title: Mapping membrane activity in undiscovered peptide sequence space using machine learning.
abstract: 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 σ 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 σ correlates not with a peptide's 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.
authors.affiliation: Department of Bioengineering, University of California, Los Angeles, CA 90095.
authors.forename: Ernest Y
authors.identifier: 0000-0001-5144-2552
authors.initials: EY
authors.lastname: Lee
authors.affiliation: Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL 61801.
authors.forename: Benjamin M
authors.initials: BM
authors.lastname: Fulan
authors.affiliation: Department of Bioengineering, University of California, Los Angeles, CA 90095; gclwong@seas.ucla.edu alf@illinois.edu.
authors.forename: Gerard C L
authors.initials: GC
authors.lastname: Wong
authors.affiliation: Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801; gclwong@seas.ucla.edu alf@illinois.edu.|Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801.
authors.forename: Andrew L
authors.identifier: 0000-0002-8829-9726
authors.initials: AL
authors.lastname: Ferguson
mesh_terms: D000595:Amino Acid Sequence / Q000235:genetics; D000900:Anti-Bacterial Agents / Q000494:pharmacology; D000890:Anti-Infective Agents / Q000494:pharmacology; D023181:Antimicrobial Cationic Peptides / Q000737:chemistry / Q000378:metabolism*; D002462:Cell Membrane / Q000378:metabolism; D000069550:Machine Learning / Q000706:statistics & numerical data*; D008826:Microbial Sensitivity Tests / Q000379:methods; D008962:Models, Theoretical; D010455:Peptides / Q000737:chemistry / Q000235:genetics; D060388:Support Vector Machine / Q000706:statistics & numerical data; D014961:X-Ray Diffraction
chemical_list: D000900:Anti-Bacterial Agents; D000890:Anti-Infective Agents; D023181:Antimicrobial Cationic Peptides; D010455:Peptides
keywords: antimicrobial peptides; cell-penetrating peptides; machine learning; membrane curvature; membrane permeation