Advanced Review
Artificial intelligence advances for de novo molecular structure modeling in cryo-electron microscopy
Dong Si,
Andrew Nakamura,
Runbang Tang,
Haowen Guan,
Jie Hou,
Ammaar Firozi,
Renzhi Cao,
Kyle Hippe,
Minglei Zhao,
Corresponding Author
Dong Si
Division of Computing and Software Systems, University of Washington Bothell, Bothell, Washington, USA
Correspondence
Dong Si, Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA.
Email: [email protected]
Contribution: Conceptualization, Data curation, Funding acquisition, Investigation, Resources, Supervision, Visualization, Writing - original draft, Writing - review & editing
Search for more papers by this authorAndrew Nakamura
Division of Computing and Software Systems, University of Washington Bothell, Bothell, Washington, USA
Contribution: Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - review & editing
Search for more papers by this authorRunbang Tang
Molecular Engineering and Sciences Institute, University of Washington Seattle, Seattle, Washington, USA
Contribution: Data curation, Formal analysis, Investigation, Validation, Writing - review & editing
Search for more papers by this authorHaowen Guan
Applied and Computational Math Sciences, University of Washington Seattle, Seattle, Washington, USA
Contribution: Data curation, Investigation, Methodology, Validation, Writing - review & editing
Search for more papers by this authorJie Hou
Department of Computer Science, Saint Louis University, Saint Louis, Missouri, USA
Contribution: Investigation, Methodology, Visualization, Writing - review & editing
Search for more papers by this authorAmmaar Firozi
Department of Computer Science, Saint Louis University, Saint Louis, Missouri, USA
Contribution: Investigation, Methodology, Writing - review & editing
Search for more papers by this authorRenzhi Cao
Department of Computer Science, Pacific Lutheran University, Tacoma, Washington, USA
Contribution: Investigation, Methodology, Writing - review & editing
Search for more papers by this authorKyle Hippe
Department of Computer Science, Pacific Lutheran University, Tacoma, Washington, USA
Contribution: Investigation, Writing - review & editing
Search for more papers by this authorMinglei Zhao
Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois, USA
Contribution: Investigation, Writing - review & editing
Search for more papers by this authorDong Si,
Andrew Nakamura,
Runbang Tang,
Haowen Guan,
Jie Hou,
Ammaar Firozi,
Renzhi Cao,
Kyle Hippe,
Minglei Zhao,
Corresponding Author
Dong Si
Division of Computing and Software Systems, University of Washington Bothell, Bothell, Washington, USA
Correspondence
Dong Si, Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA.
Email: [email protected]
Contribution: Conceptualization, Data curation, Funding acquisition, Investigation, Resources, Supervision, Visualization, Writing - original draft, Writing - review & editing
Search for more papers by this authorAndrew Nakamura
Division of Computing and Software Systems, University of Washington Bothell, Bothell, Washington, USA
Contribution: Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - review & editing
Search for more papers by this authorRunbang Tang
Molecular Engineering and Sciences Institute, University of Washington Seattle, Seattle, Washington, USA
Contribution: Data curation, Formal analysis, Investigation, Validation, Writing - review & editing
Search for more papers by this authorHaowen Guan
Applied and Computational Math Sciences, University of Washington Seattle, Seattle, Washington, USA
Contribution: Data curation, Investigation, Methodology, Validation, Writing - review & editing
Search for more papers by this authorJie Hou
Department of Computer Science, Saint Louis University, Saint Louis, Missouri, USA
Contribution: Investigation, Methodology, Visualization, Writing - review & editing
Search for more papers by this authorAmmaar Firozi
Department of Computer Science, Saint Louis University, Saint Louis, Missouri, USA
Contribution: Investigation, Methodology, Writing - review & editing
Search for more papers by this authorRenzhi Cao
Department of Computer Science, Pacific Lutheran University, Tacoma, Washington, USA
Contribution: Investigation, Methodology, Writing - review & editing
Search for more papers by this authorKyle Hippe
Department of Computer Science, Pacific Lutheran University, Tacoma, Washington, USA
Contribution: Investigation, Writing - review & editing
Search for more papers by this authorMinglei Zhao
Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois, USA
Contribution: Investigation, Writing - review & editing
Search for more papers by this authorEdited by: Peter R. Schreiner, Editor-in-Chief
Funding information: National Science Foundation, Grant/Award Number: 2030381
Abstract
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
Graphical Abstract
De novo structure modeling and feature detection from cryo-EM.
CONFLICT OF INTEREST
The authors have declared no conflicts of interest for this article.
Open Research
DATA AVAILABILITY STATEMENT
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
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