Refine colab notebook (#52)

* refine notebook * rephrase * rephrase * rephrase * rephrase * rm output * rephrase * rephrase Co-authored-by: Guolin Ke <[email protected]>
dptech-corp · Sep 22, 2022 · 0220958 · 0220958
1 parent 1624ef2
commit 0220958
Showing 1 changed file with 15 additions and 15 deletions.
diff --git a/notebooks/unifold.ipynb b/notebooks/unifold.ipynb
@@ -6,13 +6,11 @@
         "id": "jMGcXXPabEN4"
       },
       "source": [
-        "# Uni-Fold Colab\n",
+        "# Uni-Fold Notebook\n",
         "\n",
-        "This Colab notebook provides an online runnable version of [Uni-Fold](https://github.com/dptech-corp/Uni-Fold/) for users to predict the structure of a protein, single chain or multimer, with custom settings.\n",
+        "This notebook provides protein structure prediction service of [Uni-Fold](https://github.com/dptech-corp/Uni-Fold/) as well as [UF-Symmetry](https://www.biorxiv.org/content/10.1101/2022.08.30.505833v1). Predictions of both protein monomers and multimers are supported. The homology search process in this notebook is enabled with the [MMSeqs2](https://github.com/soedinglab/MMseqs2.git) server provided by [ColabFold](https://github.com/sokrypton/ColabFold). For more consistent results with the original AlphaFold(-Multimer), please refer to the open-source repository of [Uni-Fold](https://github.com/dptech-corp/Uni-Fold/), or our convenient web server at [Hermite™](https://hermite.dp.tech/).\n",
         "\n",
-        "Thanks to  [MMSeqs2](https://github.com/soedinglab/MMseqs2.git) and the server provided by [ColabFold](https://github.com/sokrypton/ColabFold), the homogeneous searching in this notebook is very fast and is comparable with the original AlphaFold(-Multimer). If you want more consistent results with the original AlphaFold(-Multimer), you can use the [full open source Uni-Fold](https://github.com/dptech-corp/Uni-Fold/), or the convenient web server at [Hermite™](https://hermite.dp.tech/).\n",
-        "\n",
-        "Please note that this Colab notebook is not a finished product and is provided as an early-access prototype. It is provided for theoretical modeling only and caution should be exercised in its use. \n",
+        "Please note that this notebook is provided as an early-access prototype, and is NOT an official product of DP Technology. It is provided for theoretical modeling only and caution should be exercised in its use. \n",
         "\n",
         "**Licenses**\n",
         "\n",
@@ -23,16 +21,15 @@
         "\n",
         "Please cite the following papers if you use this notebook:\n",
         "\n",
-        "*   Jumper et al. \"[Highly accurate protein structure prediction with AlphaFold.](https://doi.org/10.1038/s41586-021-03819-2)\" Nature (2021)\n",
-        "*   Evans et al. \"[Protein complex prediction with AlphaFold-Multimer.](https://www.biorxiv.org/content/10.1101/2021.10.04.463034v1)\" biorxiv (2021)\n",
-        "*   Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S and Steinegger M. \"[ColabFold: Making protein folding accessible to all.](https://www.nature.com/articles/s41592-022-01488-1)\" Nature Methods (2022) \n",
         "*   Ziyao Li, Xuyang Liu, Weijie Chen, Fan Shen, Hangrui Bi, Guolin Ke, Linfeng Zhang. \"[Uni-Fold: An Open-Source Platform for Developing Protein Folding Models beyond AlphaFold.](https://www.biorxiv.org/content/10.1101/2022.08.04.502811v1)\" biorxiv (2022)\n",
         "*   Ziyao Li, Shuwen Yang, Xuyang Liu, Weijie Chen, Han Wen, Fan Shen, Guolin Ke, Linfeng Zhang. \"[Uni-Fold Symmetry: Harnessing Symmetry in Folding Large Protein Complexes.](https://www.biorxiv.org/content/10.1101/2022.08.30.505833v1)\" bioRxiv (2022)\n",
-        "\n",
+        "*   Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S and Steinegger M. \"[ColabFold: Making protein folding accessible to all.](https://www.nature.com/articles/s41592-022-01488-1)\" Nature Methods (2022)\n",
         "\n",
         "**Acknowledgements**\n",
         "\n",
-        "We thank [@sokrypton](https://twitter.com/sokrypton) for many helpful suggestions to this notebook.\n"
+        "The model architecture of Uni-Fold is largely based on [AlphaFold](https://doi.org/10.1038/s41586-021-03819-2) and [AlphaFold-Multimer](https://www.biorxiv.org/content/10.1101/2021.10.04.463034v1). The design of this notebook refers directly to [ColabFold](https://www.nature.com/articles/s41592-022-01488-1). We specially thank [@sokrypton](https://twitter.com/sokrypton) for his helpful suggestions to this notebook.\n",
+        "\n",
+        "Copyright © 2022 DP Technology. All rights reserved."
