+
+How many clusters of acceptable or better quality have been generate according to CAPRI criteria?
+
+What is the rank of the best cluster generated?
+
+What is the rank of the first acceptable of better cluster generated?
+
+
+
+
+### Visualizing the scores and their components
+
+
+Next to the cluster statistic table shown above, the `report.html` file also contains a variety of plots displaying the HADDOCK score
+and its components against various CAPRI metrics (i-RMSD, l-RMSD, Fnat, Dock-Q) with a color-coded representation of the clusters.
+These are interactive plots. A menu on the top right of the first row (you might have to scroll to the rigth to see it)
+allows you to zoom in and out in the plots and turn on and off clusters.
+
+
+
+
+
+As a reminder, you can also view this report online [here](plots/report.html){:target="_blank"}
+
+
+Examine the plots (remember here that higher DockQ values and lower i-RMSD values correspond to better models)
+
+
+
+Finally, the report also shows plots of the cluster statistics (distributions of values per cluster ordered according to their HADDOCK rank):
+
+
+
+
+
+For this antibody-antigen case, which of the score components correlates best with the quality of the models?
+
+
+
+
+### Some single structure analysis
+
+
+Going back to command line analysis, we are providing in the `scripts` directory a simple script that extracts some model statistics for acceptable or better models from the `caprieval` steps.
+To use it, simply call the script with as argument the run directory you want to analyze, e.g.:
+
+
+./scripts/extract-capri-stats.sh ./runs/run1-CDR-NMR-CSP
+
+
+
+
+View the output of the scriptexpand_more
+
+
+==============================================
+== runs/run1-CDR-NMR-CSP/02_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models: 10 out of 50
+Total number of medium or better models: 7 out of 50
+Total number of high quality models: 0 out of 50
+
+First acceptable model - rank: 1 i-RMSD: 1.181 Fnat: 0.690 DockQ: 0.749
+First medium model - rank: 1 i-RMSD: 1.181 Fnat: 0.690 DockQ: 0.749
+Best model - rank: 2 i-RMSD: 1.074 Fnat: 0.707 DockQ: 0.731
+==============================================
+== runs/run1-CDR-NMR-CSP/05_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models: 8 out of 40
+Total number of medium or better models: 7 out of 40
+Total number of high quality models: 1 out of 40
+
+First acceptable model - rank: 1 i-RMSD: 1.145 Fnat: 0.828 DockQ: 0.798
+First medium model - rank: 1 i-RMSD: 1.145 Fnat: 0.828 DockQ: 0.798
+Best model - rank: 2 i-RMSD: 0.936 Fnat: 0.948 DockQ: 0.877
+==============================================
+== runs/run1-CDR-NMR-CSP/07_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models: 8 out of 40
+Total number of medium or better models: 7 out of 40
+Total number of high quality models: 1 out of 40
+
+First acceptable model - rank: 1 i-RMSD: 1.193 Fnat: 0.862 DockQ: 0.803
+First medium model - rank: 1 i-RMSD: 1.193 Fnat: 0.862 DockQ: 0.803
+Best model - rank: 2 i-RMSD: 0.957 Fnat: 0.948 DockQ: 0.874
+==============================================
+== runs/run1-CDR-NMR-CSP/10_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models: 4 out of 16
+Total number of medium or better models: 4 out of 16
+Total number of high quality models: 1 out of 16
+
+First acceptable model - rank: 1 i-RMSD: 1.193 Fnat: 0.862 DockQ: 0.803
+First medium model - rank: 1 i-RMSD: 1.193 Fnat: 0.862 DockQ: 0.803
+Best model - rank: 2 i-RMSD: 0.957 Fnat: 0.948 DockQ: 0.874
+
+ In terms of iRMSD values, we only observe very small differences in the best model.
+ The fraction of native contacts and the DockQ scores are however improving much more after flexible refinement but increases again slightly after final minimisation.
+ All this will of course depend on how different are the bound and unbound conformations and the amount of data used to drive the docking process.
+ In general, from our experience, the more and better data at hand, the larger the conformational changes that can be induced.
+
+ This is not the case. The scoring function is not perfect, but does a reasonable job at ranking models of acceptable or better quality on top in this case.
+
Top-ranked model of the top cluster superimposed onto the reference crystal structure (in yellow)
+
+
+
+
+
+
+
+
+
+
+
+## Conclusions
+
+We have demonstrated the usage of HADDOCK3 in an antibody-antigen docking scenario making use of the paratope information on the antibody side (i.e. no prior experimental information, but computational predictions) and an NMR-mapped epitope for the antigen.
+Compared to the static HADDOCK2.X workflow, the modularity and flexibility of HADDOCK3 allow to customise the docking protocols and to run a deeper analysis of the results.
+While HADDOCK3 is still very much a work in progress, its intrinsic flexibility can be used to improve the performance of antibody-antigen modelling compared to the results we presented in our
+[Structure 2020](https://doi.org/10.1016/j.str.2019.10.011){:target="_blank"} article and in the [related HADDOCK2.4 tutorial](/education/HADDOCK24/HADDOCK24-antibody-antigen){:target="_blank"}.
+
+
+
+
+
+## BONUS 1: Does the antibody bind to a known interface of interleukin? ARCTIC-3D analysis
+
+Gevokizumab is a highly specific antibody that targets an allosteric site of Interleukin-1β (IL-1β) in PDB file *4G6M*, thus reducing its binding affinity for its substrate, interleukin-1 receptor type I (IL-1RI). Canakinumab, another antibody binding to IL-1β, has a different mode of action, as it competes directly with the binding site of IL-1RI (in PDB file *4G6J*). For more details please check [this article](https://www.sciencedirect.com/science/article/abs/pii/S0022283612007863?via%3Dihub){:target="_blank"}.
+
+We will now use our new software, [ARCTIC-3D](https://www.nature.com/articles/s42003-023-05718-w){:target="_blank"}, to visualize the binding interfaces formed by IL-1β. First, the program retrieves all the existing binding surfaces formed by IL-1β from the [PDBe website](https://www.ebi.ac.uk/pdbe/){:target="_blank"}. Then, these binding surfaces are compared and clustered together if they span a similar region of the selected protein (IL-1β in our case).
+
+We will run an ARCTIC-3D job targeting the uniprot ID of human Interleukin-1 beta, namely [P01584](https://www.uniprot.org/uniprotkb/P01584/entry){:target="_blank"}.
+
+For this first open the ARCTIC-3D web-server page [here](https://wenmr.science.uu.nl/arctic3d/){:target="_blank"}.
+
+
+Insert the selected UniProt ID in the **UniprotID** field.
+
+
+
+Leave the other parameters as they are and click on **Submit**.
+
+
+Wait a few seconds for the job to complete or access a precalculated run [here](https://wenmr.science.uu.nl/arctic3d/example-P01584){:target="_blank"}.
+
+
+How many interface clusters were found for this protein?
+
+
+Once you download the output archive, you can find the clustering information presented in the dendrogram:
+
+
+
+
+
+We can see how the two *4G6M* antibody chains are recognized as a unique cluster, clearly separated from the other binding surfaces and, in particular, from those proper to IL-1RI (uniprot ID P14778).
+
+
+Re-run ARCTIC-3D with a clustering threshold equal to 0.95
+
+
+This means that the clustering will be looser, therefore lumping more dissimilar surfaces into the same object.
+
+You can inspect a precalculated run [here](https://wenmr.science.uu.nl/arctic3d/example-P01584-095){:target="_blank"}.
+
+
+How do the results change? Are gevokizumab or canakinumab PDB files being clustered with any IL-1RI-related interface?
+
+
+
+
+
+
+
+## BONUS 2: How good are AI-based models of antibody for docking?
+
+The release of [AlphaFold2 in late 2020](https://www.nature.com/articles/s41586-021-03819-2) has brought structure prediction methods to a new frontier, providing accurate models for the majority of known proteins. This revolution did not spare antibodies, with [Alphafold2-multimer](https://github.com/sokrypton/ColabFold){:target="_blank"} and other prediction methods (most notably [ABodyBuilder2](https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabpred/abodybuilder2/){:target="_blank"}, from the ImmuneBuilder suite) performing nicely on the variable regions.
+
+For a short introduction to AI and AlphaFold2 refer to this other tutorial [introduction](/education/molmod_online/alphafold/#introduction){:target="_blank"}.
+
+For antibody modelings, CDR loops are clearly the most challenging region to be predicted given their high sequence variability and flexibility.
+Multiple Sequence Alignment (MSA)-derived information is also less useful in this context.
+
+Here we will see whether the antibody models given by Alphafold2-multimer and ABodyBuilder2 can be used for generating reliable models of the antibody-antigen complex by docking, instead of the unbound form used in this tutorial, which, in many cases, will not be available.
+
+
+### Analysing the AI models
+
+We already ran the prediction with these two tools, and you can find the resulting models in the `pdbs` directory as:
+
+- `4g6k_Abodybuilder2.pdb`
+- `4g6k_AF2_multimer.pdb`
+
+
+As was demonstrated in the tutorial, those files must be preprocessed for their use in HADDOCK. Docking-ready files are also provided in the `pdbs` directory:
+
+
+- `4G6K_abb_clean.pdb`
+- `4G6K_af2_clean.pdb`
+
+
+Load the experimental unbound structure (`4G6K_clean.pdb`) and the two AI models in PyMOL to see whether they resemble the experimental unbound structure.
+
+
+File menu -> Open -> select 4G6K_clean.pdb
+
+
+File menu -> Open -> select 4G6K_abb_clean.pdb
+
+
+File menu -> Open -> select 4G6K_af2_clean.pdb
+
+
+Align the models to the experimental unbound structure
+
+
+alignto 4G6K_clean
+
+
+
+Which model is the closest to the unbound conformation?
+
+
+
+
+ See the RMSD valuesexpand_more
+
+
+ 4G6K_abb_clean RMSD = 0.428 Å
+ 4G6K_af2_clean RMSD = 0.765 Å
+
+ 4G6K_abb_clean RMSD = 0.330 Å
+ 4G6K_af2_clean RMSD = 0.675 Å
+ 4G6K_clean RMSD = 0.393 Å
+
+
+
+
+
+
+
+
+### Docking performance using AI-based antibody models
+
+We can repeat the docking as described above in our tutorial, but using this time either the ABodyBuilder2 or AlphaFold2 models as input.
+For this, modify your haddock3 configuration file, changing the input PDB file of the first molecule (the antibody) using the respective HADDOCK-ready models provided in the `pdbs` directory.
+You will also need to change the restraint filename used to keep the two parts of the antibody together (those files are present in the `restraints` directory.
+
+Further, run haddock3 as described above.
+
+Pre-calculated runs are provided in the `runs` directory. Analyse your run (or the pre-calculated ones) as described previously.
+
+
+Which starting structure of the antibody gives the best results in terms of cluster quality and ranking?
+
+
+
+
+ See the cluster statistics expand_more
+
+
+==============================================
+== runs/run1-CDR-NMR-CSP/10_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters: 1 out of 4
+Total number of medium or better clusters: 1 out of 4
+Total number of high quality clusters: 0 out of 4
+
+First acceptable cluster - rank: 1 i-RMSD: 1.063 Fnat: 0.918 DockQ: 0.844
+First medium cluster - rank: 1 i-RMSD: 1.063 Fnat: 0.918 DockQ: 0.844
+Best cluster - rank: 1 i-RMSD: 1.063 Fnat: 0.918 DockQ: 0.844
+
+==============================================
+== runs/run1-abb-CDR-NMR-CSP/10_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters: 1 out of 2
+Total number of medium or better clusters: 1 out of 2
+Total number of high quality clusters: 0 out of 2
+
+First acceptable cluster - rank: 1 i-RMSD: 1.197 Fnat: 0.845 DockQ: 0.796
+First medium cluster - rank: 1 i-RMSD: 1.197 Fnat: 0.845 DockQ: 0.796
+Best cluster - rank: 1 i-RMSD: 1.197 Fnat: 0.845 DockQ: 0.796
+
+==============================================
+== runs/run1-CDR-NMR-CSP-af2/10_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters: 3 out of 5
+Total number of medium or better clusters: 0 out of 5
+Total number of high quality clusters: 0 out of 5
+
+First acceptable cluster - rank: 1 i-RMSD: 2.458 Fnat: 0.474 DockQ: 0.486
+First medium cluster - rank: - i-RMSD: - Fnat: - DockQ: -
+Best cluster - rank: 1 i-RMSD: 2.458 Fnat: 0.474 DockQ: 0.486
+
+==============================================
+== runs/run1-CDR-NMR-CSP/07_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models: 8 out of 40
+Total number of medium or better models: 7 out of 40
+Total number of high quality models: 1 out of 40
+
+First acceptable model - rank: 1 i-RMSD: 1.193 Fnat: 0.862 DockQ: 0.803
+First medium model - rank: 1 i-RMSD: 1.193 Fnat: 0.862 DockQ: 0.803
+Best model - rank: 2 i-RMSD: 0.957 Fnat: 0.948 DockQ: 0.874
+
+==============================================
+== runs/run1-abb-CDR-NMR-CSP/07_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models: 13 out of 40
+Total number of medium or better models: 9 out of 40
+Total number of high quality models: 1 out of 40
+
+First acceptable model - rank: 1 i-RMSD: 1.406 Fnat: 0.862 DockQ: 0.775
+First medium model - rank: 1 i-RMSD: 1.406 Fnat: 0.862 DockQ: 0.775
+Best model - rank: 4 i-RMSD: 0.862 Fnat: 0.879 DockQ: 0.870
+
+==============================================
+== runs/run1-CDR-NMR-CSP-af2/07_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models: 15 out of 40
+Total number of medium or better models: 1 out of 40
+Total number of high quality models: 0 out of 40
+
+First acceptable model - rank: 1 i-RMSD: 2.780 Fnat: 0.362 DockQ: 0.421
+First medium model - rank: 10 i-RMSD: 1.654 Fnat: 0.707 DockQ: 0.645
+Best model - rank: 10 i-RMSD: 1.654 Fnat: 0.707 DockQ: 0.645
+
+
+
+
+
+
+
+
+### Conclusions - AI-based docking
+
+All three antibody structures used in input give good to reasonable results.
