Your browser doesn't support javascript.
loading
Designing antimicrobial peptides using deep learning and molecular dynamic simulations.
Cao, Qiushi; Ge, Cheng; Wang, Xuejie; Harvey, Peta J; Zhang, Zixuan; Ma, Yuan; Wang, Xianghong; Jia, Xinying; Mobli, Mehdi; Craik, David J; Jiang, Tao; Yang, Jinbo; Wei, Zhiqiang; Wang, Yan; Chang, Shan; Yu, Rilei.
Afiliação
  • Cao Q; Key Laboratory of Marine Drugs, Chinese Ministry of Education, School of Medicine and Pharmacy, Ocean University of China, 5 Yushan Road, Qingdao 266003, China.
  • Ge C; Qingdao National Laboratory for Marine Science and Technology, Qingdao 266003, China.
  • Wang X; Key Laboratory of Marine Drugs, Chinese Ministry of Education, School of Medicine and Pharmacy, Ocean University of China, 5 Yushan Road, Qingdao 266003, China.
  • Harvey PJ; Qingdao National Laboratory for Marine Science and Technology, Qingdao 266003, China.
  • Zhang Z; Qingdao National Laboratory for Marine Science and Technology, Qingdao 266003, China.
  • Ma Y; College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China.
  • Wang X; Institute for Molecular Bioscience, Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science, The University of Queensland, Brisbane, QLD, 4072 Australia.
  • Jia X; Key Laboratory of Marine Drugs, Chinese Ministry of Education, School of Medicine and Pharmacy, Ocean University of China, 5 Yushan Road, Qingdao 266003, China.
  • Mobli M; Qingdao National Laboratory for Marine Science and Technology, Qingdao 266003, China.
  • Craik DJ; Key Laboratory of Marine Drugs, Chinese Ministry of Education, School of Medicine and Pharmacy, Ocean University of China, 5 Yushan Road, Qingdao 266003, China.
  • Jiang T; Qingdao National Laboratory for Marine Science and Technology, Qingdao 266003, China.
  • Yang J; Qingdao National Laboratory for Marine Science and Technology, Qingdao 266003, China.
  • Wei Z; College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China.
  • Wang Y; Centre for Advanced Imaging, The University of Queensland, St Lucia QLD 4072.
  • Chang S; Centre for Advanced Imaging, The University of Queensland, St Lucia QLD 4072.
  • Yu R; Institute for Molecular Bioscience, Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science, The University of Queensland, Brisbane, QLD, 4072 Australia.
Brief Bioinform ; 24(2)2023 03 19.
Article em En | MEDLINE | ID: mdl-36857616
With the emergence of multidrug-resistant bacteria, antimicrobial peptides (AMPs) offer promising options for replacing traditional antibiotics to treat bacterial infections, but discovering and designing AMPs using traditional methods is a time-consuming and costly process. Deep learning has been applied to the de novo design of AMPs and address AMP classification with high efficiency. In this study, several natural language processing models were combined to design and identify AMPs, i.e. sequence generative adversarial nets, bidirectional encoder representations from transformers and multilayer perceptron. Then, six candidate AMPs were screened by AlphaFold2 structure prediction and molecular dynamic simulations. These peptides show low homology with known AMPs and belong to a novel class of AMPs. After initial bioactivity testing, one of the peptides, A-222, showed inhibition against gram-positive and gram-negative bacteria. The structural analysis of this novel peptide A-222 obtained by nuclear magnetic resonance confirmed the presence of an alpha-helix, which was consistent with the results predicted by AlphaFold2. We then performed a structure-activity relationship study to design a new series of peptide analogs and found that the activities of these analogs could be increased by 4-8-fold against Stenotrophomonas maltophilia WH 006 and Pseudomonas aeruginosa PAO1. Overall, deep learning shows great potential in accelerating the discovery of novel AMPs and holds promise as an important tool for developing novel AMPs.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Antibacterianos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Antibacterianos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article