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Prediction of Order Parameters based on Protein NMR Structure Ensemble and Machine Learning
Open accessPreprint — published as 10.1007/s10858-024-00435-w · Paper page
Abstract
The fast motions of proteins at the picosecond to nanosecond timescale, known as fast dynamics, are closely related to protein conformational entropy and rearrangement, which in turn affect catalysis, ligand binding and protein allosteric effects. The most used NMR approach to study fast protein dynamics is the model free method, which uses order parameter S2 to describe the amplitude of the internal motion of local group. However, to obtain order parameter through NMR experiments is quite complex and lengthy. In this paper, we present a machine learning approach for predicting order parameters based on protein NMR structure ensemble. A random forest model is used to learn the relationship between order parameters and structural features. Our method achieves high accuracy in predicting order parameters for a test dataset of 10 proteins, with a Pearson correlation coefficient of 0.817 and a root-mean-square error of 0.131.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906
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Metadata
- Authors
- Wang, Q., Miao, Z., Xiao, X., Zhang, X., Yang, D., Jiang, B., & Liu, M.
- Affiliations
- Wuhan Institute of Physics and Mathematics (CN)
- National University of Singapore (SG)
- Corresponding author
- Jiang, Bin
- Liu, Maili
- Venue
- Journal of Biomolecular NMR
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-10-11
- Volume
- 78
- Issue
- 2
- Pages
- 87-94
- ISSN
- 0925-2738 (ISSN-L)
- OpenAlex
- W4393201605
- Cited by
- 9
- References
- 2
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 3
- FWCI
- 1.22
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Biomolecular NMR (publishedVersion)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Key Research and Development Program of China (2018YFA0704002, 2018YFE0202300)
- National Natural Science Foundation of China (21991081, 21921004, 21974149)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Spectroscopy › Advanced NMR Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Molecular spectroscopy and chirality
- Keywords
- random forest, model-independent analysis, conformational entropy
- MeSH terms
- Machine Learning, Protein Conformation, Proteins, Nuclear Magnetic Resonance, Biomolecular
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