+The unbound and the ABodyBuilder2 antibodies provided better results, with the best cluster showing models within 1 angstrom of interface-RMSD with respect to the unbound structure.
+Using the Alphafold2 structure in this case is not the best option, as the input antibody is not perfectly modelled in its H3 loop.
+
+
+
+
+
+## BONUS 3: Ensemble docking using a combination of exprimental and AI-predicted antibody structures
+
+
+Instead of running haddock3 using a specific input structure of the antibody, we can also use an ensemble of all available models.
+Such an ensemble can be created from the individual models using `pdb_mkensemble` from PDB-tools:
+
+
+pdb_mkensemble 4G6K_clean.pdb 4G6K_abb_clean.pdb 4G6K_af2_clean.pdb > 4G6K-ensemble.pdb
+
+
+This ensemble file is provided in the `pdbs` directory.
+
+Now we can make use of the flexibility of haddock3 in defining workflows to add a clustering step after the rigid body docking step in order to make sure that models originating from all models will ideally be selected for the refinement steps (provided they do cluster). This modified workflow looks like:
+
+
+{% highlight toml %}
+# ====================================================================
+# Antibody-antigen docking example with restraints from the antibody
+# paratope to the NMR-identified epitope on the antigen
+# ====================================================================
+
+# directory in which the scoring will be done
+run_dir = "run-ens-CDR-NMR-CSP"
+
+# compute mode
+mode = "local"
+ncores = 50
+
+# Self contained rundir (to avoid problems with long filename paths)
+self_contained = true
+
+# Post-processing to generate statistics and plots
+postprocess = true
+clean = true
+
+molecules = [
+ "pdbs/4G6K-ensemble.pdb",
+ "pdbs/4I1B_clean.pdb"
+ ]
+
+# ====================================================================
+# Parameters for each stage are defined below, prefer full paths
+# ====================================================================
+[topoaa]
+
+[rigidbody]
+# CDR to NMR epitope ambig restraints
+ambig_fname = "restraints/ambig-paratope-NMR-epitope.tbl"
+# Restraints to keep the antibody chains together
+unambig_fname = "restraints/antibody-unambig.tbl"
+# Increased sampling so each conformation is sampled 50 times
+sampling = 150
+
+[clustfcc]
+plot_matrix = true
+
+[seletopclusts]
+top_models = 10
+
+[caprieval]
+reference_fname = "pdbs/4G6M_matched.pdb"
+
+[flexref]
+tolerance = 5
+# CDR to NMR epitope ambig restraints
+ambig_fname = "restraints/ambig-paratope-NMR-epitope.tbl"
+# Restraints to keep the antibody chains together
+unambig_fname = "restraints/antibody-unambig.tbl"
+
+[caprieval]
+reference_fname = "pdbs/4G6M_matched.pdb"
+
+[emref]
+tolerance = 5
+# CDR to NMR epitope ambig restraints
+ambig_fname = "restraints/ambig-paratope-NMR-epitope.tbl"
+# Restraints to keep the antibody chains together
+unambig_fname = "restraints/antibody-unambig.tbl"
+
+[caprieval]
+reference_fname = "pdbs/4G6M_matched.pdb"
+
+[clustfcc]
+plot_matrix = true
+
+[seletopclusts]
+top_models = 4
+
+[caprieval]
+reference_fname = "pdbs/4G6M_matched.pdb"
+
+[contactmap]
+
+# ====================================================================
+
+{% endhighlight %}
+
+
+Our workflow consists of the following 14 modules:
+
+0. **`topoaa`**: *Generates the topologies for the CNS engine and builds missing atoms*
+1. **`rigidbody`**: *Performs Rigid body energy minimisation* - with increased sampling (150 models - 50 per input model)
+2. **`caprieval`**: *Calculates CAPRI metrics*
+3. **`clustfcc`**: *Clustering of models based on the fraction of common contacts (FCC)*
+4. **`seletopclusts`**: *Selects the top models of all clusters* - In this case, we select max 10 models per cluster.
+5. **`caprieval`**: *Calculates CAPRI metrics* of the selected clusters
+6. **`flexref`**: *Performs Semi-flexible refinement of the interface (`it1` in haddock2.4)*
+7. **`caprieval`**
+8. **`emref`**: *Performs a final refinement by energy minimisation (`itw` EM only in haddock2.4)*
+9. **`caprieval`**
+10. **`clustfcc`**: *Clustering of models based on the fraction of common contacts (FCC)*
+11. **`seletopclusts`**: *Selects the top models of all clusters*
+12. **`caprieval`**
+13. **`contactmap`**: *Contacts matrix and a chordchart of intermolecular contacts*
+
+Compared to the original workflow described in this tutorial we have added clustering and cluster selections steps after the rigid body docking.
+
+Run haddock3 with this configuration file as described above.
+
+A pre-calculated run is provided in the `runs` directory as `run1-ens-clst`.
+Analyse your run (or the pre-calculated ones) as described previously.
+
+
+
+
+ See the cluster statistics expand_more
+
+
+==============================================
+== runs/run-ens-CDR-NMR-CSP/11_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters: 4 out of 11
+Total number of medium or better clusters: 1 out of 11
+Total number of high quality clusters: 0 out of 11
+
+First acceptable cluster - rank: 1 i-RMSD: 1.188 Fnat: 0.862 DockQ: 0.795
+First medium cluster - rank: 1 i-RMSD: 1.188 Fnat: 0.862 DockQ: 0.795
+Best cluster - rank: 1 i-RMSD: 1.188 Fnat: 0.862 DockQ: 0.795
+
+
+
+
+
+
+
+
+ See single structure statistics expand_more
+
+
+==============================================
+== runs/run-ens-CDR-NMR-CSP/04_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models: 25 out of 83
+Total number of medium or better models: 10 out of 83
+Total number of high quality models: 0 out of 83
+
+First acceptable model - rank: 3 i-RMSD: 1.238 Fnat: 0.672 DockQ: 0.725
+First medium model - rank: 3 i-RMSD: 1.238 Fnat: 0.672 DockQ: 0.725
+Best model - rank: 6 i-RMSD: 1.074 Fnat: 0.707 DockQ: 0.731
+==============================================
+== runs/run-ens-CDR-NMR-CSP/06_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models: 27 out of 83
+Total number of medium or better models: 10 out of 83
+Total number of high quality models: 5 out of 83
+
+First acceptable model - rank: 1 i-RMSD: 1.492 Fnat: 0.741 DockQ: 0.697
+First medium model - rank: 1 i-RMSD: 1.492 Fnat: 0.741 DockQ: 0.697
+Best model - rank: 4 i-RMSD: 0.857 Fnat: 0.897 DockQ: 0.872
+==============================================
+== runs/run-ens-CDR-NMR-CSP/08_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models: 26 out of 83
+Total number of medium or better models: 10 out of 83
+Total number of high quality models: 3 out of 83
+
+First acceptable model - rank: 1 i-RMSD: 1.504 Fnat: 0.776 DockQ: 0.708
+First medium model - rank: 1 i-RMSD: 1.504 Fnat: 0.776 DockQ: 0.708
+Best model - rank: 4 i-RMSD: 0.902 Fnat: 0.914 DockQ: 0.871
+
+
+
+
+
+
+We started from three different conformations of the antibody: 1) the unbound crystal structure, 2) the ABodyBuilder2 model and 3) the AlphaFold2 model.
+
+
+Using the information in the _traceback_ directory, try to figure out which of the three starting antibody models makes it into the best cluster at the end of the workflow.
+
+
+
+
+
+
+
+## BONUS 4: Antibody-antigen complex structure prediction from sequence using AlphaFold2
+
+
+With the advent of Artificial Intelligence (AI) and AlphaFold2, we can also try to predict directly the full antibody-antigen complex using AlphaFold2.
+For this we are going to use the _AlphaFold2_mmseq2_ Jupyter notebook which can be found with other interesting notebooks in Sergey Ovchinnikov
+[ColabFold GitHub repository](https://github.com/sokrypton/ColabFold){:target="_blank"}, making use of the Google Colab CLOUD resources.
+
+Start the AlphaFold2 notebook on Colab by clicking [here](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb){:target="_blank"}.
+
+**Note**: The bottom part of the notebook contains instructions on how to use it.
+
+
+
+### Setting up the antibody-antigen complex prediction with AlphaFold2
+
+To setup the prediction we need to provide the sequence of the heavy and light chains of the antibody and the sequence of the antigen.
+These are respectively
+
+* Antibody heavy chain:
+
+
+
+
+Define the _jobname_, e.g. Ab-Ag
+
+
+
+In the _Advanced settings_ block you can check the option to save the results to your Google Drive (if you have an account)
+
+
+
+In the top section of the Colab, click: _Runtime > Run All_
+
+
+(It may give a warning that this is not authored by Google because it is pulling code from GitHub - you can ignore it).
+
+This will automatically install, configure and run AlphaFold2 for you - leave this window open.
+After the prediction completed, you will be asked to download a zip archive with the results (if you configured it to use Google Drive, a result archive will be automatically saved to your Google Drive).
+
+
+_Time to grab a cup of tea or a coffee!
+And while waiting try to answer the following questions:_
+
+
+ How do you interpret AlphaFold2 predictions? What are the predicted LDDT (pLDDT), PAE, iptm?
+
+
+_Tip_: Try to find information about the prediction confidence at [https://alphafold.ebi.ac.uk/faq](https://alphafold.ebi.ac.uk/faq){:target="\_blank"}. A nice summary can also be found [here](https://www.rbvi.ucsf.edu/chimerax/data/pae-apr2022/pae.html){:target="\_blank"}.
+
+
+Pre-calculated AlphFold2 predictions are provided [here](abagtest_2d03e.result.zip){:target="\_blank"}. This archive contains the five predicted models (the naming indicates the rank), figures (png) files (PAE, pLDDT, coverage) and json files containing the corresponding values (the last part of the json files report the ptm and iptm values).
+
+
+
+
+### Analysis of the generated AF2 models
+
+While the notebook is running, models will appear first under the `Run Prediction` section, colored both by chain and by pLDDT.
+
+The best model will then be displayed under the `Display 3D structure` section. This is an interactive 3D viewer that allows you to rotate the molecule and zoom in or out.
+
+**Note** that you can change the model displayed with the _rank_num_ option. After changing, it execute the cell by clicking on the run cell icon on the left of it.
+
+
+ How similar are the five models generated by AF2?
+
+
+Consider the pLDDT of the various models (the higher the pLDDT the more reliable the model).
+
+
+ What is the confidence of those predictions? (check again the FAQ page of the Alphafold database provided above for pLDDT values)
+
+
+While the pLDDT score is an overall measure, you can also focus on the interface score reported in the `iptm` score (value between 0 and 1).
+
+
+
+
+
+ See the confidence statistics for the five generated models
+
+
+
+
+Note that if you performed a fresh run your results might well differ from those shown here.
+
+
+
+
+
+ Based on the iptm scores, would you qualify those models as reliable?
+
+
+**Note** that in this case the iptm score reports on all interfaces, i.e. both the interface between the two chains of the antibody, and the antibody-antigen interface
+
+Another useful way of looking at the model accuracy is to check the Predicted Alignment Error plots (PAE) (also referred to as Domain position confidence).
+The PAE gives a distance error for every pair of residues: It gives the estimate of the position error at residue x when the predicted and true structures are aligned on residue y.
+Values range from 0 to 35 Angstroms.
+It is usually shown as a heatmap image with residue numbers running along vertical and horizontal axes and each pixel colored according to the PAE value for the corresponding pair of residues.
