Machine Learning and Protein Allostery

Authors

Sian Xiao, Southern Methodist University
Gennady M. Verkhivker, Chapman UniversityFollow
Peng Tao, Southern Methodist University

Document Type

Article

Publication Date

12-21-2022

Abstract

The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric mechanisms will have an increasingly important role in bridging a wide spectrum of data-intensive experimental and theoretical technologies.

Comments

NOTICE: this is the author’s version of a work that was accepted for publication in Trends in Biochemical Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Trends in Biochemical Sciences, volume 48, issue 4, in 2023. https://doi.org/10.1016/j.tibs.2022.12.001

The Creative Commons license below applies only to this version of the article.

This scholarship is part of the Chapman University COVID-19 Archives.

Recommended Citation

Xiao S, Verkhivker GM, Tao P. Machine learning and protein allostery. Trends Biochem Sci. 2023;48(4):375-390. https://doi.org/10.1016/j.tibs.2022.12.001

Peer Reviewed

1

Copyright

Elsevier

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.