期刊
INTERFACE FOCUS
卷 7, 期 6, 页码 -出版社
ROYAL SOC
DOI: 10.1098/rsfs.2016.0153
关键词
machine learning; antimicrobial peptides; membrane curvature; amphiphilic peptides
类别
资金
- T32 Systems and Integrative Biology Training Grant at University of California, Los Angeles (UCLA) [T32GM008185]
- T32 Medical Scientist Training Program at UCLA [T32GM008042]
- National Science Foundation (NSF) [DMS 1345032]
- NIH [1R21AI122212]
- US DOE Office of Basic Energy Sciences [DE-AC02-76SF00515]
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 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.
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