AI protocol for retrieving protein dynamic structures from two-dimensional infrared spectra
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.
Article Details
Journal Info
Proceedings of the National Academy of Sciences
National Academy of Sciences
Authors (10)
Sheng Ye
School of Artificial Intelligence
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
Zhicheng Zhao
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
Fan Wu
Zhipeng Li
Beijing Huairou Laboratory
Binbin Wang
Department of Chemistry, Key Laboratory of Surface & Interface Science of Polymer Materials of Zhejiang Province
Kai Zhong
Zernike Institute for Advanced Materials, Department of Nanoscience and Materials Science, University of Groningen
Changyin Sun
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
Shaul Mukamel
Department of Chemistry, University of California
Jun Jiang
State Key Laboratory of Precision and Intelligent Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science