%ASi, Dong [Division of Computing and Software Systems University of Washington Bothell Bothell Washington USA]%ANakamura, Andrew [Division of Computing and Software Systems University of Washington Bothell Bothell Washington USA]%ATang, Runbang [Molecular Engineering and Sciences Institute University of Washington Seattle Seattle Washington USA]%AGuan, Haowen [Applied and Computational Math Sciences University of Washington Seattle Seattle Washington USA]%AHou, Jie [Department of Computer Science Saint Louis University Saint Louis Missouri USA]%AFirozi, Ammaar [Department of Computer Science Saint Louis University Saint Louis Missouri USA]%ACao, Renzhi [Department of Computer Science Pacific Lutheran University Tacoma Washington USA]%AHippe, Kyle [Department of Computer Science Pacific Lutheran University Tacoma Washington USA]%AZhao, Minglei [Department of Biochemistry and Molecular Biology University of Chicago Chicago Illinois USA]%BJournal Name: WIREs Computational Molecular Science; Journal Volume: 12; Journal Issue: 2; Related Information: CHORUS Timestamp: 2023-08-27 03:08:54 %D2021%IWiley Blackwell (John Wiley & Sons) %JJournal Name: WIREs Computational Molecular Science; Journal Volume: 12; Journal Issue: 2; Related Information: CHORUS Timestamp: 2023-08-27 03:08:54 %K %MOSTI ID: 10363811 %PMedium: X %TArtificial intelligence advances for de novo molecular structure modeling in cryo‐electron microscopy %XAbstract

Cryo‐electron microscopy (cryo‐EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize. Although cryo‐EM has been drastically improved to generate high‐resolution three‐dimensional maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo‐EM model building approach is template‐based homology modeling. Manual de novo modeling is very time‐consuming when no template model is found in the database. In recent years, de novo cryo‐EM modeling using machine learning (ML) and deep learning (DL) has ranked among the top‐performing methods in macromolecular structure modeling. DL‐based de novo cryo‐EM modeling is an important application of artificial intelligence, with impressive results and great potential for the next generation of molecular biomedicine. Accordingly, we systematically review the representative ML/DL‐based de novo cryo‐EM modeling methods. Their significances are discussed from both practical and methodological viewpoints. We also briefly describe the background of cryo‐EM data processing workflow. Overall, this review provides an introductory guide to modern research on artificial intelligence for de novo molecular structure modeling and future directions in this emerging field.

This article is categorized under:

Structure and Mechanism > Molecular Structures

Structure and Mechanism > Computational Biochemistry and Biophysics

Data Science > Artificial Intelligence/Machine Learning

%0Journal Article