+If the relative position of two domains is confidently predicted then the PAE values will be low (less than 5A - dark blue) for pairs of residues with one residue in each domain.
+When analysing your complex, the diagonal block will indicate the PAE within each molecule/domain, while the off-diagonal blocks report the accuracy of the domain-domain placement.
+
+
+Our antibody-antigen complex consists of three interfaces:
+
+* The interface between the heavy and light chains of the antibody
+* The interface between the heavy chain of the antibody and the antigen
+* The interface between the light chain of the antibody and the antigen
+
+
+
+
+ See the PAE plots for the five generated models
+
+
+
+
+
+
+
+
+
+ Based on the PAE plots, which interfaces can be considered reliable/unreliable?
+
+
+
+
+
+### Visualization of the generated AF2 models
+
+In order to visualize the models in PyMOL save your predictions to disk or download the precalculated AlphaFold2 models from [here](abagtest_2d03e.result.zip){:target="\_blank"}.
+
+Start PyMOL and load via the File menu all five AF2 models.
+
+File menu -> Open -> select abagtest_2d03e_unrelaxed_rank_001_alphafold2_multimer_v3_model_3_seed_000.pdb
+
+Repeat this for each model (`abagtest_2d03e_unrelaxed_rank_X_alphafold2_multimer_v3_model_X_seed_000.pdb` or whatever the naming of your model is).
+
+Let us superimpose all models on the antibody (the antibody in the provided AF2 models correspond to chains A and B):
+
+
+util.cbc
+select abagtest_2d03e_unrelaxed_rank_001_alphafold2_multimer_v3_model_3_seed_000 and chain A+B
+alignto sele
+
+
+This will align all clusters on the antibody, maximizing the differences in the orientation of the antigen.
+
+
+Examine the various models. How does the orientation of the antigen differ between them?
+
+
+**Note:** You can turn on and off a model by clicking on its name in the right panel of the PyMOL window.
+
+
+
+
+ See tips on how to visualize the prediction confidence in PyMOL
+
+
+ When looking at the structures generated by AlphaFold2 in PyMOL, the pLDDT is encoded as the B-factor.
+ To color the model according to the pLDDT type in PyMOL:
+
+
+ spectrum b
+
+
+ **Note** that the scale in the B-factor field is the inverse of the color coding in the PAE plots: i.e. red mean reliable (high pLDDT) and blue unreliable (low pLDDT))
+
+
+
+Since we do have NMR chemical shift perturbation data for the antigen, we can check if the perturbed residues are at the interface in the AF2 models.
+Note that there is a shift in the numbering of 2 residues between the AF2 and the HADDOCK models.
+
+
+util.cbc
+select epitope, (resi 70,71,72,73,81,82,87,88,90,92,94,95,96,113,114,115) and chain C
+color orange, epitope
+
+
+
+Does any model have the NMR-identified epitope at the interface with the antibody?
+
+
+
+
+
+
+ See the AlphaFold2 models with the NMR-mapped epitope
+
+
+
+
+
+
+
+
+
+It should be clear from the visualization of the NMR-mapped epitope on the AF2 models that none satisfies the NMR data.
+To further make that clear we can compare the models to the crystal structure of the complex (4G6M).
+
+For this download and superimpose the models onto the crystal structure in PyMOL with the following commands:
+
+
+fetch 4G6M
+remove resn HOH
+color yellow, 4G6M
+select 4G6M and chain H+L
+alignto sele
+
+
+
+
+
+ See the AlphaFold2 models superimposed onto the crystal structure of the complex (4G6M)
+
+
+
+
+
+
+
+
+
+
+More recently, the third version of AlphaFold (AlphaFold3) has been [published](https://www.nature.com/articles/s41586-024-07487-w){:target="\_blank"}.
+While the code is not yet released, a dedicated online tool [AlphaFoldServer](https://golgi.sandbox.google.com/){:target="\_blank"} is made available for the academic community to allow us to make upto 20 predictions per day with this new version.
+Pre-calculated AlphFold3 predictions are provided [here](af3server_abag_15052024.zip){:target="\_blank"}.
+
+
+Try to reproduce the previous steps and examine the quality of the various generated models. Do AlphaFold3 provide better predictions?
+
+
+
+
+
+ See the AlphaFold3 models with mapped epitope residues in orange
+
+
+
+
+
+
+
+
+
+
+
+
+ See the AlphaFold3 models onto the crystal structure of the complex (4G6M) in red
+
+
+
+
+
+
+
+
+
+
+
+
+
+## BONUS 5: Introduction to the haddock3 webapp
+
+In addition to the command line interface of haddock3, we are currently developing a new dedicated web application enabling the use of haddock3 under a graphical user interface, directly linking the software functionalities and computed results in an interactive manner.
+While not yet deployed as a web service, the application is already available for local installations.
+
+
+### Intallation procedure
+
+The current version requires [docker](https://www.docker.com/) to build the web application.
+Once docker is installed on your computer, deploying the webapp is as simple as following the next 3 command lines.
+
+**Note** that for the sake of this tutorial, the webapp was already built on your computer, and you can simply access it using the following url: [http://localhost:8080](http://localhost:8080)
+
+
+
+
+ How to build the webappexpand_more
+
+
+First, download the haddock3 webapp:
+
+git clone https://github.com/i-VRESSE/haddock3-webapp.git
+
+Then copy a compiled CNS executable in the `deploy/` directory.
+**Note** that the container is running under Linux, therfore a Linux-compiled version (compatible with your CPU architecture) of CNS is required here. Instructions on how to compile CNS are available in the [haddock3 documentation](https://github.com/haddocking/haddock3/blob/main/docs/CNS.md)
+
+As this is still in heavy development, the instructions on how to build the web-app is not yet trivial. Better instructions will be written onces it will be in production.
+
+
+
+### Creating an account
+
+For the sake of this tutorial, we have already created accounts:
+* **email**: summerschool@bioexcel.eu
+* **password**: haddock3
+
+
+
+ How to create an accountexpand_more
+
+
+To create a new account, add your email adresse and choose a password.
+
+
+Right after its creation, you will be able to login, but before you can definitely use all the functionalities of the webapp, your account must be validated by an adminitrator that will grant you priviledges.
+
+**Note** that with the current implementation, the first user to register comes the administrator.
+
+
+
+
+### Running this tutorial
+
+Once logged in, click on the `build` menu to start the creation of a custom workflow.
+You will land on the workflow-builder page, where you can interactively build your haddock3 scenario by combining the available modules.
+This page is subdivided into three areas described below.
+
+
+
+
+
+On the left is presented the list of modules.
+To add a module to the workflow, just click on it, and it will be automatically added at the bottom of the configuration file.
+Alternatively, you can drag-and-drop (using the dots) it to the central panel, at the location where you wish to place it.
+
+The set of modules defining your current workflow is presented on the central panel.
+You can switch between interactive (click on “Visual” tab under the Workflow section) and textual (click on “Text” tab) forms of it.
+You can configure the parameters of each module by clicking on this module (inside the central panel).
+
+Initially, default parameters are set for each module.
+Parameters are sub-categorized based on their properties.
+Unfold a property by clicking on it, and discover the set of related parameters.
+**Note** that you should always click the `save` button after modifying a parameter value for it to be taken into consideration.
+
+Finally, once you configured your workflow, click on `submit` to launch the corresponding haddock3 run.
+
+
+
+
+ Display available modulesexpand_more
+
+
+
+* **Topology modules**
+ * `topoaa`: *Generates the all-atom topologies for the CNS engine.*
+
+* **Sampling modules**
+ * `rigidbody`: *Performs rigid body energy minimization with CNS (`it0` in haddock2.x).*
+ * `lightdock`: *Third-party glow-worm swam optimization docking software.*
+
+* **Model refinement modules**
+ * `flexref`: *Performs semi-flexible refinement using a simulated annealing protocol through molecular dynamics simulations in torsion angle space (`it1` in haddock2.x).*
+ * `emref`: *Performs refinement by energy minimisation (`itw` EM only in haddock2.4).*
+ * `mdref`: *Performs refinement by a short molecular dynamics simulation in explicit solvent (`itw` in haddock2.X).*
+
+* **Scoring modules**
+ * `emscoring`: *Performs scoring of a complex performing a short EM (builds the topology and all missing atoms).*
+ * `mdscoring`: *Performs scoring of a complex performing a short MD in explicit solvent + EM (builds the topology and all missing atoms).*
+
+* **Analysis modules**
+ * `alascan`: *Performs a systematic (or user-define) alanine scanning mutagenesis of interface residues.*
+ * `caprieval`: *Calculates CAPRI metrics (i-RMSD, l-RMSD, Fnat, DockQ) with respect to the top scoring model or reference structure if provided.*
+ * `clustfcc`: *Clusters models based on the fraction of common contacts (FCC)*
+ * `clustrmsd`: *Clusters models based on pairwise RMSD matrix calculated with the `rmsdmatrix` module.*
+ * `contactmap`: *Generate contact matrices of both intra- and intermolecular contacts and a chordchart of intermolecular contacts.*
+ * `rmsdmatrix`: *Calculates the pairwise RMSD matrix between all the models generated in the previous step.*
+ * `ilrmsdmatrix`: *Calculates the pairwise interface-ligand-RMSD (il-RMSD) matrix between all the models generated in the previous step.*
+ * `seletop`: *Selects the top N models from the previous step.*
+ * `seletopclusts`: *Selects top N clusters from the previous step.*
+
+
+
+
+
+**Note** that you can also upload a zip archive of a workflow containing a configuration file named `workflow.cfg` and all corresponding files (e.g.: pdb structures, restraints files, topological parameters, etc.). Workflow archives presented in this tutorial are available in `workflows/webapp-workflows/`.
+
+
+### Loading haddock3 runs
+
+The webapp also allows you to upload a pre-computed run, so you can navigate through the docking results with ease thanks to the graphical interface.
+Under the `Upload` menu, you can upload two types of zip archives:
+
+* **Workflow**: a zip archive containing a configuration file named `workflow.cfg` and all corresponding files (e.g.: pdb structures, restraints files, topological parameters, etc.)
+* **Run**: a zip archive of the run.
+
+
+
+ How to generate a zip archive of a run?expand_more
+
+
+
+First go to the run directory containing all the generated data
+cd run_dir
+Create the zip archive
+zip -r run.zip .
+
+
+
+
+
+**Note** that the archives of workflows are available in `workflows/webapp-workflows/`, and archives of pre-computed runs are stored in `runs/webapp_runs/`.
+
+
+### Navigating throught the results
+
+On the `Manage` page, a table displays all the haddock3 runs performed by one user.
+This table contains the job status (queued, running, error, ok), its name, creation date and modification date.
+On the right side of the table, actions can be performed.
+The current implementation allows to rename a run or to delete it.
+
+
+
+
+
+To access the content of a run, click on its name to be directed to the haddock3 webapp results page.
+You will land on the analysis page, which summarizes the performance of the models obtained at the last stage.
+This is similar to the previous method of opening the `report.html` file (see above), which contains various plots displaying the HADDOCK score and its components against different CAPRI metrics.
+In this case, because a reference was provided during the `caprieval` module, performance is evaluated based on this structure.
+
+In addition, you can click on the `browse` button, which will let you access all the files of the run.
+
+
+### Running a scoring scenario
+
+In this scenario, we want to score the various models obtained at the previous stages (ensemble docking and AlphaFold predictions) and observe if the HADDOCK scoring function is able to detect the quality of the models.
+
+In this scenario, we want to:
+- Start by generating the topologies for the various models.
+- Cluster the models using Fraction of Common Contacts:
+ - set the parameter `min_population` to 1 so that all models, including singletons (models that do not cluster with any others), will be forwarded to the next steps.
+ - set the parameter `plot_matrix` to true to generate a matrix of the clusters for a visual representation.
+- Add the Energy Minimisation module to score all complexes.
+- End the scenario with a comparison of the models with the reference complex `4G6M_matched.pdb` using CAPRI criteria.
+
+For this, two ensembles must be scored and one structure will be used as a reference. You can find them in the `pdbs/` directory:
+- `07_emref_and_top5af2_ensemble.pdb`: An ensemble of models obtained from the ensemble run, combined with top5 AlphaFold2 predictions.
+- `af3server_15052024_top5ens.pdb`: An ensemble of top5 AlphaFold3 predictions.
+- `4G6M_matched.pdb`: The reference structure for quality assessments.
+
+
+
+Generate a simple scoring configuration file scenario using the workflow builder.
