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Open Access

Protein Residue Contact Prediction Based on Deep Learning and Massive Statistical Features from Multi-Sequence Alignment

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
University of Chinese Academy of Sciences, Beijing 100049, China
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
School of Software Engineering, University of Science and Technology of China, Hefei 230051, China
Department of Computer Science, The University of Hong Kong, Hong Kong 999077, China
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

†Huiling Zhang, Min Hao, and Hao Wu contribute equally to this work.

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Abstract

Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3D structure. An accurate residue-residue contact map is one of the essential elements for current ab initio prediction protocols of 3D structure prediction. Recently, with the combination of deep learning and direct coupling techniques, the performance of residue contact prediction has achieved significant progress. However, a considerable number of current Deep-Learning (DL)-based prediction methods are usually time-consuming, mainly because they rely on different categories of data types and third-party programs. In this research, we transformed the complex biological problem into a pure computational problem through statistics and artificial intelligence. We have accordingly proposed a feature extraction method to obtain various categories of statistical information from only the multi-sequence alignment, followed by training a DL model for residue-residue contact prediction based on the massive statistical information. The proposed method is robust in terms of different test sets, showed high reliability on model confidence score, could obtain high computational efficiency and achieve comparable prediction precisions with DL methods that relying on multi-source inputs.

Keywords

multi-sequence alignmentresidue-residue contact predictionfeature extractionstatistical informationDeep Learning (DL)high computational efficiency

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Tsinghua Science and Technology
Volume 27 Issue 5,
October 2022
Pages 843-854
DOI: 10.26599/TST.2021.9010064
Cite this article:
Zhang H, Hao M, Wu H, et al. Protein Residue Contact Prediction Based on Deep Learning and Massive Statistical Features from Multi-Sequence Alignment. Tsinghua Science and Technology, 2022, 27(5): 843-854. https://doi.org/10.26599/TST.2021.9010064

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Received: 20 July 2021
Revised: 17 August 2021
Accepted: 20 August 2021
Published: 17 March 2022
© The author(s) 2022.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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