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A machine Learning-based Approach for Peptide function Prediction

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PEPred: A machine Learning-based Approach for Peptide function Prediction

This is a Peptide function prediction tool based on a machine learning approach that relies mainly on non-covalent interaction data of single chain protein in a protein complex.

GitHub Repository for https://github.com/greenGM/PEPred

Repository

Dependencies:

Name version
R 4.3.3 or higher
tidyverse 2.0.0 or higher
caret 6.0-94 or higher

data genereation

  1. The descriptors were generated by using the following programs:

    Propy3 (version 1.1.1, https://pypi.org/project/propy3/) (18 descriptors) Peptides (version 0.3.2, https://pypi.org/project/peptides/) (132 descriptors)

  2. All descriptors must be generated and follow the order in descriptors explanation.csv.

How to use this tool:

  1. Download all files.

  2. Open in Rstudio or R.

  3. Enter the following code:

    R

    load(".RData")

    library(tidyverse)

    library(caret)

    unknown <- read.csv("yourfile.csv",header = F,col.names = name)#Import your data; better without column name, if you have, please set "header = T".

    (Note: the first column of your data should start from the fi, not be the protein's name.)

    unknownS <- predict(preprocessParams, unknown)

    unknownprediction <- data.frame(name=unknown$youproteinname, #the nameor the sequence infor of your peptide.

                               preclass=predict(fit.C50.Tsmote,unknownS),
    
                               preprob=predict(fit.C50.Tsmote,unknownS,type = 'prob' ))
    

    write.csv(unknownprediction,'PEPred.csv')

    (Note: Please adjust the above code to suit your needs.)

Result explanation:

The result will be returned as a csv file.

 name--the name of the candidate nanobody.

 preclass-- Final predicted function.

 preprob.function--the probability of the predicted function. 

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