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Interface Focus. 2017 Dec 6;7(6):20160153. doi: 10.1098/rsfs.2016.0153. Epub 2017 Oct 20.

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

Author information

1
Department of Bioengineering, University of California, Los Angeles, CA 90095, USA.
2
Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
3
Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
4
Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Abstract

Antimicrobial peptides (AMPs) are a diverse class of well-studied membrane-permeating 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 physico-chemical 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.

KEYWORDS:

amphiphilic peptides; antimicrobial peptides; machine learning; membrane curvature

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