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Deep learning\n models are adept at predicting protein secondary structures but their\n ability to model them in aqueous solutions is hindered by data\n constraints. Here, we present a mid-infrared plasmonic sensor integrated\n with a synthesized complex-frequency waves (s-CFW) informed convolutional\n neural network (CNN) to address these limitations. Our sensor enables\n direct probing of the amide I band in sub-10 nm proteins. By employing\n s-CFW to amplify target spectral features, the developed physics-informed\n CNN achieves a mean relative error of less than 0.1 in predicting\n secondary structure percentages—over twice as accurate as a pristine CNN.\n Our method enables in-situ and real-time quantification of subtle\n conformational changes during protein assembly, thereby addressing the\n issue of data scarcity that currently hinders the development of advanced\n deep learning models for predicting protein dynamics and interactions in\n physiological environments.","descriptionType":"Abstract"},{"description":"# Data from: Physics-informed deep learning for plasmonic sensing of\n nanoscale protein dynamics in solution Dataset DOI:\n [10.5061/dryad.qjq2bvqtd](10.5061/dryad.qjq2bvqtd) Open Source datasets\n for the manuscript titled \"Physics-informed Deep Learning for\n Plasmonic Sensing of Nanoscale Protein Dynamics in Solution\". This\n data consists of the raw data of the images and the code involved in the\n article. It contains raw data from nano-fabriction experiments to\n simulations, and the CNN models. ## Description of the data and file\n structure We describe our raw data in the order in which each image\n appears in the main text. The final dataset includes: 1. Experimental\n spectra (results of infrared spectrum measurement of SNFs) 2. Simulated\n spectra (simulated data of proteins under different parameters) 3. Spectra\n after data augmentation (with noise addition and s-CFW processing) 4. Data\n generated by the CNN model (prediction results and loss values during the\n training process) 5. Device and sample characterization data (AFM\n characterization results) 6. A compressed package of CNN datasets (all\n processing code) #### Raw data naming\n format:\nsourcedata_FigureNumber_Subnumber.xlsx Comprehensive\n examples: 1. sourcedata_Fig.1B_C_D.xlsx sourcedata - indicates this is a\n source data to plot figures Fig.1 - indicated this is the source data from\n Figure 1 B_C_D - the origin data of figure subnumber B, C, and D 2.\n sourcedata_Fig.S1A_B_C_D_E.xlsx sourcedata - indicates this is a source\n data to plot figures Fig.S1 - indicated this is the source data from\n Supplymentry Figure S1 A_B_C_D_E - the origin data of figure subnumber A,\n B, C, D, and E *Note: The datasets are listed vertically (or column-wise)\n within our sourcedata*.xlsx files. For data processing scenarios (e.g.,\n spreadsheet analysis, code reading), \"column-wise arrangement\"\n specifically refers to data distribution in columns (as opposed to\n \"row-wise\" for horizontal distribution), which should be noted\n when importing or parsing the data. The folder\n \"sourcedata_CNN.zip\" contains all source code files used in this\n study. Within this directory, the file \"README_CNN.docx\"\n provides detailed explanations of each Python script (.py), including: 1.\n the specific function of each script 2. the purpose of the test files 3.\n descriptions of the model-generated weight parameters ## Sharing/Access\n Information All data is available here. If anything appears to be missing,\n please contact the corresponding author (Chenchen Wu). ## Code/software\n All our CNN model codes are run and computed using Python. Please refer to\n GitHub for the complete code with train/test/prediction dataset:\n [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.","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"funderName":"National Key R\u0026D Program of China","awardNumber":"2023YFA1407003"},{"funderName":"Beijing Natural Foundation","awardNumber":"2254099"},{"schemeUri":"https://ror.org","funderName":"China Postdoctoral Science Foundation","awardNumber":"2024M760681","funderIdentifier":"https://ror.org/0426zh255","funderIdentifierType":"ROR"},{"funderName":"New Cornerstone Science Foundation","awardNumber":"AoE/P-502/20"},{"funderName":"Guangdong Provincial Quantum Science Strategic Initiative","awardNumber":"GDZX2204004"},{"schemeUri":"https://ror.org","funderName":"National Natural Science Foundation of China","awardNumber":"51925203; 52022025; 52102160; 51972074; U2032206; 52472155.","funderIdentifier":"https://ror.org/01h0zpd94","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Chinese Academy of Sciences","awardNumber":"XDB36000000","funderIdentifier":"https://ror.org/034t30j35","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Chinese Academy of Sciences","awardNumber":"YSBR-086","funderIdentifier":"https://ror.org/034t30j35","funderIdentifierType":"ROR"},{"funderName":"National Key R\u0026D Program of China","awardNumber":"2021YFA1201500"},{"funderName":"New Cornerstone Science Foundation","awardNumber":"STG3/E-704/23-N"},{"funderName":"New Cornerstone Science Foundation","awardNumber":"17309021"},{"funderName":"Guangdong Provincial Quantum Science Strategic Initiative","awardNumber":"GDZX2304001"},{"schemeUri":"https://ror.org","funderName":"Chinese Academy of Sciences","awardNumber":"XDB30000000","funderIdentifier":"https://ror.org/034t30j35","funderIdentifierType":"ROR"}],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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    <title>
      Data from: Physics-informed deep learning for plasmonic sensing of
      nanoscale protein dynamics in solution
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    <description descriptionType="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 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>
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      # 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:&amp;#xA;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.
    </description>
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