Cited by 239Antimicrobial Peptides and ActivitiesComputational Drug Discovery MethodsChemical Synthesis and Analysis

Discovering highly potent antimicrobial peptides with deep generative model HydrAMP

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Szymczak, Paulina, Możejko, Marcin, Grzegorzek, Tomasz, Jurczak, Radosław, Bauer, Marta, Neubauer, Damian, Sikora, Karol, Michalski, Michał +4 more·2023·Nature Communications

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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
Venue
Nature Communications
Published
2023-03-15
Volume
14
Issue
1
Pages
1453-1453
ISSN
2041-1723 (ISSN-L)
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
Open access
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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