期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2016
卷号：113
期号：48
页码：13588-13593
DOI：10.1073/pnas.1609893113
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：SignificanceWe use machine learning on membrane-permeating -helical host defense peptides to study the nature of their functional commonality and sequence homology. Machine learning is combined with calibrating experiments to show that the metric in our support vector machine model correlates not with antimicrobial activity but with a peptides ability to generate the negative Gaussian membrane curvature necessary for membrane permeation. Moreover, we use the classifier reflexively to map the undiscovered sequence space of antimicrobial peptides and identify taxonomies of peptides with similar topological membrane remodeling activity, including endogenous neuropeptides, viral fusion proteins, topogenic peptides, and amyloids. There are some [~]1,100 known antimicrobial peptides (AMPs), which permeabilize microbial membranes but have diverse sequences. Here, we develop a support vector machine (SVM)-based classifier to investigate -helical AMPs and the interrelated nature of their functional commonality and sequence homology. SVM is used to search the undiscovered peptide sequence space and identify Pareto-optimal candidates that simultaneously maximize the distance{sigma} from the SVM hyperplane (thus maximize its "antimicrobialness") and its -helicity, but minimize mutational distance to known AMPs. By calibrating SVM machine learning results with killing assays and small-angle X-ray scattering (SAXS), we find that the SVM metric{sigma} correlates not with a peptides minimum inhibitory concentration (MIC), but rather its ability to generate negative Gaussian membrane curvature. This surprising result provides a topological basis for membrane activity common to AMPs. Moreover, we highlight an important distinction between the maximal recognizability of a sequence to a trained AMP classifier (its ability to generate membrane curvature) and its maximal antimicrobial efficacy. As mutational distances are increased from known AMPs, we find AMP-like sequences that are increasingly difficult for nature to discover via simple mutation. Using the sequence map as a discovery tool, we find a unexpectedly diverse taxonomy of sequences that are just as membrane-active as known AMPs, but with a broad range of primary functions distinct from AMP functions, including endogenous neuropeptides, viral fusion proteins, topogenic peptides, and amyloids. The SVM classifier is useful as a general detector of membrane activity in peptide sequences.
