Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 12
3572-3577

Protein function prediction based on coevolutionary information and deep learning

Wang Jinlei
Ding Xueming
Qin Qiqi
Peng Boya
School of Optical-Electrical & Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2023.04.0166

Abstract

The function of protein is crucial for understanding the mechanisms of cellular and biological activities, as well as for studying the mechanisms of diseases. Traditional experimental and sequence alignment methods are insufficient to support large-scale protein functional annotation when in the face of the rapid growth of sequence databases. For this situation, this paper proposed EGNet model, which utilized the protein pre-training language model ESM2 and one-hot encoding to obtain the protein sequence encoding. The model integrated the coevolutionary information between residues, including PI and SPI, through sequence self-attention and physical calculations. Subsequently, the two types of coevolutionary information and the sequence encoding used in inputs for a multi-layered cascaded graph convolutional network to learn the node features of the sequence encoding and achieve end-to-end protein function prediction. Compared with earlier methods, EGNet achieves better performance on the EC category labels in the ENZYME database, which reaches 0.89 in the F-score and 0.91 in the AUPR. The results indicate that EGNet can achieve good performance by using only a single sequence to predict protein function, providing a rapid and effective method for protein function annotation.

Foundation Support

国家自然科学基金资助项目(11502145)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.04.0166
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 12
Section: Algorithm Research & Explore
Pages: 3572-3577
Serial Number: 1001-3695(2023)12-008-3572-06

Publish History

[2023-07-05] Accepted Paper
[2023-12-05] Printed Article

Cite This Article

王金雷, 丁学明, 秦琪琪, 等. 基于协同进化信息和深度学习的蛋白质功能预测 [J]. 计算机应用研究, 2023, 40 (12): 3572-3577. (Wang Jinlei, Ding Xueming, Qin Qiqi, et al. Protein function prediction based on coevolutionary information and deep learning [J]. Application Research of Computers, 2023, 40 (12): 3572-3577. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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