Deep Learning in the Study of Protein-Related Interactions.

Journal: Protein and peptide letters
Published Date:

Abstract

Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In recent years, advances in biological and medical technology have provided us with explosive biological and physiological data, and deep learning-based algorithms have shown great promise in extracting features and learning patterns from complex data. At present, deep learning in protein research has emerged. In this review, we provide an introductory overview of the deep neural network theory and its unique properties. Mainly focused on the application of this technology in protein-related interactions prediction over the past five years, including protein-protein interactions prediction, protein-RNA\DNA, Protein- drug interactions prediction, and others. Finally, we discuss some of the challenges that deep learning currently faces.

Authors

  • Cheng Shi
    School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China.
  • Jiaxing Chen
    School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China.
  • Xinyue Kang
    School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China.
  • Guiling Zhao
    School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China.
  • Xingzhen Lao
    School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China.
  • Heng Zheng
    School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China.