ArticleNature biomedical engineering2024
Deep mutational scanning and machine learning for the analysis of antimicrobial-peptide features driving membrane selectivity.
Article in Nature biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Profiling Protein-Peptide Interactions by Yeast Surface Display.Current protocols · 2026Article
- Engineering host‑defense peptides enhanced by artificial intelligence and nano delivery systems to overcome biofilms and antimicrobial resistance.Engineering microbiology · 2026Review
- AI-Driven Discovery and Design of Antimicrobial Peptides: Progress, Challenges, and Opportunities.Probiotics and antimicrobial proteins · 2026Review
- β-Hairpin Antimicrobial Peptides: Class Diversity and Sequence Analysis.ACS infectious diseases · 2026Review
- Intrinsic dataset features drive mutational effect prediction by protein language models.bioRxiv : the preprint server for biology · 2026Article
- Mechanism-Guided Nanoengineered Therapeutic Peptides for Bone Healing.International journal of nanomedicine · 2026Review
- Antifungal peptides: From modes of action to synergistic and immunologic potential.Cell stress · 2026Article
- Design and TAG-Assisted Synthesis of the C-Terminal Amidated Antimicrobial Peptide NCBP-1 Derived from a Plant-Derived Noncanonical NCBP and Its Biological Activity.Protein and peptide letters · 2026Article
- A Comprehensive In-Silico Pipeline for the Discovery of Non-toxic, Stable Antimicrobial Peptides From Databases for Targeting Multi-Drug ResistantBioinformatics and biology insights · 2026Article
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- LDBT instead of DBTL: combining machine learning and rapid cell-free testing.Nature communications · 2025Article
- Painting Peptides With Antimicrobial Potency Through Deep Reinforcement Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Resurfacing promotes antibacterial activity of a lipid A-binding nanobody.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A comprehensive dataset of therapeutic peptides on multi-function property and structure information.Scientific data · 2025Article
- Medium-sized protein language models perform well at transfer learning on realistic datasets.Scientific reports · 2025Article
- Medium-sized protein language models perform well at transfer learning on realistic datasets.bioRxiv : the preprint server for biology · 2025Article
- A systematic evaluation of the language-of-viral-escape model using multiple machine learning frameworks.Journal of the Royal Society, Interface · 2025Article
- Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Protease-Resistant, Broad-Spectrum Antimicrobial Peptides with High Antibacterial and Antifungal Activity.Life (Basel, Switzerland) · 2025Article
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Abstract
Many antimicrobial peptides directly disrupt bacterial membranes yet can also damage mammalian membranes. It is therefore central to their therapeutic use that rules governing the membrane selectivity of antimicrobial peptides be deciphered. However, this is difficult even for short peptides owing to the large combinatorial space of amino acid sequences. Here we describe a method for measuring the loss or maintenance of antimicrobial-peptide activity for thousands of peptide-sequence variants simultaneously, and its application to Protegrin-1, a potent yet toxic antimicrobial peptide, to determine the positional importance and flexibility of residues across its sequence while identifying variants with changes in membrane selectivity. More bacterially selective variants maintained a membrane-bound secondary structure while avoiding aromatic residues and cysteine pairs. A machine-learning model trained with our datasets accurately predicted membrane-specific activities for over 5.7 million Protegrin-1 variants, and identified one variant that showed substantially reduced toxicity and retention of activity in a mouse model of intraperitoneal infection. The high-throughput methodology may help elucidate sequence-structure-function relationships in antimicrobial peptides and inform the design of peptide-based synthetic drugs.
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