       ]
     },
     {
@@ -127,7 +124,6 @@
         "output_dir_base = \"./prediction\"\n",
         "os.makedirs(output_dir_base, exist_ok=True)\n",
         "\n",
-        "\n",
         "def clean_and_validate_sequence(\n",
         "    input_sequence: str, min_length: int, max_length: int) -> str:\n",
         "  \"\"\"Checks that the input sequence is ok and returns a clean version of it.\"\"\"\n",
@@ -203,21 +199,25 @@
         "def add_hash(x,y):\n",
         "    return x+\"_\"+hashlib.sha1(y.encode()).hexdigest()[:5]\n",
         "\n",
+        "jobname = 'unifold_colab' #@param {type:\"string\"}\n",
         "\n",
         "sequence_1 = 'LILNLRGGAFVSNTQITMADKQKKFINEIQEGDLVRSYSITDETFQQNAVTSIVKHEADQLCQINFGKQHVVCTVNHRFYDPESKLWKSVCPHPGSGISFLKKYDYLLSEEGEKLQITEIKTFTTKQPVFIYHIQVENNHNFFANGVLAHAMQVSI'  #@param {type:\"string\"}\n",
         "sequence_2 = ''  #@param {type:\"string\"}\n",
         "sequence_3 = ''  #@param {type:\"string\"}\n",
         "sequence_4 = ''  #@param {type:\"string\"}\n",
         "\n",
+        "#@markdown Use symmetry group `C1` for default Uni-Fold predictions.\n",
+        "#@markdown Or, specify a **cyclic** symmetry group (e.g. `C4``) and\n",
+        "#@markdown the sequences of the asymmetric unit (i.e. **do not copy\n",
+        "#@markdown them multiple times**) to predict with UF-Symmetry.\n",
+        "\n",
         "symmetry_group = 'C1' #@param {type:\"string\"}\n",
         "\n",
         "use_templates = True #@param {type:\"boolean\"}\n",
         "msa_mode = \"MMseqs2\" #@param [\"MMseqs2\",\"single_sequence\"]\n",
         "\n",
         "input_sequences = [sequence_1, sequence_2, sequence_3, sequence_4]\n",
         "\n",
-        "jobname = 'unifold_colab' #@param {type:\"string\"}\n",
-        "\n",
         "basejobname = \"\".join(input_sequences)\n",
         "basejobname = re.sub(r'\\W+', '', basejobname)\n",
         "target_id = add_hash(jobname, basejobname)\n",
@@ -1046,7 +1046,7 @@
     },
     "gpuClass": "standard",
     "kernelspec": {
-      "display_name": "Python 3.8.10 ('ProteinMD')",
+      "display_name": "Python 3.8.10 64-bit",
       "language": "python",
       "name": "python3"
     },
@@ -1056,7 +1056,7 @@
     },
     "vscode": {
       "interpreter": {
-        "hash": "af92dc656850d97b5469b75c9ef2009aaa936e713f0093b069a7ff14eeb2ca8d"
+        "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
       }
     }
   },