+
+
+
+{% highlight toml %}
+# ====================================================================
+# Antibody-antigen docking example with restraints from the antibody
+# paratope to the NMR-identified epitope on the antigen
+# ====================================================================
+run_dir = "scoring-haddock3-alphafold2and3-ensemble"
+
+molecules = [
+ "07_emref_and_top5af2_ensemble.pdb",
+ "af3server_15052024_top5ens.pdb",
+ ]
+
+# ====================================================================
+# Parameters for each stage are defined below
+# ====================================================================
+
+# Start by generating the topologies
+[topoaa]
+
+# Cluster structures to observe similarities
+[clustfcc]
+# Reducing min_population to define a cluster to 1 so even complexes
+# that do not cluster with any other will define singlotons
+min_population = 1
+# Generate a matrix of the clusters
+plot_matrix = true
+
+# Run the Energy Minimisation Scoring module
+[emscoring]
+
+
+# Evaluate the models with the CAPRI criterions
+[caprieval]
+reference_fname = "4G6M_matched.pdb"
+
+# ====================================================================
+
+{% endhighlight %}
+
+
+To simplify the tutorial, scoring scenario configuration files are provided in the `workflow/` directory, precomputed results in the `runs/` directory and finally archives for the haddock3-webapp upload section in `workflow/webapp/scoring-*.cfg`.
+
+
+How are scoring the AlphaFold predictions?
+
+
+
+Can the HADDOCK scoring function identify the best models?
+
+
+
+
+
+
+## Congratulations! 🎉
+
+You have completed this tutorial. If you have any questions or suggestions, feel free to contact us via email or asking a question through our [support center](https://ask.bioexcel.eu){:target="_blank"}.
+
+And check also our [education](/education){:target="_blank"} web page where you will find more tutorials!
+
+
+
+
+
+[air-help]: https://www.bonvinlab.org/software/haddock2.4/airs/ "AIRs help"
+[gentbl]: https://wenmr.science.uu.nl/gentbl/ "GenTBL"
+[haddock24protein]: /education/HADDOCK24/HADDOCK24-protein-protein-basic/
+[haddock-repo]: https://github.com/haddocking/haddock3 "HADDOCK3 GitHub"
+[haddock-tools]: https://github.com/haddocking/haddock-tools "HADDOCK tools GitHub"
+[installation]: https://www.bonvinlab.org/haddock3/INSTALL.html "Installation"
+[link-cns]: https://cns-online.org "CNS online"
+[link-forum]: https://ask.bioexcel.eu/c/haddock "HADDOCK Forum"
+[link-freesasa]: https://freesasa.github.io "FreeSASA"
+[link-pdbtools]:http://www.bonvinlab.org/pdb-tools/ "PDB-Tools"
+[link-pymol]: https://www.pymol.org/ "PyMOL"
+[nat-pro]: https://www.nature.com/nprot/journal/v5/n5/abs/nprot.2010.32.html "Nature protocol"
+[tbl-examples]: https://github.com/haddocking/haddock-tools/tree/master/haddock_tbl_validation "tbl examples"
diff --git a/education/HADDOCK3/HADDOCK3-antibody-antigen-bioexcel2024/index.md-Bratislava b/education/HADDOCK3/HADDOCK3-antibody-antigen-bioexcel2024/index.md-Bratislava
new file mode 100644
index 00000000..e8424c64
--- /dev/null
+++ b/education/HADDOCK3/HADDOCK3-antibody-antigen-bioexcel2024/index.md-Bratislava
@@ -0,0 +1,1531 @@
+---
+layout: page
+title: "Low-sampling antibody-antigen modelling tutorial using a local version of HADDOCK3"
+excerpt: "A tutorial describing the use of HADDOCK3 in the low-sampling scenario to model an antibody-antigen complex"
+tags: [HADDOCK, HADDOCK3, installation, preparation, proteins, docking, analysis, workflows, sampling]
+image:
+ feature: pages/banner_education-thin.jpg
+---
+This tutorial consists of the following sections:
+
+* table of contents
+{:toc}
+
+
+
+
+## Introduction
+
+This tutorial demonstrates the use of the new modular HADDOCK3 version for predicting
+the structure of an antibody-antigen complex using knowledge of the hypervariable loops
+on the antibody (i.e., the most basic knowledge) and epitope information identified from NMR experiments for the antigen to guide the docking.
+
+An antibody is a large protein that generally works by attaching itself to an antigen,
+which is a unique site of the pathogen. The binding harnesses the immune system to directly
+attack and destroy the pathogen. Antibodies can be highly specific while showing low immunogenicity,
+which is achieved by their unique structure. **The fragment crystallizable region (Fc region)**
+activates the immune response and is species-specific, i.e. the human Fc region should not
+induce an immune response in humans. **The fragment antigen-binding region (Fab region**)
+needs to be highly variable to be able to bind to antigens of various nature (high specificity).
+In this tutorial we will concentrate on the terminal **variable domain (Fv)** of the Fab region.
+
+
+
+
+
+The small part of the Fab region that binds the antigen is called **paratope**. The part of the antigen
+that binds to an antibody is called **epitope**. The paratope consists of six highly flexible loops,
+known as **complementarity-determining regions (CDRs)** or hypervariable loops whose sequence
+and conformation are altered to bind to different antigens. CDRs are shown in red in the figure below:
+
+
+
+
+
+In this tutorial we will be working with Interleukin-1β (IL-1β)
+(PDB code [4I1B](https://www.ebi.ac.uk/pdbe/entry/pdb/4i1b){:target="_blank"}) as an antigen
+and its highly specific monoclonal antibody gevokizumab
+(PDB code [4G6K](https://www.ebi.ac.uk/pdbe/entry/pdb/4g6k){:target="_blank"})
+(PDB code of the complex [4G6M](https://www.ebi.ac.uk/pdbe/entry/pdb/4g6m){:target="_blank"}).
+
+
+Throughout the tutorial, colored text will be used to refer to questions or
+instructions, and/or PyMOL commands.
+
+This is a question prompt: try answering it!
+This an instruction prompt: follow it!
+This is a PyMOL prompt: write this in the PyMOL command line prompt!
+This is a Linux prompt: insert the commands in the terminal!
+
+
+
+
+## Setup/Requirements
+
+In order to follow this tutorial you will need to work on a Linux or MacOSX system. We will also make use of [**PyMOL**][link-pymol] (freely available for most operating systems) in order to visualize the input and output data.
+
+### PyMOL on the DEVANA cluster
+It is possible to run PyMOL directly on DEVANA by connecting to the [DEVANA Desktop application](https://ood.devana.nscc.sk/pun/sys/dashboard/batch_connect/sessions). There, you can start a Desktop session by specifying the **testing** partition and 1 as the number of cores.
+
+Once logged in, you can start a terminal in the DEVANA Desktop emulator. From there, you can start PyMOL by typing:
+
+module load PyMOL
+
+and then
+
+pymol
+
+
+Further we are providing pre-processed PDB files for docking and analysis (but the preprocessing of those files will also be explained in this tutorial). The files have been processed
+to facilitate their use in HADDOCK and for allowing comparison with the known reference structure of the complex.
+
+For this, navigate through the terminal to the tutorial directory:
+
+
+
+ Cagliariexpand_more
+
+
+
+ cd /home/utente/BioExcel_SS_2023/HADDOCK
+
+
+
+
+### Bratislava
+
+ Please connect to the DEVANA supercomputer using your credentials, either using SSH or accessing [this page](https://ood.devana.nscc.sk/pun/sys/shell/ssh/login01)
+
+ From your home directory, navigate to the tutorial directory:
+
+ cd HADDOCK
+
+
+
+In it you should find the following directories and files:
+
+* `pdbs`: Contains the pre-processed PDB files
+* `restraints`: Contains the interface information and the corresponding restraint files for HADDOCK
+* `runs`: Contains pre-calculated run results for the various protocols in this tutorial
+* `scripts`: Contains a variety of scripts used in this tutorial
+* `docking-antibody-antigen-CDR-NMR-CSP*.cfg`: the different HADDOCK3 configuration files that can be used in the tutorial
+
+
+
+If you are working from your own computer please download [this zip archive](https://surfdrive.surf.nl/files/index.php/s/2NbStaQ4ub5Vgv1). Remember that on your local machine you'll have to install CNS and HADDOCK3.
+
+## HADDOCK general concepts
+
+HADDOCK (see [https://www.bonvinlab.org/software/haddock2.4](https://www.bonvinlab.org/software/haddock2.4){:target="_blank"})
+is a collection of python scripts derived from ARIA ([https://aria.pasteur.fr](https://aria.pasteur.fr){:target="_blank"})
+that harness the power of CNS (Crystallography and NMR System – [https://cns-online.org](https://cns-online.org){:target="_blank"})
+for structure calculation of molecular complexes. What distinguishes HADDOCK from other docking software is its ability,
+inherited from CNS, to incorporate experimental data as restraints and use these to guide the docking process alongside
+traditional energetics and shape complementarity. Moreover, the intimate coupling with CNS endows HADDOCK with the
+ability to actually produce models of sufficient quality to be archived in the Protein Data Bank.
+
+A central aspect to HADDOCK is the definition of Ambiguous Interaction Restraints or AIRs. These allow the
+translation of raw data such as NMR chemical shift perturbation or mutagenesis experiments into distance
+restraints that are incorporated in the energy function used in the calculations. AIRs are defined through
+a list of residues that fall under two categories: active and passive. Generally, active residues are those
+of central importance for the interaction, such as residues whose knockouts abolish the interaction or those
+where the chemical shift perturbation is higher. Throughout the simulation, these active residues are
+restrained to be part of the interface, if possible, otherwise incurring in a scoring penalty. Passive residues
+are those that contribute for the interaction, but are deemed of less importance. If such a residue does
+not belong in the interface there is no scoring penalty. Hence, a careful selection of which residues are
+active and which are passive is critical for the success of the docking.
+
+
+
+
+
+## A brief introduction to HADDOCK3
+
+HADDOCK3 is the next generation integrative modelling software in the
+long-lasting HADDOCK project. It represents a complete rethinking and rewriting
+of the HADDOCK2.X series, implementing a new way to interact with HADDOCK and
+offering new features to users who can now define custom workflows.
+
+In the previous HADDOCK2.x versions, users had access to a highly
+parameterisable yet rigid simulation pipeline composed of three steps:
+`rigid-body docking (it0)`, `semi-flexible refinement (it1)`, and `final refinement (itw)`.
+
+
+
+
+
+In HADDOCK3, users have the freedom to configure docking workflows into
+functional pipelines by combining the different HADDOCK3 modules, thus
+adapting the workflows to their projects. HADDOCK3 has therefore developed to
+truthfully work like a puzzle of many pieces (simulation modules) that users can
+combine freely. To this end, the “old” HADDOCK machinery has been modularized,
+and several new modules added, including third-party software additions. As a
+result, the modularization achieved in HADDOCK3 allows users to duplicate steps
+within one workflow (e.g., to repeat twice the `it1` stage of the HADDOCK2.x
+rigid workflow).
+
+Note that, for simplification purposes, at this time, not all functionalities of
+HADDOCK2.x have been ported to HADDOCK3, which does not (yet) support NMR RDC,
+PCS and diffusion anisotropy restraints, cryo-EM restraints and coarse-graining.
+Any type of information that can be converted into ambiguous interaction
+restraints can, however, be used in HADDOCK3, which also supports the
+*ab initio* docking modes of HADDOCK.
+
+
+
+
+
+To keep HADDOCK3 modules organized, we catalogued them into several
+categories. But, there are no constraints on piping modules of different
+categories.
+
+The main module categories are "topology", "sampling", "refinement",
+"scoring", and "analysis". There is no limit to how many modules can belong to a
+category. Modules are added as developed, and new categories will be created
+if/when needed. You can access the HADDOCK3 documentation page for the list of
+all categories and modules. Below is a summary of the available modules:
+
+* **Topology modules**
+ * `topoaa`: *generates the all-atom topologies for the CNS engine.*
+* **Sampling modules**
+ * `rigidbody`: *Rigid body energy minimization with CNS (`it0` in haddock2.x).*
+ * `lightdock`: *Third-party glow-worm swam optimization docking software.*
+* **Model refinement modules**
+ * `flexref`: *Semi-flexible refinement using a simulated annealing protocol through molecular dynamics simulations in torsion angle space (`it1` in haddock2.x).*
+ * `emref`: *Refinement by energy minimisation (`itw` EM only in haddock2.4).*
+ * `mdref`: *Refinement by a short molecular dynamics simulation in explicit solvent (`itw` in haddock2.X).*
+* **Scoring modules**
+ * `emscoring`: *scoring of a complex performing a short EM (builds the topology and all missing atoms).*
+ * `mdscoring`: *scoring of a complex performing a short MD in explicit solvent + EM (builds the topology and all missing atoms).*
+* **Analysis modules**
+ * `caprieval`: *Calculates CAPRI metrics (i-RMSD, l-RMSD, Fnat, DockQ) with respect to the top scoring model or reference structure if provided.*
+ * `clustfcc`: *Clusters models based on the fraction of common contacts (FCC)*
+ * `clustrmsd`: *Clusters models based on pairwise RMSD matrix calculated with the `rmsdmatrix` module.*
+ * `rmsdmatrix`: *Calculates the pairwise RMSD matrix between all the models generated in the previous step.*
+ * `seletop`: *Selects the top N models from the previous step.*
+ * `seletopclusts`: *Selects top N clusters from the previous step.*
+
+The HADDOCK3 workflows are defined in simple configuration text files, similar to the TOML format but with extra features.
