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Machine learning recognition of protein secondary structures based on two-dimensional spectroscopic descriptors.

ABSTRACT: Protein secondary structure discrimination is crucial for understanding their biological function. It is not generally possible to invert spectroscopic data to yield the structure. We present a machine learning protocol which uses two-dimensional UV (2DUV) spectra as pattern recognition descriptors, aiming at automated protein secondary structure determination from spectroscopic features. Accurate secondary structure recognition is obtained for homologous (97%) and nonhomologous (91%) protein segments, randomly selected from simulated model datasets. The advantage of 2DUV descriptors over one-dimensional linear absorption and circular dichroism spectra lies in the cross-peak information that reflects interactions between local regions of the protein. Thanks to their ultrafast (∼200 fs) nature, 2DUV measurements can be used in the future to probe conformational variations in the course of protein dynamics.

SUBMITTER: Ren H 

PROVIDER: S-EPMC9171355 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Machine learning recognition of protein secondary structures based on two-dimensional spectroscopic descriptors.

Ren Hao H   Zhang Qian Q   Wang Zhengjie Z   Zhang Guozhen G   Liu Hongzhang H   Guo Wenyue W   Mukamel Shaul S   Jiang Jun J  

Proceedings of the National Academy of Sciences of the United States of America 20220427 18

Protein secondary structure discrimination is crucial for understanding their biological function. It is not generally possible to invert spectroscopic data to yield the structure. We present a machine learning protocol which uses two-dimensional UV (2DUV) spectra as pattern recognition descriptors, aiming at automated protein secondary structure determination from spectroscopic features. Accurate secondary structure recognition is obtained for homologous (97%) and nonhomologous (91%) protein se  ...[more]

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