Prediction Of Therapeutic Peptides Based On Machine Learning And Feature Representation Learning

Prediction of therapeutic peptides is critical for the discovery of novel and efficient peptidebased therapeutics.Computational methods,especially machine learning based methods,have been developed for addressing this need.However,most of existing methods are peptide-specific;currently,there is no generic predictor for multiple peptide types.Moreover,it is still challenging to extract informative feature representations from the perspective of primary sequences.In this study,we have developed PEPred-Suite,a bioinformatics tool for the generic prediction of therapeutic peptides.In PEPred-Suite,we introduce an adaptive feature representation strategy that can learn the most representative features for different peptide types.To be specific,we train diverse sequence-based feature descriptors,integrate the learnt class information into our features,and utilize a two-step feature optimization strategy based on the area under receiver operating characteristic curve to extract the most discriminative features.Using the learnt representative features,we trained eight Random Forest models for eight different types of functional peptides,respectively.Benchmarking results showed that as compared with existing predictors,PEPred-Suite achieves better and robust performance for different peptides.As far as we know,PEPred-Suite is currently the first tool that is capable of predicting so many peptide types simultaneously.In addition,our work demonstrates that the learnt features can reliably predict different peptides.For convenience of researchers,we have established a user-friendly webserver that implements our predictor.In this webserver,there are two modules: prediction module,and feature representation module.