Issue 8, 2021

Predicting the conformations of the silk protein through deep learning

Abstract

As with other proteins, the conformation of the silk protein is critical for determining the mechanical, optical and biological performance of materials. However, an efficient, accurate and time-efficient method for evaluating the protein conformation from Fourier transform infrared (FTIR) spectra is still desired. A set of convolutional neural network (CNN)-based deep learning models was developed in this study to identify the silk proteins and evaluate their relative content of each conformation from FTIR spectra. Compared with the conventional deconvolution algorithm, our CNN models are highly accurate and time-efficient, showing promise in processing massive FTIR data sets, such as data from FTIR imaging, and in quick analysis feedback, such as on-line and time-resolved FTIR measurements. We compiled an open-source and user-friendly graphical Python program that allows users to analyze their own FTIR data set, which can be from the silk protein or other proteins, for the encouragement and convenience of interested researchers to use the CNN models.

Graphical abstract: Predicting the conformations of the silk protein through deep learning

Supplementary files

Article information

Article type
Paper
Submitted
16 févr. 2021
Accepted
05 mars 2021
First published
05 mars 2021

Analyst, 2021,146, 2490-2498

Predicting the conformations of the silk protein through deep learning

M. Jiang, T. Shu, C. Ye, J. Ren and S. Ling, Analyst, 2021, 146, 2490 DOI: 10.1039/D1AN00290B

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