Chen Jiming, Shukla Diwakar
Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A.
Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A.
Biochem J. 2022 Apr 29;479(8):921-928. doi: 10.1042/BCJ20200942.
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.
近几十年来，随着新计算工具的开发和计算硬件的进步，蛋白质的计算结构生物学发展迅速。然而，虽然这些技术已广泛用于推动人类医学的进步，但这些方法在植物科学中的应用较少。在过去几年中，机器学习方法在计算结构生物学中受到欢迎。这些方法推动了新工具的开发，这些新工具能够应对阻碍植物计算结构生物学广泛应用的主要挑战。本文探讨了计算结构生物学中仍然存在的挑战，以及机器学习技术的发展如何推动对植物进行更深入的计算结构生物学研究。
