Discovering highly potent antimicrobial peptides with deep generative model HydrAMP
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Abstract
Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance, prompting a need for novel computational approaches to peptide generation. Here, we propose HydrAMP, a conditional variational autoencoder that learns lower-dimensional, continuous representation of peptides and captures their antimicrobial properties. The model disentangles the learnt representation of a peptide from its antimicrobial conditions and leverages parameter-controlled creativity. HydrAMP is the first model that is directly optimized for diverse tasks, including unconstrained and analogue generation and outperforms other approaches in these tasks. An additional preselection procedure based on ranking of generated peptides and molecular dynamics simulations increases experimental validation rate. Wet-lab experiments on five bacterial strains confirm high activity of nine peptides generated as analogues of clinically relevant prototypes, as well as six analogues of an inactive peptide. HydrAMP enables generation of diverse and potent peptides, making a step towards resolving the antimicrobial resistance crisis.
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- Authors
- Szymczak, P., Możejko, M., Grzegorzek, T., Jurczak, R., Bauer, M., Neubauer, D., Sikora, K., Michalski, M., Sroka, J., Setny, P., Kamysz, W., & Szczurek, E.
- Affiliations
- University of Warsaw (PL)
- Nvidia (United States) (US)
- Gdańsk Medical University (PL)
- Corresponding author
- Szczurek, Ewa
- ORCID
- Venue
- Nature Communications
- Published
- 2023-03-15
- Volume
- 14
- Issue
- 1
- Pages
- 1453-1453
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10017685
- OpenAlex
- W4324344300
- Cited by
- 231
- References
- 78
- Citations by year
- 2026: 85 · 2025: 88 · 2024: 49 · 2023: 9
- FWCI
- 42.06
- Type
- journal article
- Language
- en
- Update notices
- correction (2023-08-23)
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ReIn UW (University of Warsaw) (submittedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Nvidia
- Narodowym Centrum Nauki
- Uniwersytet Warszawski
- Interdyscyplinarne Centrum Modelowania Matematycznego i Komputerowego UW
- Topics
- Life Sciences › Immunology and Microbiology › Microbiology › Antimicrobial Peptides and Activities
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Keywords
- antimicrobial peptides, conditional variational autoencoder, deep generative models, peptide production, antimicrobial resistance, peptide analogues, disentangled representations, molecular dynamics simulation
- MeSH terms
- Antimicrobial Peptides, Anti-Infective Agents, Bacteria, Antimicrobial Cationic Peptides
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