Application of DNA-Binding Protein Prediction Based on Graph Convolutional Network and Contact Map

Weizhong Lu; Nan Zhou; Yijie Ding; Hongjie Wu; Yu Zhang; Qiming Fu; Haiou Li

doi:10.1155/2022/9044793

Application of DNA-Binding Protein Prediction Based on Graph Convolutional Network and Contact Map

Biomed Res Int. 2022 Jan 17:2022:9044793. doi: 10.1155/2022/9044793. eCollection 2022.

Authors

Weizhong Lu^{1

2}, Nan Zhou¹, Yijie Ding¹, Hongjie Wu¹, Yu Zhang³, Qiming Fu¹, Haiou Li²

Affiliations

¹ School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
² Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, China.
³ Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, China.

Abstract

DNA contains the genetic information for the synthesis of proteins and RNA, and it is an indispensable substance in living organisms. DNA-binding proteins are an enzyme, which can bind with DNA to produce complex proteins, and play an important role in the functions of a variety of biological molecules. With the continuous development of deep learning, the introduction of deep learning into DNA-binding proteins for prediction is conducive to improving the speed and accuracy of DNA-binding protein recognition. In this study, the features and structures of proteins were used to obtain their representations through graph convolutional networks. A protein prediction model based on graph convolutional network and contact map was proposed. The method had some advantages by testing various indexes of PDB14189 and PDB2272 on the benchmark dataset.

MeSH terms

DNA-Binding Proteins*
Neural Networks, Computer*

Substances

DNA-Binding Proteins