HemoNet: Predicting Hemolytic Activity with Integrated Feature Learning A neural network that perdict hemolytic activity classification of a peptide sequence along with inforamtion of N/C terminal modifications.This network captures context of amino acid with SeqVec embedding, Circular Fingerprint repersatation of N/C terminal modification's SMILES.
Quantifying the hemolytic activity of peptides is a crucial step in the discovery of novel therapeutic peptides. Computational methods are attractive in this domain due to their ability to guide wet-lab experimental discovery or screening of peptides based on their hemolytic activity. However, existing methods are unable to accurately model various important aspects of this predictive problem such as the role of N/C terminal modifications, D- and L- amino acids, etc. In this work we have developed a novel neural network-based approach called HemoNet for predicting the hemolytic activity of peptides. The proposed method captures the contextual importance of different amino acids in a given peptide sequence using a specialized feature embedding in conjunction with SMILES-based fingerprint representation of N/C terminal modifications. We have analyzed the predictive performance of the proposed method using stratified cross-validation for comparison with previous methods, non-redundant cross-validation as well as validation on external peptides and clinical antimicrobial peptides. Our analysis shows the proposed approach achieves significantly better predictive performance (AUC-ROC of 88%) in comparison to previous approaches (HemoPI and HemoPred with AUC-ROC of 73%). HemoNet can be a useful and much-needed tool in the search for novel therapeutic peptides. The python implementation of the proposed method is available at the URL: https://github.com/adibayaseen/HemoNet Keywords: Hemolytic activity prediction, peptide toxicity classification, Antimicrobial activity, Machine learning guided drug discovery
python=3.6
conda install -c conda-forge rdkit
Dataset can be downloaded from this link [https://github.com/adibayaseen/HemoNet/blob/main/Datasets.docx]
Seprate Hemolytic examples can be downloaded from here https://github.com/adibayaseen/HemoNet/blob/main/hemo_All_seq.txt
Non-hemolytic examples can be downloaded from here https://github.com/adibayaseen/HemoNet/blob/main/Nonhemo_All_seq.txt
External dataset from here https://github.com/adibayaseen/HemoNet/blob/b1291e5b378d1f11e9cf0dad407dc36fef26e806/HemolyticExternalwithNCmodification.txt
Clinical dataset from here https://github.com/adibayaseen/HemoNet/blob/0052f9f33e49db889d0c128a0e4a1453ae1da78e/DRAMP_Clinical_data.txt
weights.hdf file have link of google drive in which neural network weights of SeqVec features are stored
5-foldResultsComparison.py is used for 5-fold comparison with existing models and basline results.
HemoNet10RunsResults( 5-fold and Non-redundant) is used for HemoNet's all methods(5-fold and Non-redundant Cross-Validation Analysis)
UMAP.py file is used for visulaization of the data
Clusterify.py used for making non-redendend clusters
Final_Baseline_blaster_NN_5fold_cv_from_output_files.py used for Baseline Blast search
Results.py used for result in multiple runs(10)
Input File format
Id_Nterminal_CTerminal
sequence
Sample External data
Genearate_Prediction used for prediction of test file in given format