Prediction of order parameters based on protein NMR structure ensemble and machine learning.

Journal: Journal of biomolecular NMR
PMID: 38530516

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 S 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 backbone H-N 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 backbone H-N 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.

Authors

  • Qianqian Wang
    School of Teacher Education, Zhejiang Normal University, Jinhua, China.
  • Zhiwei Miao
    Wuhan National Laboratory for Optoelectronics, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Xiongjie Xiao
    Wuhan National Laboratory for Optoelectronics, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Xu Zhang
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Daiwen Yang
    Department of Biological Sciences, National University of Singapore, Singapore, Singapore.
  • Bin Jiang
    Department of Urology, Chinese People's Liberation Army General Hospital, Beijing, 100039 China.
  • Maili Liu
    State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Center for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, PR China; University of Chinese Academy of Sciences, 10049 Beijing, PR China.

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