HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes

Microbiome

Pak-Leung Ho¹  Huiluo Cao¹  Ramzan Umarov²  Wenkai Han²  Xin Gao²  Carlos M. Duarte³  Yu Li⁴  Ming Fan⁵  Lihua Li⁵  Huan Chen⁶  Zeling Xu⁷  Aixin Yan⁷ 

[1] Carol Yu Center for Infection and Department of Microbiology, The University of Hong Kong, Hong Kong, People’s Republic of China;Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), 23955, Thuwal, Saudi Arabia;Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), 23955, Thuwal, Saudi Arabia;Biological and Environmental Sciences and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), 23955, Thuwal, Saudi Arabia;Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), 23955, Thuwal, Saudi Arabia;Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK), Hong Kong, People’s Republic of China;Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, People’s Republic of China;Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Zhejiang Institute of Microbiology, Hangzhou, People’s Republic of China;School of Biological Sciences, The University of Hong Kong, Hong Kong, People’s Republic of China;

关键词: Antibiotic resistance genes;    Deep learning;    Antibiotic class;    Resistant mechanism;    Gene mobility;    Multi-task learning;   

DOI  :  10.1186/s40168-021-01002-3

来源: Springer

【 摘 要 】

BackgroundThe spread of antibiotic resistance has become one of the most urgent threats to global health, which is estimated to cause 700,000 deaths each year globally. Its surrogates, antibiotic resistance genes (ARGs), are highly transmittable between food, water, animal, and human to mitigate the efficacy of antibiotics. Accurately identifying ARGs is thus an indispensable step to understanding the ecology, and transmission of ARGs between environmental and human-associated reservoirs. Unfortunately, the previous computational methods for identifying ARGs are mostly based on sequence alignment, which cannot identify novel ARGs, and their applications are limited by currently incomplete knowledge about ARGs.ResultsHere, we propose an end-to-end Hierarchical Multi-task Deep learning framework for ARG annotation (HMD-ARG). Taking raw sequence encoding as input, HMD-ARG can identify, without querying against existing sequence databases, multiple ARG properties simultaneously, including if the input protein sequence is an ARG, and if so, what antibiotic family it is resistant to, what resistant mechanism the ARG takes, and if the ARG is an intrinsic one or acquired one. In addition, if the predicted antibiotic family is beta-lactamase, HMD-ARG further predicts the subclass of beta-lactamase that the ARG is resistant to. Comprehensive experiments, including cross-fold validation, third-party dataset validation in human gut microbiota, wet-experimental functional validation, and structural investigation of predicted conserved sites, demonstrate not only the superior performance of our method over the state-of-art methods, but also the effectiveness and robustness of the proposed method.ConclusionsWe propose a hierarchical multi-task method, HMD-ARG, which is based on deep learning and can provide detailed annotations of ARGs from three important aspects: resistant antibiotic class, resistant mechanism, and gene mobility. We believe that HMD-ARG can serve as a powerful tool to identify antibiotic resistance genes and, therefore mitigate their global threat. Our method and the constructed database are available at http://www.cbrc.kaust.edu.sa/HMDARG/.2a9o_5z-RdRo9vpmWq5oHNVideo abstract (MP4 50984 kb)

【 授权许可】

CC BY