AI protocol for retrieving protein dynamic structures from two-dimensional infrared spectra.

Journal: Proceedings of the National Academy of Sciences of the United States of America
PMID: 39951500

Abstract

Understanding the dynamic evolution of protein structures is crucial for uncovering their biological functions. Yet, real-time prediction of these dynamic structures remains a significant challenge. Two-dimensional infrared (2DIR) spectroscopy is a powerful tool for analyzing protein dynamics. However, translating its complex, low-dimensional signals into detailed three-dimensional structures is a daunting task. In this study, we introduce a machine learning-based approach that accurately predicts dynamic three-dimensional protein structures from 2DIR descriptors. Our method establishes a robust "spectrum-structure" relationship, enabling the recovery of three-dimensional structures across a wide variety of proteins. It demonstrates broad applicability in predicting dynamic structures along different protein folding trajectories, spanning timescales from microseconds to milliseconds. This approach also shows promise in identifying the structures of previously uncharacterized proteins based solely on their spectral descriptors. The integration of AI with 2DIR spectroscopy offers insights and represents a significant advancement in the real-time analysis of dynamic protein structures.

Authors

  • Sheng Ye
    Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
  • Lvshuai Zhu
    Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, School of AI, Anhui University, Hefei 230601, China.
  • Zhicheng Zhao
  • Fan Wu
    Department of Product Design, Dalian Polytechnic University, Dalian 116034, China.
  • Zhipeng Li
  • Binbin Wang
    Center for Genetics, National Research Institute for Family Planning, Beijing 100081, China Biozy@ict.ac.cn Nicgr@263.net.
  • Kai Zhong
    Hefei National Laboratory for Physical Sciences at the Microscale, CAS Center for Excellence in Nanoscience, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China.
  • Changyin Sun
    School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China. Electronic address: cys@ustb.edu.cn.
  • Shaul Mukamel
    Departments of Chemistry, and Physics & Astronomy, University of California, Irvine, California 92697, United States.
  • Jun Jiang
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China.

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