Sheng Ye
A Machine Learning Protocol for Predicting Protein Infrared Spectra
Ye, Sheng; Zhong, Kai; Zhang, Jinxiao; Hu, Wei; Hirst, Jonathan D.; Zhang, Guozhen; Mukamel, Shaul; Jiang, Jun
Authors
Kai Zhong
Jinxiao Zhang
Wei Hu
Professor JONATHAN HIRST JONATHAN.HIRST@NOTTINGHAM.AC.UK
Professor of Computational Chemistry
Guozhen Zhang
Shaul Mukamel
Jun Jiang
Abstract
© 2020 American Chemical Society. Infrared (IR) absorption provides important chemical fingerprints of biomolecules. Protein secondary structure determination from IR spectra is tedious since its theoretical interpretation requires repeated expensive quantum-mechanical calculations in a fluctuating environment. Herein we present a novel machine learning protocol that uses a few key structural descriptors to rapidly predict amide I IR spectra of various proteins and agrees well with experiment. Its transferability enabled us to distinguish protein secondary structures, probe atomic structure variations with temperature, and monitor protein folding. This approach offers a cost-effective tool to model the relationship between protein spectra and their biological/chemical properties.
Citation
Ye, S., Zhong, K., Zhang, J., Hu, W., Hirst, J. D., Zhang, G., …Jiang, J. (2020). A Machine Learning Protocol for Predicting Protein Infrared Spectra. Journal of the American Chemical Society, 142(45), 19071–19077. https://doi.org/10.1021/jacs.0c06530
Journal Article Type | Article |
---|---|
Acceptance Date | Oct 22, 2020 |
Online Publication Date | Oct 30, 2020 |
Publication Date | Nov 11, 2020 |
Deposit Date | Nov 2, 2020 |
Publicly Available Date | Oct 31, 2021 |
Journal | Journal of the American Chemical Society |
Print ISSN | 0002-7863 |
Electronic ISSN | 1520-5126 |
Publisher | American Chemical Society |
Peer Reviewed | Peer Reviewed |
Volume | 142 |
Issue | 45 |
Pages | 19071–19077 |
DOI | https://doi.org/10.1021/jacs.0c06530 |
Keywords | Colloid and Surface Chemistry; Biochemistry; General Chemistry; Catalysis |
Public URL | https://nottingham-repository.worktribe.com/output/5012497 |
Publisher URL | https://pubs.acs.org/doi/10.1021/jacs.0c06530 |
Additional Information | This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of the American Chemical Society,copyright© American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://pubs.acs.org/doi/10.1021/jacs.0c06530 |
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