Physics-informed deep learning for plasmonic sensing of nanoscale protein dynamics in solution.

ABSTRACT: Quantifying nanoscale protein secondary structure in aqueous solutions is crucial for understanding protein interactions and dynamics. Deep learning models are adept at predicting protein secondary structures, but their ability to model them in aqueous solutions is hindered by data constraints. Here, we present a mid-infrared plasmonic sensor integrated with a synthesized complex-frequency wave (s-CFW)-informed convolutional neural network (CNN) to address these limitations. The sensor enables direct probing of the amide I band in sub-10-nanometer proteins. By using s-CFW to amplify target spectral features, the developed physics-informed CNN achieves a mean relative error of less than 0.1 in predicting secondary structure percentages, more than twice as accurate as a pristine CNN. This me

SUBMITTER: Wu C 

PROVIDER: S-EPMC12466911 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature