Integration of machine learning with computational structural biology of plants.

Journal: The Biochemical journal
Published Date:

Abstract

Computational structural biology of proteins has developed rapidly in recent decades with the development of new computational tools and the advancement of computing hardware. However, while these techniques have widely been used to make advancements in human medicine, these methods have seen less utilization in the plant sciences. In the last several years, machine learning methods have gained popularity in computational structural biology. These methods have enabled the development of new tools which are able to address the major challenges that have hampered the wide adoption of the computational structural biology of plants. This perspective examines the remaining challenges in computational structural biology and how the development of machine learning techniques enables more in-depth computational structural biology of plants.

Authors

  • Jiming Chen
    College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, China.
  • Diwakar Shukla
    Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A.