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.

Huiling Zhang;Min Hao;Hao Wu;Hing-Fung Ting;Yihong Tang;Wenhui Xi;Yanjie Wei

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

清华大学学报自然科学版（英文版）

[1]Huiling Zhang;Min Hao;Hao Wu;Hing-Fung Ting;Yihong Tang;Wenhui Xi;Yanjie Wei-.Protein Residue Contact Prediction Based on Deep Learning and Massive Statistical Features from Multi-Sequence Alignment)[J].清华大学学报自然科学版（英文版）,2022(05):843-854

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