Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM

• URL: http://arxiv.org/abs/2102.06125v1
• Date: Thu, 11 Feb 2021 17:06:20 GMT
• Title: Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM
• Authors: Dong Si, Andrew Nakamura, Runbang Tang, Haowen Guan, Jie Hou, Ammaar Firozi, Renzhi Cao, Kyle Hippe, Minglei Zhao
• Abstract summary: cryo-electron microscopy (cryo-EM) has become a major experimental technology to determine the structures of large protein complexes and molecular assemblies. Machine learning (ML) and deep learning (DL) has ranked among the top-performing methods in macromolecular structure modeling. This review provides an introductory guide to modern research on artificial intelligence for de novo molecular structure modeling.
• Score: 1.6301630538569722
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cryo-electron microscopy (cryo-EM) has become a major experimental technology 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 (3D) maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. Traditional cryo-EM model building approach is template-based homology modeling. Manual de novo modeling is very time-consuming when no template model could be 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. Deep-learning-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. And 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 (AI) for de novo molecular structure modeling and future directions in this emerging field.

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