Struct2GO: protein function prediction based on graph pooling algorithm and AlphaFold2 structure information.

Journal: Bioinformatics (Oxford, England)
Published Date: Oct 3, 2023

Abstract

MOTIVATION: In recent years, there has been a breakthrough in protein structure prediction, and the AlphaFold2 model of the DeepMind team has improved the accuracy of protein structure prediction to the atomic level. Currently, deep learning-based protein function prediction models usually extract features from protein sequences and combine them with protein-protein interaction networks to achieve good results. However, for newly sequenced proteins that are not in the protein-protein interaction network, such models cannot make effective predictions. To address this, this article proposes the Struct2GO model, which combines protein structure and sequence data to enhance the precision of protein function prediction and the generality of the model.

Authors

  • Peishun Jiao
    School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guang Dong 518055, China.
  • Beibei Wang
    School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, China.
  • Xuan Wang
    Baylor Scott & White Health, Dallas, TX, USA.
  • Bo Liu
    Wuhan United Imaging Healthcare Surgical Technology Co., Ltd., Wuhan, China.
  • Yadong Wang
    The Biofoundry, Department of Biomedical Engineering, Cornell University, Ithaca, NY, United States.
  • Junyi Li
    School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China. Electronic address: lijunyi@hit.edu.cn.

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