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

• 成果类型: Article
• 署名作者: Ye, Sheng; Zhu, Lvshuai; Zhao, Zhicheng; Wu, Fan; Li, Zhipeng; Wang, Binbin; Zhong, Kai; Sun, Changyin; Mukamel, Shaul; Jiang, Jun
• 署名单位: Anhui University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Groningen; University of California System; University of California Irvine; University of California System; University of California Irvine
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-13852
• DOI: 10.1073/pnas.2424078122
• 发表日期: 2025-02-18
• 关键词: particle cryo-em; structure prediction; spectroscopy; insights; nmr

摘要

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.