Unraveling dynamic protein structures by two- dimensional infrared spectra with a pretrained machine learning model

• 成果类型: Article
• 署名作者: Wu, Fan; Huang, Yan; Yang, Guokun; Ye, Sheng; Mukamel, Shaul; Jiang, Jun
• 署名单位: Chinese Academy of Sciences; University of Science & Technology of China, CAS; Anhui University; 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: 0027-13931
• DOI: 10.1073/pnas.2409257121
• 发表日期: 2024-07-02
• 关键词: secondary structure; spectroscopy; simulations; peptides; DESIGN; probes

摘要

Dynamic protein structures are crucial for deciphering their diverse biological functions. Two- dimensional infrared (2DIR) spectroscopy stands as an ideal tool for tracing rapid conformational evolutions in proteins. However, linking spectral characteristics to dynamic structures poses a formidable challenge. Here, we present a pretrained machine learning model based on 2DIR spectra analysis. This model has learned signal features from approximately 204,300 spectra to establish a spectrum- structure correlation, thereby tracing the dynamic conformations of proteins. It excels in accurately predicting the dynamic content changes of various secondary structures and demonstrates universal transferability on real folding trajectories spanning timescales from microseconds to milliseconds. Beyond exceptional predictive performance, the model offers attention - based spectral explanations of dynamic conformational changes. Our 2DIR-based pretrained model is anticipated to provide unique insights into the dynamic structural information of proteins in their native environments.