An antibacterial peptides recognition method based on BERT and Text-CNN.

Xiaofang XU; Chunde Yang; Kunxian Shu; Xinpu Yuan; Mocheng LI; Yunping Zhu; Tao Chen

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An antibacterial peptides recognition method based on BERT and Text-CNN.

Author: Xiaofang XU ¹ ; Chunde YANG ¹ ; Kunxian SHU ² ; Xinpu YUAN ³ ; Mocheng LI ⁴ ; Yunping ZHU ⁵ ; Tao CHEN ⁵
Author Information

1. The School of Computer Science and Technology, Chongqing University of Posts and Telecommunications Chongqing 400065, China.
2. Chongqing Key Laboratory on Big Data for Bio-Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
3. Department of General Surgery, First Medical Center, Chinese PLA General Hospital, Beijing 102206, China.
4. State Key Laboratory of High Performance Computing, Institute for Quantum Information, College of Computer National University of Defense Technology, Changsha 410073, Hunan, China.
5. State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Institute of Lifeomics, Academy of Military Medical Sciences, Academy of Military Sciences, Beijing 102206, China.
Publication Type:Journal Article
Keywords: antibacterial peptides; language model; pre-training; protein
MeSH: Anti-Bacterial Agents/chemistry*; Amino Acid Sequence; Antimicrobial Cationic Peptides/chemistry*; Antimicrobial Peptides; Natural Language Processing
From: Chinese Journal of Biotechnology 2023;39(4):1815-1824
CountryChina
Language:Chinese
Abstract: Antimicrobial peptides (AMPs) are small molecule peptides that are widely found in living organisms with broad-spectrum antibacterial activity and immunomodulatory effect. Due to slower emergence of resistance, excellent clinical potential and wide range of application, AMP is a strong alternative to conventional antibiotics. AMP recognition is a significant direction in the field of AMP research. The high cost, low efficiency and long period shortcomings of the wet experiment methods prevent it from meeting the need for the large-scale AMP recognition. Therefore, computer-aided identification methods are important supplements to AMP recognition approaches, and one of the key issues is how to improve the accuracy. Protein sequences could be approximated as a language composed of amino acids. Consequently, rich features may be extracted using natural language processing (NLP) techniques. In this paper, we combine the pre-trained model BERT and the fine-tuned structure Text-CNN in the field of NLP to model protein languages, develop an open-source available antimicrobial peptide recognition tool and conduct a comparison with other five published tools. The experimental results show that the optimization of the two-phase training approach brings an overall improvement in accuracy, sensitivity, specificity, and Matthew correlation coefficient, offering a novel approach for further research on AMP recognition.