Improved compound-protein interaction site and binding affinity prediction using self-supervised protein embeddings.

Journal: BMC bioinformatics
Published Date: Dec 16, 2022

Abstract

BACKGROUND: Compound-protein interaction site and binding affinity predictions are crucial for drug discovery and drug design. In recent years, many deep learning-based methods have been proposed for predications related to compound-protein interaction. For protein inputs, how to make use of protein primary sequence and tertiary structure information has impact on prediction results.

Authors

  • Jialin Wu
    Department of Computer Science, University of Texas at Austin, Austin, TX 78712, United States.
  • Zhe Liu
    Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
  • Xiaofeng Yang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Zhanglin Lin
    School of Biology and Biological Engineering, South China University of Technology, 382 East Outer Loop Road, University Park, Guangzhou, 510006, Guangdong, China. zhanglinlin@scut.edu.cn.

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