ArticleNature biotechnology2022
Identification of antimicrobial peptides from the human gut microbiome using deep learning.
Article in Nature biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 234 papers, 1 of them a synthesis that pooled it.
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Who cites it
234 citing papers in PubMed, 1 synthesis or guideline pooled it, 450 citations in OpenAlex.
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Combating antimicrobial resistance through multimodal data via artificial intelligence.One health (Amsterdam, Netherlands) · 2026Review
- From sequence space to ecological function: microbiome-derived antimicrobial peptides as community effectors and therapeutic leads.Essays in biochemistry · 2026Review
- A unified framework for potency-oriented AMP discovery via multi-modal learning and guided sequence synthesis.PLoS computational biology · 2026Article
- Molecular bioindicators for assessing environmental impacts of emerging contaminants.Fundamental research · 2026Review
- Toward microbiota-friendly design and evaluation of antimicrobial peptides: a mini-review.Folia microbiologica · 2026Review
- DeorphaNN: Virtual screening of GPCR peptide agonists using AlphaFold-predicted active-state complexes and deep learning embeddings.Molecular cell · 2026Article
- Encrypted immunity mediated by peptides.PLoS biology · 2026Review
- PepMCP: a graph-based membrane contact probability predictor for membrane-lytic antimicrobial peptides.Bioinformatics (Oxford, England) · 2026Article
- Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications.Science China. Life sciences · 2026Review
- Deep learning-driven discovery and mechanism of action study of a minimalist conopeptide targetingActa pharmaceutica Sinica. B · 2026Article
- Goat gut microbiome as a reservoir for microorganism-encoded short peptides: regulation by host development age and nematode challenge.NPJ biofilms and microbiomes · 2026Article
- AI-empowered human microbiome research.Gut · 2026Review
- Harnessing artificial intelligence for antimicrobial discovery and optimization.Current opinion in microbiology · 2026Review
- Cutibacterium acnes inhibits Staphylococcus lugdunensis biofilm formation through inhibition of autolysis and purine biosynthesis.NPJ biofilms and microbiomes · 2026Article
- Review
- Molecular fingerprints are strong models for peptide function prediction.Bioinformatics (Oxford, England) · 2026Article
- Identifying Cytokine Motif-Containing, Immunomodulatory Bacterial Proteins in Human Gut Microbiome.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- De Novo Multi-Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
174 more citing papers are in PubMed but not listed here.
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The human gut microbiome encodes a large variety of antimicrobial peptides (AMPs), but the short lengths of AMPs pose a challenge for computational prediction. Here we combined multiple natural language processing neural network models, including LSTM, Attention and BERT, to form a unified pipeline for candidate AMP identification from human gut microbiome data. Of 2,349 sequences identified as candidate AMPs, 216 were chemically synthesized, with 181 showing antimicrobial activity (a positive rate of >83%). Most of these peptides have less than 40% sequence homology to AMPs in the training set. Further characterization of the 11 most potent AMPs showed high efficacy against antibiotic-resistant, Gram-negative pathogens and demonstrated significant efficacy in lowering bacterial load by more than tenfold against a mouse model of bacterial lung infection. Our study showcases the potential of machine learning approaches for mining functional peptides from metagenome data and accelerating the discovery of promising AMP candidate molecules for in-depth investigations.
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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.