DOI: 10.5061/dryad.qjq2bvqtd
title: Data from: Physics-informed deep learning for plasmonic sensing of nanoscale protein dynamics in solution

description: 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.
description: # Data from: Physics-informed deep learning for plasmonic sensing of
 nanoscale protein dynamics in solution Dataset DOI:
 [10.5061/dryad.qjq2bvqtd](10.5061/dryad.qjq2bvqtd) Open Source datasets
 for the manuscript titled "Physics-informed Deep Learning for
 Plasmonic Sensing of Nanoscale Protein Dynamics in Solution". This
 data consists of the raw data of the images and the code involved in the
 article. It contains raw data from nano-fabriction experiments to
 simulations, and the CNN models. ## Description of the data and file
 structure We describe our raw data in the order in which each image
 appears in the main text. The final dataset includes: 1. Experimental
 spectra (results of infrared spectrum measurement of SNFs) 2. Simulated
 spectra (simulated data of proteins under different parameters) 3. Spectra
 after data augmentation (with noise addition and s-CFW processing) 4. Data
 generated by the CNN model (prediction results and loss values during the
 training process) 5. Device and sample characterization data (AFM
 characterization results) 6. A compressed package of CNN datasets (all
 processing code) #### Raw data naming
 format:
sourcedata_FigureNumber_Subnumber.xlsx Comprehensive
 examples: 1. sourcedata_Fig.1B_C_D.xlsx sourcedata - indicates this is a
 source data to plot figures Fig.1 - indicated this is the source data from
 Figure 1 B_C_D - the origin data of figure subnumber B, C, and D 2.
 sourcedata_Fig.S1A_B_C_D_E.xlsx sourcedata - indicates this is a source
 data to plot figures Fig.S1 - indicated this is the source data from
 Supplymentry Figure S1 A_B_C_D_E - the origin data of figure subnumber A,
 B, C, D, and E *Note: The datasets are listed vertically (or column-wise)
 within our sourcedata*.xlsx files. For data processing scenarios (e.g.,
 spreadsheet analysis, code reading), "column-wise arrangement"
 specifically refers to data distribution in columns (as opposed to
 "row-wise" for horizontal distribution), which should be noted
 when importing or parsing the data. The folder
 "sourcedata_CNN.zip" contains all source code files used in this
 study. Within this directory, the file "README_CNN.docx"
 provides detailed explanations of each Python script (.py), including: 1.
 the specific function of each script 2. the purpose of the test files 3.
 descriptions of the model-generated weight parameters ## Sharing/Access
 Information All data is available here. If anything appears to be missing,
 please contact the corresponding author (Chenchen Wu). ## Code/software
 All our CNN model codes are run and computed using Python. Please refer to
 GitHub for the complete code with train/test/prediction dataset:
 [https://github.com/syY6777/Predicting-protein-secondary-structure.git](https://github.com/syY6777/Predicting-protein-secondary-structure.git) ## Human subjects data We state that our received explicit consent from our participants to publish the de-identified data in the public domain. ## Human subjects data We state that our received explicit consent from our participants to publish the de-identified data in the public domain.