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

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. Beyo

SUBMITTER: Wu F 

PROVIDER: S-EPMC11228460 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature