A Method for Predicting Hemolytic Potency of Chemically Modified Peptides From Its Structure

Frontiers in Pharmacology
Vinod KumarGajendra P S Raghava

Abstract

In the present study, a systematic effort has been made to predict the hemolytic potency of chemically modified peptides. All models have been trained, tested, and evaluated on a dataset that contains 583 modified hemolytic peptides and a balanced number of non-hemolytic peptides. Machine learning techniques have been used to build the classification models using an immense range of peptide features that include 2D, 3D descriptors, fingerprints, atom, and diatom compositions. Random Forest based model developed using fingerprints as an input feature achieved maximum accuracy of 78.33% with AUC of 0.86 on the main dataset and accuracy of 78.29% with AUC of 0.85 on the validation dataset. Models developed in this study have been incorporated in a web server "HemoPImod" to facilitate the scientific community (http://webs.iiitd.edu.in/raghava/hemopimod/).

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Citations

Jul 4, 2020·Scientific Reports·Patrick Brendan Timmons, Chandralal M Hewage
Feb 10, 2021·Advanced Drug Delivery Reviews·Woochang HwangNamshik Han
Nov 3, 2020·Frontiers in Immunology·Salman Sadullah Usmani, Gajendra P S Raghava
Aug 7, 2021·Journal of Bioinformatics and Computational Biology·Adiba YaseenFayyaz Minhas

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Methods Mentioned

BETA
lipidation
acetylation
amidation

Software Mentioned

PEPstrMod
CfsSubsetEval
AntiMPmod
Extratree
pROC
ML
MOD
Scikit
HemoPImod
GPSRdocker

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