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A QSAR modeling approach for predicting myeloid antimicrobial peptides with high sequence similarity
Author(s) -
Waghu Faiza Hanif,
Gawde Ulka,
Gomatam Anish,
Coutinho Evans,
IdiculaThomas Susan
Publication year - 2020
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.13749
Subject(s) - quantitative structure–activity relationship , antimicrobial , computational biology , antimicrobial peptides , similarity (geometry) , peptide , sequence (biology) , escherichia coli , chemistry , combinatorial chemistry , biological system , biology , biochemistry , computer science , artificial intelligence , stereochemistry , microbiology and biotechnology , gene , image (mathematics)
Microbial resistance to conventional antibiotics has led to a surge in antimicrobial peptide (AMP) rational design initiatives that rely heavily on algorithms with good prediction accuracy and sensitivity. We present a quantitative structure‐activity relationship (QSAR) approach for predicting activity of cathelicidins, an AMP family with broad‐spectrum activity. The best multiple linear regression model built against Escherichia coli ATCC 25922 could accurately predict activity of three rationally designed peptides CP, DP, and Mapcon, having high sequence similarity. On further experimental validation of the rationally designed peptides, CP was found to exhibit high antimicrobial activity with negligible hemolysis. Here, we provide CP, an AMP with potential therapeutic applications and a family‐based QSAR model for AMP prediction.