Discovering highly potent antimicrobial peptides with deep generative model HydrAMP

Paulina Szymczak, Marcin Możejko, Tomasz Grzegorzek, Radosław Jurczak, Marta Bauer, Damian Neubauer, Karol Sikora, Michał Michalski, Jacek Sroka, Piotr Setny, Wojciech Kamysz and Ewa Szczurek ()
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Paulina Szymczak: University of Warsaw
Marcin Możejko: University of Warsaw
Tomasz Grzegorzek: University of Warsaw
Radosław Jurczak: University of Warsaw
Marta Bauer: Medical University of Gdańsk
Damian Neubauer: Medical University of Gdańsk
Karol Sikora: Medical University of Gdańsk
Michał Michalski: University of Warsaw
Jacek Sroka: University of Warsaw
Piotr Setny: University of Warsaw
Wojciech Kamysz: Medical University of Gdańsk
Ewa Szczurek: University of Warsaw

Nature Communications, 2023, vol. 14, issue 1, 1-23

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-36994-z

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