Data from: Physics-informed deep learning for plasmonic sensing of nanoscale protein dynamics in solution

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 waves (s-CFW) informed convolutional
neural network (CNN) to address these limitations. Our sensor enables
direct probing of the amide I band in sub-10 nm proteins. By employing
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—over twice as accurate as a pristine CNN.
Our method enables in-situ and real-time quantification of subtle
conformational changes during protein assembly, thereby addressing the
issue of data scarcity that currently hinders the development of advanced
deep learning models for predicting protein dynamics and interactions in
physiological environments.