Identification of antimicrobial peptides from the human gut microbiome using deep learning.

Journal: Nature biotechnology
PMID: 35241840

Abstract

The human gut microbiome encodes a large variety of antimicrobial peptides (AMPs), but the short lengths of AMPs pose a challenge for computational prediction. Here we combined multiple natural language processing neural network models, including LSTM, Attention and BERT, to form a unified pipeline for candidate AMP identification from human gut microbiome data. Of 2,349 sequences identified as candidate AMPs, 216 were chemically synthesized, with 181 showing antimicrobial activity (a positive rate of >83%). Most of these peptides have less than 40% sequence homology to AMPs in the training set. Further characterization of the 11 most potent AMPs showed high efficacy against antibiotic-resistant, Gram-negative pathogens and demonstrated significant efficacy in lowering bacterial load by more than tenfold against a mouse model of bacterial lung infection. Our study showcases the potential of machine learning approaches for mining functional peptides from metagenome data and accelerating the discovery of promising AMP candidate molecules for in-depth investigations.

Authors

  • Yue Ma
    The School of Civil Engineering, Harbin University, Harbin 150086, China.
  • Zhengyan Guo
    State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.
  • Binbin Xia
    CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.
  • Yuwei Zhang
    Institute of Biomaterials and Biomedical Engineering , University of Toronto , 164 College Street , Toronto , Ontario M5S 3G9 , Canada.
  • Xiaolin Liu
    Department of Physics, Shanghai University of Electric Power, Shanghai 200090, China. Electronic address: xlliu@shiep.edu.cn.
  • Ying Yu
    School of Chemistry and Environment, Guangzhou Key Laboratory of Analytical Chemistry for Biomedicine, South China Normal University, Guangzhou 510006, PR China. Electronic address: yuyhs@scnu.edu.cn.
  • Na Tang
    State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.
  • Xiaomei Tong
    CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.
  • Min Wang
    National and Local Joint Engineering Research Center of Ecological Treatment Technology for Urban Water Pollution, Wenzhou University, Wenzhou 325035, China.
  • Xin Ye
    Department of Stomatology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Jie Feng
  • Yihua Chen
    University of Chinese Academy of Sciences, Beijing, China. chenyihua@im.ac.cn.
  • Jun Wang
    Department of Speech, Language, and Hearing Sciences and the Department of Neurology, The University of Texas at Austin, Austin, TX 78712, USA.

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