+Contrarily to HADDOCK2.X which follows a rigid (yet highly parameterisable)
+procedure, in HADDOCK3, you can create your own simulation workflows by
+combining a multitude of independent modules that perform specialized tasks.
+
+
+
+
+
+## Software requirements
+
+
+### Installing CNS
+The other required piece of software to run HADDOCK is its computational engine,
+CNS (Crystallography and NMR System –
+[https://cns-online.org](https://cns-online.org){:target="_blank"}). CNS is
+freely available for non-profit organizations. In order to get access to all
+features of HADDOCK you will need to compile CNS using the additional files
+provided in the HADDOCK distribution in the `varia/cns1.3` directory. Compilation of
+CNS might be non-trivial. Some guidance on installing CNS is provided in the online
+HADDOCK3 documentation page [here](https://www.bonvinlab.org/haddock3/CNS.html){:target="_blank"}.
+
+In this tutorial CNS has already been installed, so you don't have to worry.
+
+
+### Installing HADDOCK3
+
+In this tutorial we will make use of the HADDOCK3 version. HADDOCK3 is already pre-installed in your system.
+
+To make sure the HADDOCK3 is properly installed
+
+
+ Cagliariexpand_more
+
+
+ activate its conda environment:
+
+
+ conda activate haddock3
+
+
+
+### Bratislava
+
+ load the Python 3.9.6 module:
+
+
+ module load Python/3.9.6-GCCcore-11.2.0
+
+
+ and then source the HADDOCK3 environment:
+
+ source /home/projects/training-05/.haddock3-env/bin/activate
+
+
+and then type
+
+
+haddock3 -h
+
+
+in the terminal. You should see a small help message explaining how to use the software.
+
+In case you want to obtain HADDOCK3 for another platform, navigate to [its repository][haddock-repo], fill the
+registration form, and then follow the [installation instructions](https://www.bonvinlab.org/haddock3/INSTALL.html){:target="_blank"}.
+
+
+### Auxiliary software
+
+**[PDB-tools][link-pdbtools]**: A useful collection of Python scripts for the
+manipulation (renumbering, changing chain and segIDs...) of PDB files is freely
+available from our GitHub repository. `pdb-tools` is automatically installed
+with HADDOCK3. If you have activated the HADDOCK3 Python environment you have
+access to the pdb-tools package.
+
+**[PyMol][link-pymol]**: We will make use of PyMol for visualization. If not already installed on your system, download and install PyMol.
+
+
+
+
+## Preparing PDB files for docking
+
+In this section we will prepare the PDB files of the antibody and antigen for docking.
+Crystal structures of both the antibody and the antigen in their free forms are available from the
+[PDBe database](https://www.pdbe.org){:target="_blank"}. In the case of the antibody which consists
+of two chains (L+H) we will have to prepare it for use in HADDOCK such as it can be treated as
+a single chain with non-overlapping residue numbering. For this we will be making use of `pdb-tools` from the command line.
+
+_**Note**_ that `pdb-tools` is also available as a [web service](https://wenmr.science.uu.nl/pdbtools/){:target="_blank"}.
+
+
+
+
+### Preparing the antibody structure
+
+Using PDB-tools we will download the unbound structure of the antibody from the PDB database (the PDB ID is [4G6K](https://www.ebi.ac.uk/pdbe/entry/pdb/4g6k){:target="_blank"}) and then process it to have a unique chain ID (A) and non-overlapping residue numbering by renumbering the merged pdb (starting from 1).
+
+This can be done from the command line with:
+
+
+pdb_fetch 4G6K | pdb_tidy -strict | pdb_selchain -H | pdb_delhetatm | pdb_fixinsert | pdb_keepcoord | pdb_selres -1:120 | pdb_tidy -strict > 4G6K_H.pdb
+
+
+pdb_fetch 4G6K | pdb_tidy -strict | pdb_selchain -L | pdb_delhetatm | pdb_fixinsert | pdb_keepcoord | pdb_selres -1:107 | pdb_tidy -strict > 4G6K_L.pdb
+
+
+pdb_merge 4G6K_H.pdb 4G6K_L.pdb | pdb_reres -1 | pdb_chain -A | pdb_chainxseg | pdb_reres -1 | pdb_tidy -strict > 4G6K_clean.pdb
+
+
+The first command fetches the PDB ID, selects the heavy chain (H) and removes water and heteroatoms (in this case no co-factor is present that should be kept).
+An important part for antibodies is the `pdb_fixinsert` command that fixes the residue numbering of the HV loops: Antibodies often follow the [Chothia numbering scheme](https://pubmed.ncbi.nlm.nih.gov/9367782/?otool=inluulib){:target="_blank"} and insertions created by this numbering scheme (e.g. 82A, 82B, 82C) cannot be processed by HADDOCK directly. As such renumbering is necessary before starting the docking. Then, the command `pdb_selres` selects only the residues from 1 to 120, so as to consider only the variable domain (FV) of the antibody. This allows to save a substantial amount of computational resources.
+
+The second command does the same for the light chain (L) with the difference that the light chain is slightly shorter and we can focus on the first 107 residues.
+
+The third and last command merges the two processed chains and assign them unique chain and segIDs, resulting in the HADDOCK-ready `4G6K_clean.pdb` file. You can view its sequence running:
+
+
+pdb_tofasta 4G6K_clean.pdb
+
+
+_**Note**_ that the corresponding files can be found in the `pdbs` directory of the archive you downloaded.
+
+
+
+### Machine-learning-based modelling of antibodies
+
+The release of Alphafold2 in late 2020 has brought structure prediction methods to a new frontier, providing accurate models for the majority of known proteins. This revolution did not spare antibodies, with [Alphafold2-multimer](https://github.com/sokrypton/ColabFold) and other prediction methods (most notably [ABodyBuilder2](https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabpred/abodybuilder2/), from the ImmuneBuilder suite) performing nicely on the variable regions.
+
+For a short introduction to AI and AlphaFold refer to this other tutorial [introduction](/education/molmod_online/alphafold/#introduction){:target="_blank"}.
+
+CDR loops are clearly the most challenging region to be predicted given their high sequence variability and flexibility. Multiple Sequence Alignment (MSA)-derived information is also less useful in this context.
+
+Here we will see whether the antibody models given by Alphafold2-multimer and ABodyBuilder2 can be used to target the antigen in place of the standard unbound form, which is not usually available.
+
+We already ran the prediction with these two tools, and you can find them in the `pdbs` directory (with names `4g6k_Abodybuilder2.pdb` and `4g6k_AF2_multimer.pdb`).
+
+Let's preprocess these models!
+
+
+pdb_tidy -strict pdbs/4g6k_Abodybuilder2.pdb | pdb_selchain -H | pdb_delhetatm | pdb_fixinsert | pdb_keepcoord | pdb_tidy -strict > 4G6K_abb_H.pdb
+
+
+pdb_tidy -strict pdbs/4g6k_Abodybuilder2.pdb | pdb_selchain -L | pdb_delhetatm | pdb_fixinsert | pdb_keepcoord | pdb_tidy -strict > 4G6K_abb_L.pdb
+
+
+ pdb_merge 4G6K_abb_H.pdb 4G6K_abb_L.pdb | pdb_chain -A | pdb_chainxseg | pdb_reres -1 | pdb_tidy -strict > 4G6K_abb_clean.pdb
+
+
+Now the Alphafold2-multimer top ranked structure. By default it is written to disk with chains A and B.
+
+
+pdb_tidy -strict pdbs/4g6k_AF2_multimer.pdb | pdb_selchain -A | pdb_delhetatm | pdb_fixinsert | pdb_keepcoord | pdb_tidy -strict > 4g6k_af2_A.pdb
+
+
+pdb_tidy -strict pdbs/4g6k_AF2_multimer.pdb | pdb_selchain -B | pdb_delhetatm | pdb_fixinsert | pdb_keepcoord | pdb_tidy -strict > 4g6k_af2_B.pdb
+
+
+pdb_merge 4g6k_af2_A.pdb 4g6k_af2_B.pdb | pdb_chain -A | pdb_chainxseg | pdb_reres -1 | pdb_tidy -strict > 4G6K_af2_clean.pdb
+
+
+Let's load the three cleaned antibody structures in Pymol to see whether they resemble the experimental unbound structure.
+
+
+File menu -> Open -> select 4G6K_clean.pdb
+
+
+File menu -> Open -> select 4G6K_abb_clean.pdb
+
+
+File menu -> Open -> select 4G6K_af2_clean.pdb
+
+
+We now use the backbone RMSD to align the machine learning models to the experimental structure.
+
+alignto 4G6K_clean
+
+
+
+Which structure (between _4G6K_abb_clean.pdb_ and _4G6K_af2_clean.pdb_) is closer to the unbound conformation?
+
+
+Both ABodyBuilder2 and Alphafold2 can give an _ensemble_ of models in output. All the structures in these ensembles may be used as input antibody molecules in HADDOCK.
+
+The remaining of the tutorial will consider only the experimental unbound structure, but you can use your preprocessed predicted structures simply by substituting _4G6K_clean.pdb_ with either _4G6K_abb_clean.pdb_ or _4G6K_af2_clean.pdb_.
+
+### Preparing the antigen structure
+
+Using PDB-tools we will download the structure from the PDB database (the PDB ID is [4I1B](https://www.ebi.ac.uk/pdbe/entry/pdb/4i1b){:target="_blank"}), remove the hetero atoms and then process it to assign it chainID B.
+
+
+pdb_fetch 4I1B | pdb_tidy -strict | pdb_delhetatm | pdb_keepcoord | pdb_chain -B | pdb_chainxseg | pdb_tidy -strict > 4I1B_clean.pdb
+
+
+
+
+
+## Defining restraints for docking
+
+Before setting up the docking we need first to generate distance restraint files
+in a format suitable for HADDOCK. HADDOCK uses [CNS][link-cns]{:target="_blank"} as computational
+engine. A description of the format for the various restraint types supported by
+HADDOCK can be found in our [Nature Protocol][nat-pro]{:target="_blank"} paper, Box 4.
+
+Distance restraints are defined as:
+
+
+
+The lower limit for the distance is calculated as: distance minus lower-bound
+correction and the upper limit as: distance plus upper-bound correction. The
+syntax for the selections can combine information about chainID - `segid`
+keyword -, residue number - `resid` keyword -, atom name - `name` keyword.
+Other keywords can be used in various combinations of OR and AND statements.
+Please refer for that to the [online CNS manual](http://cns-online.org/v1.3/){:target="_blank"}.
+
+We will shortly explain in this section how to generate both ambiguous
+interaction restraints (AIRs) and specific distance restraints for use in
+HADDOCK illustrating a scenario in which no _a priori_ knowledge is available
+about the antibody binding site, but in which the antigen epitope has been pinpointed
+by an NMR chemical shift perturbation experiment.
+
+Information about various types of distance restraints in HADDOCK can also be
+found in our [online manual][air-help]{:target="_blank"} pages.
+
+
+
+### Identifying the paratope of the antibody
+
+Nowadays there are several computational tools that can identify the paratope (the residues of the hypervariable loops involved in binding) from the provided antibody sequence. In this tutorial we are providing you the corresponding list of residue obtained using [ProABC-2](https://wenmr.science.uu.nl/proabc2/){:target="_blank"}. ProABC-2 uses a convolutional neural network to identify not only residues which are located in the paratope region but also the nature of interactions they are most likely involved in (hydrophobic or hydrophilic). The work is described in [Ambrosetti, *et al* Bioinformatics, 2020](https://academic.oup.com/bioinformatics/article/36/20/5107/5873593){:target="_blank"}.
+
+The corresponding paratope residues (those with either an overall probability >= 0.4 or a probability for hydrophobic or hydrophilic > 0.3) are:
+
+
+
+We will now visualize the epitope on Interleukin-1β. For this start PyMOL and from the PyMOL File menu open the provided PDB file of the antigen.
+
+
+File menu -> Open -> select 4I1B_clean.pdb
+
+
+
+color white, all
+
+
+show surface
+
+
+select epitope, (resi 72+73+74+75+81+83+84+89+90+92+94+96+97+98+115+116+117)
+
+
+color red, epitope
+
+
+Inspect the surface.
+
+
+Do the identified residues form a well defined patch on the surface?
+
+
+The answer to that question should be yes, but we can see some residues not colored that might also be involved in the binding - there are some white spots around/in the red surface.
+
+
+
+ See surface view of the epitope identified by NMRexpand_more
+
+
+
+
+
+
+
+
+
+In HADDOCK we are dealing with potentially incomplete binding sites by defining surface neighbors as `passive` residues.
