Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Designing antimicrobial peptides using deep learning and molecular dynamic simulations.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
  • معلومة اضافية
    • المصدر:
      Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE
    • بيانات النشر:
      Publication: Oxford : Oxford University Press
      Original Publication: London ; Birmingham, AL : H. Stewart Publications, [2000-
    • الموضوع:
    • نبذة مختصرة :
      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.
      (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)
    • Contributed Indexing:
      Keywords: BERT; antimicrobial peptides; deep learning; molecular dynamic simulations
    • الرقم المعرف:
      0 (Anti-Bacterial Agents)
      0 (Antimicrobial Cationic Peptides)
      0 (Antimicrobial Peptides)
    • الموضوع:
      Date Created: 20230301 Date Completed: 20230321 Latest Revision: 20230322
    • الموضوع:
      20230322
    • الرقم المعرف:
      10.1093/bib/bbad058
    • الرقم المعرف:
      36857616