Skip to main content

Research Repository

Advanced Search

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

A Machine Learning Protocol for Predicting Protein Infrared Spectra Thumbnail


Authors

Sheng Ye

Kai Zhong

Jinxiao Zhang

Wei Hu

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

Files





You might also like



Downloadable Citations