+These are added to the definition of the interface but will not lead to any energetic penalty if they are not part of the
+binding site in the final models, while the residues defined as `active` (typically the identified or predicted binding
+site residues) will. When using the HADDOCK server, `passive` residues will be automatically defined. Here since we are
+using a local version, we need to define those manually.
+
+This can easily be done in the following way:
+
+
+haddock3-restraints passive_from_active 4I1B_clean.pdb 72,73,74,75,81,83,84,89,90,92,94,96,97,98,115,116,117
+
+
+The NMR-identified residues and their surface neighbors generated with the above command can be used to define ambiguous interactions restraints, either using the NMR identified residues as active in HADDOCK, or combining those with the surface neighbors and use this combination as passive only. We will focus only on this second case here: the corresponding residues can be found in the `restraints/antigen-NMR-epitope.act-pass` file.
+The file consists of two lines, with the first one defining the `active` residues and
+the second line the `passive` ones. We will use later these files to generate the ambiguous distance restraints for HADDOCK.
+
+In general it is better to be too generous rather than too strict in the definition of passive residues.
+
+An important aspect is to filter both the active (the residues identified from
+your mapping experiment) and passive residues by their solvent accessibility.
+Our web service uses a default relative accessibility of 15% as cutoff. This is
+not a hard limit. You might consider including even more buried residues if some
+important chemical group seems solvent accessible from a visual inspection.
+
+
+
+
+### Defining ambiguous restraints
+
+Once you have defined your active and passive residues for both molecules, you
+can proceed with the generation of the ambiguous interaction restraints (AIR) file for HADDOCK.
+For this you can either make use of our online [GenTBL][gentbl] web service, entering the
+list of active and passive residues for each molecule, and saving the resulting
+restraint list to a text file, or use the relevant `haddock3-restraints` command.
+
+To use our `haddock3-restraints active_passive_to_ambig` script you need to
+create for each molecule a file containing two lines:
+
+* The first line corresponds to the list of active residues (numbers separated by spaces)
+* The second line corresponds to the list of passive residues.
+
+In this scenario the NMR epitope is defined as active (meaning ambiguous distance restraints will be defined from the NMR epitope residues) and the surface neighbors are used as passive residues in HADDOCK.
+
+* For the antibody we will use the file `antibody-paratope.act-pass` from the `restraints` directory:
+
+
+Using those two files, we can generate the CNS-formatted AIR restraint files with the following command:
+
+
+haddock3-restraints active_passive_to_ambig ./restraints/antibody-paratope.act-pass ./restraints/antigen-NMR-epitope.act-pass > ambig-paratope-NMR-epitope.tbl
+
+
+This generates a file called `ambig-paratope-NMR-epitope.tbl` that contains the AIR
+restraints. The default distance range for those is between 0 and 2Å, which
+might seem short but makes senses because of the 1/r^6 summation in the AIR
+energy function that makes the effective distance be significantly shorter than
+the shortest distance entering the sum.
+
+The effective distance is calculated as the SUM over all pairwise atom-atom
+distance combinations between an active residue and all the active+passive on
+the other molecule: SUM[1/r^6]^(-1/6).
+
+If you modify manually this file, it is possible to quickly check if the format is valid.
+
+
+haddock3-restraints validate_tbl ambig-paratope-NMR-epitope.tbl --silent
+
+
+No output means that your TBL file is valid.
+
+
+
+### Additional restraints for multi-chain proteins
+
+As an antibody consists of two separate chains, it is important to define a few distance restraints
+to keep them together during the high temperature flexible refinement stage of HADDOCK. This can easily be
+done using the `haddock3-restraints restrain_bodies` subcommand.
+
+
+haddock3-restraints restrain_bodies 4G6K_clean.pdb > antibody-unambig.tbl
+
+
+The result file contains two CA-CA distance restraints with the exact distance measured between the picked CA atoms:
+
+
+ assign (segid A and resi 110 and name CA) (segid A and resi 132 and name CA) 47.578 0.0 0.0
+ assign (segid A and resi 97 and name CA) (segid A and resi 204 and name CA) 33.405 0.0 0.0
+
+
+This file is also provided in the `restraints` directory of the archive you downloaded.
+
+If you are considering Alphafold2 or ABodyBuilder2 antibodies you have to create the appropriate distance restraints:
+
+
+haddock3-restraints restrain_bodies 4G6K_af2_clean.pdb > af2-antibody-unambig.tbl
+
+
+
+haddock3-restraints restrain_bodies 4G6K_abb_clean.pdb > abb-antibody-unambig.tbl
+
+
+
+
+
+## Setting up the docking with HADDOCK3
+
+Now that we have all required files at hand (PDB and restraints files) it is time to setup our docking protocol. The idea is to execute a fast HADDOCK3 docking workflow reducing the non-negligible computational cost of HADDOCK by decreasing the sampling, without impacting too much the accuracy of the resulting models.
+For this we need to create a HADDOCK3 configuration file that will define the docking workflow. In contrast to HADDOCK2.X,
+we have much more flexibility in doing this. As an example, we could use this flexibility by introducing a clustering step
+after the initial rigid-body docking stage, select up to 4 models per cluster and refine all of those.
+
+HADDOCK3 also provides an analysis module (`caprieval`) that allows
+to compare models to either the best scoring model (if no reference is given) or a reference structure, which in our case
+we have at hand. This will directly allow us to assess the performance of the protocol.
+
+The basic workflow will consists of the following modules:
+
+1. **`topoaa`**: *Generates the topologies for the CNS engine and build missing atoms*
+2. **`rigidbody`**: *Rigid body energy minimisation (`it0` in haddock2.x)*
+3. **`caprieval`**: *Calculates CAPRI metrics (i-RMSD, l-RMSD, Fnat, DockQ) with respect to the top scoring model or reference structure if provided*
+4. **`seletop`** : *Selection of the top X models from the previous module*
+5. **`flexref`**: *Semi-flexible refinement of the interface (`it1` in haddock2.4)*
+6. **`caprieval`**
+7. **`emref`**: *Final refinement by energy minimisation (`itw` EM only in haddock2.4)*
+8. **`caprieval`**
+9. **`clustfcc`**: *Clustering of models based on the fraction of common contacts (FCC)*
+10. **`seletopclusts`**: *Selection of the top10 models of all clusters*
+11. **`caprieval`**
+
+
+### HADDOCK3 execution modes
+
+HADDOCK3 currently supports three difference execution modes that are defined in the first section of the configuration file of a run:
+
+- **local mode** : in this mode HADDOCK3 will run on the current system, using the defined number of cores (`ncores`) in the config file to a maximum of the total number of available cores on the system minus one;
+- **batch mode**: in this mode HADDOCK3 will typically be started on your local server (e.g. the login node) and will dispatch jobs to the batch system of your cluster;
+- **MPI mode**: HADDOCK3 supports a parallel MPI implementation (functional but still very experimental at this stage).
+
+## Cagliari
+
+In this tutorial we are using local resources (our laptops), and therefore we will stick to the **local** mode. For the tutorial we limit the number of cores to 12, that is, the maximum number ofavailable cores on your computer.
+
+Make sure your `haddock3` conda environment is active:
+
+
+conda activate haddock3
+
+
+
+## Bratislava
+
+ In this tutorial we are using local resources of remote nodes on DEVANA, and therefore we will stick to the **local** mode. For the tutorial we limit the number of cores to 12, so as to avoid overloading the nodes.
+
+
+
+### Docking Scenario: Paratope - NMR-epitope
+
+Now that we have all data ready it is time to setup the docking. Here we are using the NMR-identified epitope, which is treated as active, meaning restraints will be defined from it to "force" it to be at the interface.
+
+The restraint file to use for this is `ambig-paratope-NMR-epitope.tbl`. We will also define the restraints to keep the two antibody chains together using for this the `antibody-unambig.tbl` restraint file.
+
+If you are using the Alphafold2 antibody you should use the *af2-antibody-unambig.tbl* file.
+
+If you are using the ABodyBuilder2 antibody you should use the *abb-antibody-unambig.tbl* file.
+
+In this case since we have information for both interfaces we use a low-sampling configuration file, which takes only a small amount of computational resources to run. The configuration file for this scenario (assuming a local running mode, eventually submitted to the batch system requesting a full node) is:
+
+{% highlight toml %}
+# ====================================================================
+# Antibody-antigen docking example with restraints from the antibody
+# paratope to the NMR-identified epitope on the antigen (as active)
+# and keeping the random removal of restraints
+# ====================================================================
+
+# directory name of the run
+run_dir = "run1-CDR-NMR-CSP"
+
+# compute mode
+mode = "local"
+# 12 local cores
+ncores = 12
+
+# Self contained rundir (to avoid problems with long filename paths)
+self_contained = true
+
+# Post-processing to generate statistics and plots
+postprocess = true
+
+# molecules to be docked
+molecules = [
+ "pdbs/4G6K_clean.pdb",
+ "pdbs/4I1B_clean.pdb"
+ ]
+
+# ====================================================================
+# Parameters for each stage are defined below, prefer full paths
+# ====================================================================
+[topoaa]
+
+[rigidbody]
+# CDR to NMR epitope ambig restraints
+ambig_fname = "restraints/ambig-paratope-NMR-epitope.tbl"
+# Restraints to keep the antibody chains together
+unambig_fname = "restraints/antibody-unambig.tbl"
+sampling = 96
+
+[caprieval]
+reference_fname = "pdbs/4G6M_matched.pdb"
+
+[seletop]
+select = 48
+
+[flexref]
+tolerance = 5
+# CDR to NMR epitope ambig restraints
+ambig_fname = "restraints/ambig-paratope-NMR-epitope.tbl"
+# Restraints to keep the antibody chains together
+unambig_fname = "restraints/antibody-unambig.tbl"
+
+[caprieval]
+reference_fname = "pdbs/4G6M_matched.pdb"
+
+[emref]
+# CDR to NMR epitope ambig restraints
+ambig_fname = "restraints/ambig-paratope-NMR-epitope.tbl"
+# Restraints to keep the antibody chains together
+unambig_fname = "restraints/antibody-unambig.tbl"
+
+[caprieval]
+reference_fname = "pdbs/4G6M_matched.pdb"
+
+[clustfcc]
+
+[seletopclusts]
+
+[caprieval]
+reference_fname = "pdbs/4G6M_matched.pdb"
+
+# ====================================================================
+
+{% endhighlight %}
+
+The idea of this configuration file is to generate 96 models with the standard rigid-body energy minimization (*rigidbody* module). Only the 48 best scoring models are selected (*seletop* module) for flexible refinement (*flexref* module). Refined modes are then subject to a short energy minimisation in the OPLS force field (*emref*). FCC clustering (*clustfcc*) is applied at the end of the workflow to group together models sharing a consistent fraction of the interface contacts. The top 4 models of each cluster are saved to disk (*seletopclusts*). Multiple *caprieval* modules are executed at different stages of the workflow to check how the quality (and rankings) of the models change throughout the protocol.
+
+
+
+
+ Cagliariexpand_more
+
+
+ This configuration file is provided in the `/home/utente/BioExcel_SS_2023/HADDOCK` directory on your laptop as `docking-antibody-antigen-CDR-NMR-CSP.cfg` (`docking-antibody-antigen-CDR-NMR-CSP-af2.cfg` and `docking-antibody-antigen-CDR-NMR-CSP-abb.cfg` for Alphafold2 and ABodyBuilder2 antibodies, respectively).
+
+ If you want to use your own pdb and restraint files please change the paths in the configuration files (for example from `pdbs/4G6K_clean.pdb` to `4G6K_abb_clean.pdb`).
+
+ If you have everything ready, you can launch haddock3 from the command line.
+
+
+ haddock3 docking-antibody-antigen-CDR-NMR-CSP.cfg
+
+
+
+
+## Bratislava
+
+ This configuration file is provided in the `HADDOCK` directory within your home folder on DEVANA as `docking-antibody-antigen-CDR-NMR-CSP.cfg` (`docking-antibody-antigen-CDR-NMR-CSP-af2.cfg` and `docking-antibody-antigen-CDR-NMR-CSP-abb.cfg` for Alphafold2 and ABodyBuilder2 antibodies, respectively).
+
+ If you want to use your own pdb and restraint files please change the paths in the configuration files (for example from `pdbs/4G6K_clean.pdb` to `4G6K_abb_clean.pdb`).
+
+ If you have everything ready, we can submit our haddock3 run to the cluster.
