自编码
抗菌肽
抗菌剂
肽
计算生物学
计算机科学
人工智能
生成模型
代表(政治)
抗生素耐药性
排名(信息检索)
机器学习
生成语法
生物
深度学习
生物化学
抗生素
微生物学
政治
法学
政治学
作者
Paulina Szymczak,Marcin Możejko,Tomasz Grzegorzek,Radosław Jurczak,Marta Bauer,Damian Neubauer,Karol Sikora,Michał Michalski,Jacek Sroka,Piotr Setny,Wojciech Kamysz,Ewa Szczurek
标识
DOI:10.1038/s41467-023-36994-z
摘要
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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