ArticleProceedings of the National Academy of Sciences of the United States of America2016
Mapping membrane activity in undiscovered peptide sequence space using machine learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
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Who cites it
78 citing papers in PubMed, 182 citations in OpenAlex.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Bat-derived antimicrobial peptide MC-CATH3 Confers protection against bacterial infections via a potential ferroptosis-like death process.The Journal of biological chemistry · 2026Article
- Cooperative membrane association as a mechanistic origin of synergistic antimicrobial peptide activity.RSC chemical biology · 2026Review
- Sequence-Derived and Molecular Descriptors for Interpretable Modeling of Molecular Systems: Insights from Peptide Hemolysis.Journal of chemical information and modeling · 2026Article
- Design of Multifunctional Bioactive Peptides for Potential Application in Food Packaging: A Machine-Learning-Integrated Optimization Framework.ACS omega · 2026Article
- From Membrane Composition to Antimicrobial Strategies: Experimental and Computational Approaches to AMP Design and Selectivity.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- SARS-CoV-2 peptide fragments selectively dysregulate specific immune cell populations via Gaussian curvature targeting.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Dynamics of Th1/Th17 responses and antimicrobial pathways in leprosy skin lesions.The Journal of clinical investigation · 2025Article
- Structure-Antimicrobial Activity Relationships of Recombinant Host Defence Peptides Against Drug-Resistant Bacteria.Microbial biotechnology · 2025Article
- Drp1 Proteins Released from Hydrolysis-Driven Scaffold Disassembly Trigger Nucleotide-Dependent Membrane Remodeling to Promote Scission.Journal of the American Chemical Society · 2025Article
- Biosourced Functional Hydroxybenzoate-Journal of the American Chemical Society · 2025Article
- Machine Learning-Assisted Prediction and Generation of Antimicrobial Peptides.Small science · 2025Article
- Membrane Association of Intrinsically Disordered Proteins.Annual review of biophysics · 2025Review
- peptidy: a light-weight Python library for peptide representation in machine learning.Bioinformatics advances · 2025Article
- Complexin regulation of synaptic vesicle release: mechanisms in the central nervous system and specialized retinal ribbon synapses.Cell communication and signaling : CCS · 2024Review
- Directed evolution of antimicrobial peptides using multi-objective zeroth-order optimization.Briefings in bioinformatics · 2024Article
- An active machine learning discovery platform for membrane-disrupting and pore-forming peptides.Physical chemistry chemical physics : PCCP · 2024Article
- Comparing Multifunctional Viral and Eukaryotic Proteins for Generating Scission Necks in Membranes.ACS nano · 2024Article
- Structure-aware machine learning strategies for antimicrobial peptide discovery.Scientific reports · 2024Article
- Novel technologies uncover novel 'anti'-microbial peptides inPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
18 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.