Protein design via deep learning.

Journal: Briefings in bioinformatics
Published Date:

Abstract

Proteins with desired functions and properties are important in fields like nanotechnology and biomedicine. De novo protein design enables the production of previously unseen proteins from the ground up and is believed as a key point for handling real social challenges. Recent introduction of deep learning into design methods exhibits a transformative influence and is expected to represent a promising and exciting future direction. In this review, we retrospect the major aspects of current advances in deep-learning-based design procedures and illustrate their novelty in comparison with conventional knowledge-based approaches through noticeable cases. We not only describe deep learning developments in structure-based protein design and direct sequence design, but also highlight recent applications of deep reinforcement learning in protein design. The future perspectives on design goals, challenges and opportunities are also comprehensively discussed.

Authors

  • Wenze Ding
    School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China.
  • Kenta Nakai
    Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba-ken, 277-8562, Japan. knakai@ims.u-tokyo.ac.jp.
  • Haipeng Gong
    School of Life Science, Tsinghua University, Beijing 100084, China.