ArticleProceedings of the National Academy of Sciences of the United States of America2024
Unraveling dynamic protein structures by two-dimensional infrared spectra with a pretrained machine learning model.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multidimensional Infrared Spectroscopy as a Bioanalytical Technique.Chemical reviews · 2026Review
- End-to-end molecular structure elucidation from multimodal NMR spectra images using vision transformers.Chemical science · 2026Article
- Physics-informed deep learning for plasmonic sensing of nanoscale protein dynamics in solution.Science advances · 2025Article
- Unraveling dynamic protein structures by two-dimensional infrared spectra with a pretrained machine learning model.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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6 authors.
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Abstract
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.
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