HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks.

Abstract:

:The growing prevalence of resistance to antibiotics motivates the search for new antibacterial agents. Antimicrobial peptides are a diverse class of well-studied membrane-active peptides which function as part of the innate host defence system, and form a promising avenue in antibiotic drug research. Some antimicrobial peptides exhibit toxicity against eukaryotic membranes, typically characterised by hemolytic activity assays, but currently, the understanding of what differentiates hemolytic and non-hemolytic peptides is limited. This study leverages advances in machine learning research to produce a novel artificial neural network classifier for the prediction of hemolytic activity from a peptide's primary sequence. The classifier achieves best-in-class performance, with cross-validated accuracy of [Formula: see text] and Matthews correlation coefficient of 0.71. This innovative classifier is available as a web server at https://research.timmons.eu/happenn , allowing the research community to utilise it for in silico screening of peptide drug candidates for high therapeutic efficacies.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Timmons PB,Hewage CM

doi

10.1038/s41598-020-67701-3

subject

Has Abstract

pub_date

2020-07-02 00:00:00

pages

10869

issue

1

issn

2045-2322

pii

10.1038/s41598-020-67701-3

journal_volume

10

pub_type

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