+
+
+ sbatch run-haddock3.job
+
+
+
+
+## Analysis of docking results
+
+In case something went wrong with the docking (or simply if you don't want to wait for the results) you can find the following precalculated runs in the `runs` directory:
+- `run1-CDR-NMR-CSP`: run started using the unbound antibody
+- `run1-CDR-NMR-CSP-af2`: run started using the Alphafold-multimer antibody
+- `run1-CDR-NMR-CSP-abb`: run started using the Immunebuilder antibody
+
+### Structure of the run directory
+
+Once your run has completed inspect the content of the resulting directory. You will find the various steps (modules) of the defined workflow numbered sequentially, e.g.:
+
+{% highlight shell %}
+> ls run1-CDR-NMR-CSP/
+ 0_topoaa/
+ 1_rigidbody/
+ 2_caprieval/
+ 3_seletop/
+ 4_flexref/
+ 5_caprieval/
+ 6_emref/
+ 7_caprieval
+ 8_clustfcc/
+ 9_seletopclusts/
+ 10_caprieval/
+ analysis/
+ data/
+ log
+{% endhighlight %}
+
+There is in addition the log file (text file) and two additional directories:
+
+- the `data` directory containing the input data (PDB and restraint files) for the various modules
+- the `analysis` directory containing various plots to visualise the results for each `caprieval` step
+
+You can find information about the duration of the run at the bottom of the log file. Each sampling/refinement/selection module will contain PDB files.
+
+For example, the `09_seletopclusts` directory contains the selected models from each cluster. The clusters in that directory are numbered based
+on their rank, i.e. `cluster_1` refers to the top-ranked cluster. Information about the origin of these files can be found in that directory in the `seletopclusts.txt` file.
+
+The simplest way to extract ranking information and the corresponding HADDOCK scores is to look at the `10_caprieval` directories (which is why it is a good idea to have it as the final module, and possibly as intermediate steps). This directory will always contain a `capri_ss.tsv` file, which contains the model names, rankings and statistics (score, iRMSD, Fnat, lRMSD, ilRMSD and dockq score). E.g.:
+
+
+
+If clustering was performed prior to calling the `caprieval` module the `capri_ss.tsv` file will also contain information about to which cluster the model belongs to and its ranking within the cluster.
+
+The relevant statistics are:
+
+* **score**: *the HADDOCK score (arbitrary units)*
+* **irmsd**: *the interface RMSD, calculated over the interfaces the molecules*
+* **fnat**: *the fraction of native contacts*
+* **lrmsd**: *the ligand RMSD, calculated on the ligand after fitting on the receptor (1st component)*
+* **ilrmsd**: *the interface-ligand RMSD, calculated over the interface of the ligand after fitting on the interface of the receptor (more relevant for small ligands for example)*
+* **dockq**: *the DockQ score, which is a combination of irmsd, lrmsd and fnat and provides a continuous scale between 1 (exactly equal to reference) and 0*
+
+The iRMSD, lRMSD and Fnat metrics are the ones used in the blind protein-protein prediction experiment [CAPRI](https://capri.ebi.ac.uk/) (Critical PRediction of Interactions).
+
+In CAPRI the quality of a model is defined as (for protein-protein complexes):
+
+* **acceptable model**: i-RMSD < 4Å or l-RMSD<10Å and Fnat > 0.1 (0.23 < DOCKQ < 0.49)
+* **medium quality model**: i-RMSD < 2Å or l-RMSD<5Å and Fnat > 0.3 (0.49 < DOCKQ < 0.8)
+* **high quality model**: i-RMSD < 1Å or l-RMSD<1Å and Fnat > 0.5 (DOCKQ > 0.8)
+
+
+What is based on this CAPRI criterion the quality of the best model listed above (emref_6.pdb)?
+
+
+In case the `caprieval` module is called after a clustering step an additional file will be present in the directory: `capri_clt.tsv`.
+This file contains the cluster ranking and score statistics, averaged over the minimum number of models defined for clustering
+(4 by default), with their corresponding standard deviations. E.g.:
+
+
+
+
+In this file you find the cluster rank, the cluster ID (which is related to the size of the cluster, 1 being always the largest cluster), the number of models (n) in the cluster and the corresponding statistics (averages + standard deviations). The corresponding cluster PDB files will be found in the processing `09_seletopclusts` directory.
+
+
+
+### Analysis
+
+Let us now analyse the docking results. Use for that either your own run or a pre-calculated run provided in the `runs` directory.
+Go into the _analysis/10_caprieval_analysis_ directory of the respective run directory and
+
+Inspect the final cluster statistics in _capri_clt.tsv_ file
+
+
+
+View the pre-calculated 10_caprieval/capri_clt.tsv fileexpand_more
+
+
+==============================================
+== runs/run1-CDR-NMR-CSP/10_caprieval/capri_clt.tsv
+==============================================
+Total number of acceptable or better clusters: 1 out of 4
+Total number of medium or better clusters: 1 out of 4
+Total number of high quality clusters: 0 out of 4
+
+First acceptable cluster - rank: 1 i-RMSD: 1.413 Fnat: 0.759 DockQ: 0.726
+First medium cluster - rank: 1 i-RMSD: 1.413 Fnat: 0.759 DockQ: 0.726
+Best cluster - rank: 1 i-RMSD: 1.413 Fnat: 0.759 DockQ: 0.726
+
+==============================================
+== ./runs/run1-CDR-NMR-CSP/02_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models: 25 out of 96
+Total number of medium or better models: 15 out of 96
+Total number of high quality models: 0 out of 96
+
+First acceptable model - rank: 1 i-RMSD: 2.504 Fnat: 0.328 DockQ: 0.405
+First medium model - rank: 5 i-RMSD: 1.169 Fnat: 0.828 DockQ: 0.788
+Best model - rank: 13 i-RMSD: 1.013 Fnat: 0.672 DockQ: 0.735
+==============================================
+== ./runs/run1-CDR-NMR-CSP/05_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models: 18 out of 48
+Total number of medium or better models: 15 out of 48
+Total number of high quality models: 4 out of 48
+
+First acceptable model - rank: 1 i-RMSD: 1.107 Fnat: 0.810 DockQ: 0.805
+First medium model - rank: 1 i-RMSD: 1.107 Fnat: 0.810 DockQ: 0.805
+Best model - rank: 10 i-RMSD: 0.857 Fnat: 0.810 DockQ: 0.848
+==============================================
+== ./runs/run1-CDR-NMR-CSP/07_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models: 18 out of 48
+Total number of medium or better models: 15 out of 48
+Total number of high quality models: 5 out of 48
+
+First acceptable model - rank: 1 i-RMSD: 2.111 Fnat: 0.621 DockQ: 0.555
+First medium model - rank: 2 i-RMSD: 1.472 Fnat: 0.759 DockQ: 0.726
+Best model - rank: 6 i-RMSD: 0.921 Fnat: 0.914 DockQ: 0.866
+==============================================
+== ./runs/run1-CDR-NMR-CSP/10_caprieval/capri_ss.tsv
+==============================================
+Total number of acceptable or better models: 17 out of 41
+Total number of medium or better models: 15 out of 41
+Total number of high quality models: 5 out of 41
+
+First acceptable model - rank: 1 i-RMSD: 2.111 Fnat: 0.621 DockQ: 0.555
+First medium model - rank: 2 i-RMSD: 1.472 Fnat: 0.759 DockQ: 0.726
+Best model - rank: 6 i-RMSD: 0.921 Fnat: 0.914 DockQ: 0.866
+
+ In terms of iRMSD values we only observe very small differences in the best model. The fraction of native contacts and the DockQ scores are however improving much more after flexible refinement. All this will of course depend on how different are the bound and unbound conformations and the amount of data used to drive the docking process. In general, from our experience, the more and better data at hand, the larger the conformational changes that can be induced.
+
+ This is clearly not the case. The scoring function is not perfect, but does a reasonable job in ranking models of acceptable or better quality on top in this case.
+
+
+
+
+
+#### Visualizing the scores and their components
+
+By setting `postprocess=true` in the config files, interactive plots have been automatically generated in the _analysis_ directory of the run.
+These are useful to visualise the scores and their components versus ranks and model quality.
+
+
+Examine the plots (remember here that higher DockQ values and lower i-RMSD values correspond to better models)
+
+
+Models statistics:
+
+* [iRMSD versus HADDOCK score](plots/run1-CDR-NMR-CSP/irmsd_score.html){:target="_blank"}
+* [DockQ versus HADDOCK score](plots/run1-CDR-NMR-CSP/dockq_score.html){:target="_blank"}
+* [DockQ versus van der Waals energy](plots/run1-CDR-NMR-CSP/dockq_vdw.html){:target="_blank"}
+* [DockQ versus electrostatic energy](plots/run1-CDR-NMR-CSP/dockq_elec.html){:target="_blank"}
+* [DockQ versus ambiguous restraints energy](plots/run1-CDR-NMR-CSP/dockq_air.html){:target="_blank"}
+* [DockQ versus desolvation energy](plots/run1-CDR-NMR-CSP/dockq_desolv.html){:target="_blank"}
+
+Cluster statistics (distributions of values per cluster ordered according to their HADDOCK rank):
+
+* [HADDOCK scores](plots/run1-CDR-NMR-CSP/score_clt.html){:target="_blank"}
+* [van der Waals energies](plots/run1-CDR-NMR-CSP/vdw_clt.html){:target="_blank"}
+* [electrostatic energies](plots/run1-CDR-NMR-CSP/elec_clt.html){:target="_blank"}
+* [ambiguous restraints energies](plots/run1-CDR-NMR-CSP/air_clt.html){:target="_blank"}
+* [desolvation energies](plots/run1-CDR-NMR-CSP/desolv_clt.html){:target="_blank"}
+
+For this antibody-antigen case, which of the score component is correlating best with the quality of the models?.
+
+You can also access the full analysis report on your web browser:
+
+
+firefox HADDOCK/runs/run1-CDR-NMR-CSP/analysis/10_caprieval_analysis/report.html
+
+
+
+
+### Comparing the performance of the three antibodies
+
+All three antibody structures used in input give good results. The unbound and the ABodyBuilder2 antibodies provided better results, with the best cluster showing models within 1 angstrom of interface-RMSD with respect to the unbound structure. Using the Alphafold2 structure in this case is not the best option, as the input antibody is not perfectly modelled in its H3 loop.
+
+The good information about the paratope with the NMR epitope is critical for the good docking performance, which is also the scenario described in our Structure 2020 article:
+
+* F. Ambrosetti, B. Jiménez-García, J. Roel-Touris and A.M.J.J. Bonvin. [Modeling Antibody-Antigen Complexes by Information-Driven Docking](https://doi.org/10.1016/j.str.2019.10.011). _Structure_, *28*, 119-129 (2020). Preprint freely available from [here](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3362436).
+
+
+
+
+## Visualization of the models
+
+To visualize the models from top cluster of your favorite run, start PyMOL and load the cluster representatives you want to view, e.g. this could be the top model from cluster1 for run `run1-CDR-NMR-CSP`.
+These can be found in the `runs/run1-CDR-NMR-CSP/09_seletopclusts/` directory
+
+File menu -> Open -> select cluster_1_model_1.pdb
+
+If you want to get an impression of how well defined a cluster is, repeat this for the best N models you want to view (`cluster_1_model_X.pdb`).
+Also load the reference structure from the `pdbs` directory, `4G6M-matched.pdb`.
+
+Once all files have been loaded, type in the PyMOL command window:
+
+
+show cartoon
+
+
+util.cbc
+
+
+color yellow, 4G6M_matched
+
+
+Let us then superimpose all models on the reference structure:
+
+
+alignto 4G6M_matched
+
+
+
+How close are the top4 models to the reference? Did HADDOCK do a good job at ranking the best in the top?
+
+
+Let’s now check if the active residues which we have defined (the paratope and epitope) are actually part of the interface. In the PyMOL command window type:
+
+
+select paratope, (resi 31+32+33+34+35+52+54+55+56+100+101+102+103+104+105+106+151+152+169+170+173+211+212+213+214+216 and chain A)
+
+
+color red, paratope
+
+
+select epitope, (resi 72+73+74+75+81+83+84+89+90+92+94+96+97+98+115+116+117 and chain B)
+
+
+color orange, epitope
+
+
+
+Are the residues of the paratope and NMR epitope at the interface?
+
+
+**Note:** You can turn on and off a model by clicking on its name in the right panel of the PyMOL window.
+
+
+
+ See the overlay of the best model onto the reference structureexpand_more
+
+
Top4 models of the top cluster superimposed onto the reference crystal structure (in yellow)
+
+
+
+
+
+
+
+
+
+## BONUS: Does the antibody bind to a known interface of interleukin? ARCTIC-3D analysis
+
+Gevokizumab is a highly specific antibody that targets an allosteric site of Interleukin-1β (IL-1β) in PDB file *4G6M*, thus reducing its binding affinity for its substrate, interleukin-1 receptor type I (IL-1RI). Canakinumab, another antibody binding to IL-1β, has a different mode of action, as it competes directly with IL-1RI's binding site (in PDB file *4G6J*). For more details please check [this article](https://www.sciencedirect.com/science/article/abs/pii/S0022283612007863?via%3Dihub).
