A Web Server and Mobile App for Computing Hemolytic Potency of Peptides

Overview

Journal Sci Rep

Specialty Science

Date 2016 Mar 9

PMID 26953092

Citations 83

Authors

Kumardeep Chaudhary

Ritesh Kumar

Sandeep Singh

Abhishek Tuknait

Ankur Gautam

Deepika Mathur

Priya Anand

Grish C Varshney

Gajendra P S Raghava

Affiliations

Soon will be listed here.

Abstract

Numerous therapeutic peptides do not enter the clinical trials just because of their high hemolytic activity. Recently, we developed a database, Hemolytik, for maintaining experimentally validated hemolytic and non-hemolytic peptides. The present study describes a web server and mobile app developed for predicting, and screening of peptides having hemolytic potency. Firstly, we generated a dataset HemoPI-1 that contains 552 hemolytic peptides extracted from Hemolytik database and 552 random non-hemolytic peptides (from Swiss-Prot). The sequence analysis of these peptides revealed that certain residues (e.g., L, K, F, W) and motifs (e.g., "FKK", "LKL", "KKLL", "KWK", "VLK", "CYCR", "CRR", "RFC", "RRR", "LKKL") are more abundant in hemolytic peptides. Therefore, we developed models for discriminating hemolytic and non-hemolytic peptides using various machine learning techniques and achieved more than 95% accuracy. We also developed models for discriminating peptides having high and low hemolytic potential on different datasets called HemoPI-2 and HemoPI-3. In order to serve the scientific community, we developed a web server, mobile app and JAVA-based standalone software (http://crdd.osdd.net/raghava/hemopi/).

Citing Articles

Designing, Synthesis and In Vitro Antimicrobial Activity of Peptide Against Biofilm Forming Methicillin Resistant Staphylococcus aureus.

Kaur J, Lather A, Cheema P, Jangir B, Manoj J, Singh M Curr Microbiol. 2025; 82(4):159.

PMID: 40014067 DOI: 10.1007/s00284-025-04132-1.

Deciphering optimal molecular determinants of non-hemolytic, cell-penetrating antimicrobial peptides through bioinformatics and Random Forest.

Kumar A, Chadha S, Sharma M, Kumar M Brief Bioinform. 2025; 26(1).

PMID: 39973083 PMC: 11839508. DOI: 10.1093/bib/bbaf049.

Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics.

Barroso R, Aguero-Chapin G, Sousa R, Marrero-Ponce Y, Antunes A Molecules. 2025; 30(3).

PMID: 39942653 PMC: 11820242. DOI: 10.3390/molecules30030550.

Prediction of hemolytic peptides and their hemolytic concentration.

Rathore A, Kumar N, Choudhury S, Mehta N, Raghava G Commun Biol. 2025; 8(1):176.

PMID: 39905233 PMC: 11794569. DOI: 10.1038/s42003-025-07615-w.

Machine learning for antimicrobial peptide identification and design.

Wan F, Wong F, Collins J, de la Fuente-Nunez C Nat Rev Bioeng. 2025; 2(5):392-407.

PMID: 39850516 PMC: 11756916. DOI: 10.1038/s44222-024-00152-x.

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