Journal article

A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction

Menglu Li, Yanan Wang, Fuyi Li, Yun Zhao, Mengya Liu, Sijia Zhang, Yannan Bin, A Ian Smith, Geoffrey I Webb, Jian Li, Jiangning Song, Junfeng Xia

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS | IEEE COMPUTER SOC | Published : 2021

Abstract

Multi-drug resistance (MDR) has become one of the greatest threats to human health worldwide, and novel treatment methods of infections caused by MDR bacteria are urgently needed. Phage therapy is a promising alternative to solve this problem, to which the key is correctly matching target pathogenic bacteria with the corresponding therapeutic phage. Deep learning is powerful for mining complex patterns to generate accurate predictions. In this study, we develop PredPHI (Predicting Phage-Host Interactions), a deep learning-based tool capable of predicting the host of phages from sequence data. We collect >3000 phage-host pairs along with their protein sequences from PhagesDB and GenBank datab..

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University of Melbourne Researchers

Grants

Awarded by National Natural Science Foundation of China


Awarded by Anhui Provincial Outstanding Young Talent Support Plan


Awarded by Recruitment Program for Leading Talent Team of Anhui Province


Awarded by China Postdoctoral Science Foundation


Awarded by Anhui Provincial Postdoctoral Science Foundation


Awarded by Key Project of Anhui Provincial Education Department


Awarded by National Health and Medical Research Council of Australia (NHMRC)


Awarded by National Institute of Allergy and Infectious Diseases of the National Institutes of Health


Funding Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (61672037, 21601001, 11835014 and U19A2064), the Anhui Provincial Outstanding Young Talent Support Plan (gxyqZD2017005), the Young Wanjiang Scholar Program of Anhui Province, the Recruitment Program for Leading Talent Team of Anhui Province (2019-16), the China Postdoctoral Science Foundation Grant (2018M630699), the Anhui Provincial Postdoctoral Science Foundation Grant (2017B325), and the Key Project of Anhui Provincial Education Department (KJ2017ZD01). JL and JS's work was supported by grants from the National Health and Medical Research Council of Australia (NHMRC) (1144652 and 1127948), the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (R01 AI111965) and a Major Inter-Disciplinary Research (IDR) Grant Awarded by Monash University.