+
+We will now use our new software, [ARCTIC-3D](https://www.biorxiv.org/content/10.1101/2023.07.10.548477v1), to visualize the binding interfaces formed by IL-1β. First, the program retrieves all the existing binding surfaces formed by IL-1β from the [PDBe website](https://www.ebi.ac.uk/pdbe/). Then, these binding surfaces are compared and clustered together if they span a similar region of the selected protein (IL-1β in our case).
+
+We can now open the ARCTIC-3D web-server page [here](https://wenmr.science.uu.nl/arctic3d/). We will run an ARCTIC-3D job targeting the uniprot ID proper to human Interleukin-1 beta, namely [P01584](https://www.uniprot.org/uniprotkb/P01584/entry).
+
+
+Insert the selected uniprot ID in the **UniprotID** field.
+
+
+
+Leave the other parameters as they are and click on **Submit**.
+
+
+Wait a few seconds for the job to complete or access a precalculated run [here](https://wenmr.science.uu.nl/arctic3d/example-P01584).
+
+
+How many interface clusters were found for this protein?
+
+
+Once you download the output archive, you can find the clustering information presented in the dendrogram:
+
+
+
+
+
+We can see how the two *4G6M* antibody chains are recognized as a unique cluster, clearly separated from the other binding surfaces and, in particular, from those proper to IL-1RI (uniprot ID P14778).
+
+
+Rerun ARCTIC-3D with a clustering threshold equal to 0.95
+
+
+This means that the clustering will be looser, therefore lumping more dissimilar surfaces into the same object.
+
+You can inspect a precalculated run [here](https://wenmr.science.uu.nl/arctic3d/example-P01584-095).
+
+
+How do the results change? Are gevokizumab or canakinumab PDB files being clustered with any IL-1RI-related interface?
+
+
+
+
+
+## BONUS: Alphafold2 for antibody-antigen complex structure prediction
+
+With the advent of Artificial Intelligence (AI) and AlphaFold we can also try to predict with AlphaFold this antibody-antigen complex.
+
+To predict our complex, we are going to use the _AlphaFold2_mmseq2_ Jupyter notebook which can be found with other interesting notebooks in Sergey Ovchinnikov's [ColabFold GitHub repository](https://github.com/sokrypton/ColabFold){:target="_blank"}, making use of the Google Colab CLOUD resources.
+
+Start the AlphaFold2 notebook on Colab by clicking [here](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb){:target="_blank"}.
+
+**Note**: The bottom part of the notebook contains instructions on how to use it.
+
+
+
+### Setting up the antibody-antigen complex prediction with AlphaFold2
+
+To setup the prediction we need to provide the sequence of the heavy and light chains of the antibody and the sequence of the antigen.
+These are respectively
+
+* Antibody heavy chain:
+
+
+
+To use AlphaFold2 to predict e.g. the pentamer follow the following steps:
+
+
+Copy and paste each of the above sequence in the _query_sequence_ field, adding a colon *:* in between the sequences.
+
+
+
+Define the _jobname_, e.g. Ab_Ag
+
+
+
+In the _Advanced settings_ block you can check the option to save the results to your Google Drive (if you have an account)
+
+
+
+In the top section of the Colab, click: _Runtime > Run All_
+
+
+(It may give a warning that this is not authored by Google, because it is pulling code from GitHub). This will automatically install, configure and run AlphaFold for you - leave this window open. After the prediction complete you will be asked to download a zip-archive with the results (if you configured it to use Google Drive, a result archive will be automatically saved to your Google Drive).
+
+
+_Time to grap a cup of tea or a coffee!
+And while waiting try to answer the following questions:_
+
+
+ How do you interpret AlphaFold's predictions? What are the predicted LDDT (pLDDT), PAE, iptm?
+
+
+_Tip_: Try to find information about the prediction confidence at [https://alphafold.ebi.ac.uk/faq](https://alphafold.ebi.ac.uk/faq){:target="\_blank"}. A nice summary can also be found [here](https://www.rbvi.ucsf.edu/chimerax/data/pae-apr2022/pae.html){:target="\_blank"}.
+
+
+Pre-calculated AlphFold2 predictions are provided [here](abagtest_2d03e.result.zip){:target="\_blank"}. This archive contains the five predicted models (the naming indicates the rank), figures (png) files (PAE, pLDDT, coverage) and json files containing the corresponding values (the last part of the json files report the ptm and iptm values).
+
+
+
+### Analysis of the generated AF2 models
+
+While the notebook is running models will appear first under the `Run Prediction` section, colored both by chain and by pLDDT.
+
+The best model will then be displayed under the `Display 3D structure` section. This is an interactive 3D viewer that allows you to rotate the molecule and zoom in or out.
+
+**Note** that you can change the model displayed with the _rank_num_ option. After changing it execute the cell by clicking on the run cell icon on the left of it.
+
+
+ How similar are the five models generated by AF2?
+
+
+Consider the pLDDT of the various models (the higher the pLDDT the more reliable the model).
+
+
+ What is the confidence of those predictions? (check again the FAQ page of the Alphafold database provided above for pLDDT values)
+
+
+While the pLDDT score is an overall measure, you can also focus on the interface score reported in the `iptm` score (value between 0 and 1).
+
+
+
+
+
+ See the confidence statistics for the five generated models
+
+
+
+
+
+
+
+
+ Based on the iptm scores, would you qualify those models as reliable?
+
+
+**Note** that in this case the iptm score reports on all interfaces, i.e. both the interface between the two chains of the antibody, and the antibody-antigen interface
+
+Another useful way of looking at the model accuracy is to check the Predicted Alignment Error plots (PAE) (also referred to as Domain position confidence).
+The PAE gives a distance error for every pair of residues: It gives AlphaFold's estimate of position error at residue x when the predicted and true structures are aligned on residue y.
+Values range from 0 to 35 Angstroms. It is usually shown as a heatmap image with residue numbers running along vertical and horizontal axes and each pixel colored according to the PAE value for the corresponding pair of residues. If the relative position of two domains is confidently predicted then the PAE values will be low (less than 5A - dark blue) for pairs of residues with one residue in each domain. When analysing your complex, the diagonal block will indicate the PAE within each molecule/domain, while the off-diagonal blocks report on the accuracy of the domain-domain placement.
+
+
+Our antibody-antigen complex consists of three interfaces:
+
+* The interface between the heavy and light chains of the antibody
+* The interface between the heavy chain of the antibody and the antigen
+* The interface between the light chain of the antibody and the antigen
+
+
+
+
+ See the PAE plots for the five generated models
+
+
+
+
+
+
+
+
+
+ Based on the PAE plots, which interfaces can be considered reliable/unreliable?
+
+
+
+
+
+### Visualization of the generated AF2 models
+
+Let's now visualize the models in PyMOL. For this save your predictions to disk or download the precalculated AlphaFold2 model from [here](abagtest_2d03e.result.zip){:target="\_blank"}.
+
+Start PyMOL and load via the File menu all five AF2 models.
+
+File menu -> Open -> select abagtest_2d03e_unrelaxed_rank_001_alphafold2_multimer_v3_model_3_seed_000.pdb
+
+Repeat this for each model (`abagtest_2d03e_unrelaxed_rank_X_alphafold2_multimer_v3_model_X_seed_000.pdb` or whatever the naming of your model is).
+
+Let's superimpose all models on the antibody (the antibody in the provided AF2 models correspond to chains A and B):
+
+
+util.cbc
+select Ab_Ag_unrelaxed_rank_1_model_2 and chain A+B
+alignto sele
+
+
+This will align all clusters on the antibody, maximizing the differences in the orientation of the antigen.
+
+
+Examine the various models. How does the orientation of the antigen differ between them?
+
+
+**Note:** You can turn on and off a model by clicking on its name in the right panel of the PyMOL window.
+
+
+
+
+ See tips on how to visualize the prediction confidence in PyMOL
+
+
+ When looking at the structures generated by AlphaFold in PyMOL, the pLDDT is encoded as the B-factor.
+ To color the model according to the pLDDT type in PyMOL:
+
+
+ spectrum b
+
+
+ **Note** that the scale in the B-factor field is the inverse of the color coding in the PAE plots: i.e. red mean reliable (high pLDDT) and blue unreliable (low pLDDT))
+
+
+
+Since we do have NMR chemical shift perturbation data for the antigen, let's check if the perturbed residues are at the interface in the AF2 models.
+Note that there is a shift in numbering of 2 residues between the AF2 and the HADDOCK models.
+
+
+util.cbc
+select epitope, (resi 70,71,72,73,81,82,87,88,90,92,94,95,96,113,114,115) and chain C
+color orange, epitope
+
+
+
+Does any model have the NMR-identified epitope at the interface with the antibody?
+
+
+
+
+
+
+ See the AlphaFold models with the NMR-mapped epitope
+
+
+
+
+
+
+
+
+
+It should be clear from the visualization of the NMR-mapped epitope on the AF2 models that none does satisfy the NMR data.
+To further make that clear we can compare the models to the crystal structure of the complex (4G6M).
+
+For this download and superimpose the models onto the crystal structure in PyMOL with the following commands:
+
+
+fetch 4G6M
+remove resn HOH
+color yellow, 4G6M
+select 4G6M and chain H+L
+alignto sele
+
+
+
+
+
+ See the AlphaFold models superimposed onto the crystal structure of the complex (4G6M)
+
+
+
+
+
+
+
+
+
+
+
+## Conclusions
+
+We have demonstrated the usage of HADDOCK3 in an antibody-antigen docking scenario making use of the paratope information on the antibody side (i.e. no prior experimental information) and a NMR-mapped epitope for the antigen. Compared to the static
+HADDOCK2.X workflow, the modularity and flexibility of HADDOCK3 allows to customise the docking protocols and to run a deeper analysis of the results.
+While HADDOCK3 is still very much work in progress, its intrinsic flexibility can be used to improve the performance of antibody-antigen modelling compared to the results we presented in our
+[Structure 2020](https://doi.org/10.1016/j.str.2019.10.011){:target="_blank"} article and in the [related HADDOCK2.4 tutorial](/education/HADDOCK24/HADDOCK24-antibody-antigen){:target="_blank"}.
+
+
+
+
+## Congratulations! 🎉
+
+You have completed this tutorial. If you have any questions or suggestions, feel free to contact us via email or asking a question through our [support center](https://ask.bioexcel.eu){:target="_blank"}.
+
+And check also our [education](/education) web page where you will find more tutorials!
+
+
+
+
+
+[air-help]: https://www.bonvinlab.org/software/haddock2.4/airs/ "AIRs help"
+[gentbl]: https://wenmr.science.uu.nl/gentbl/ "GenTBL"
+[haddock24protein]: /education/HADDOCK24/HADDOCK24-protein-protein-basic/
+[haddock-repo]: https://github.com/haddocking/haddock3 "HADDOCK3 GitHub"
+[haddock-tools]: https://github.com/haddocking/haddock-tools "HADDOCK tools GitHub"
+[installation]: https://www.bonvinlab.org/haddock3/INSTALL.html "Installation"
+[link-cns]: https://cns-online.org "CNS online"
+[link-forum]: https://ask.bioexcel.eu/c/haddock "HADDOCK Forum"
+[link-freesasa]: https://freesasa.github.io "FreeSASA"
+[link-pdbtools]:http://www.bonvinlab.org/pdb-tools/ "PDB-Tools"
+[link-pymol]: https://www.pymol.org/ "PyMOL"
+[nat-pro]: https://www.nature.com/nprot/journal/v5/n5/abs/nprot.2010.32.html "Nature protocol"
+[tbl-examples]: https://github.com/haddocking/haddock-tools/tree/master/haddock_tbl_validation "tbl examples"
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* [**HADDOCK3 antibody-antigen docking**](/education/HADDOCK3/HADDOCK3-antibody-antigen):
This tutorial demonstrates the use of HADDOCK3 for predicting the structure of an antibody-antigen complex using information
about the hypervariable loops of the antibody and either the entire surface of the antigen or a loose definition of the epitope.
- It illustrate the modularity of HADDOCK3 by introducing a new workflow not possible under the current HADDOCK2.X versions.
+ It illustrates the modularity of HADDOCK3 by introducing a new workflow not possible under the current HADDOCK2.X versions.
As HADDOCK3 only exists as a command line version, this tutorial does require some basic Linux expertise.
-* [**HADDOCK3 antibody-antigen docking for bioexcel 2023 workshop**](/education/HADDOCK3/HADDOCK3-antibody-antigen-bioexcel2023):
+* [**HADDOCK3 antibody-antigen docking for bioexcel 2024 workshop**](/education/HADDOCK3/HADDOCK3-antibody-antigen-bioexcel2024):
This tutorial demonstrates the use of HADDOCK3 for predicting the structure of an antibody-antigen complex using information
about the hypervariable loops of the antibody and a loose definition of the epitope determined through NMR experiments.
As HADDOCK3 only exists as a command line version, this tutorial does require some basic Linux expertise.
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