What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning?

Ernest Y. Lee, Michelle W. Lee, Benjamin M. Fulan, Andrew L. Ferguson, Gerard C.L. Wong

Research output: Contribution to journalReview article

Abstract

Antimicrobial peptides (AMPs) are a diverse class of well-studied membranepermeating peptides with important functions in innate host defense. In this short review, we provide a historical overview of AMPs, summarize previous applications of machine learning to AMPs, and discuss the results of our studies in the context of the latest AMP literature. Much work has been recently done in leveraging computational tools to design new AMP candidates with high therapeutic efficacies for drug-resistant infections. We show that machine learning on AMPs can be used to identify essential physicochemical determinants of AMP functionality, and identify and design peptide sequences to generate membrane curvature. In a broader scope, we discuss the implications of our findings for the discovery of membrane-active peptides in general, and uncovering membrane activity in new and existing peptide taxonomies.

Original languageEnglish (US)
Article number20160153
JournalInterface Focus
Volume7
Issue number6
DOIs
StatePublished - Dec 6 2017

Keywords

  • Amphiphilic peptides
  • Antimicrobial peptides
  • Machine learning
  • Membrane curvature

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Bioengineering
  • Biochemistry
  • Biomaterials
  • Biomedical Engineering

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