Predicting the conformations of the silk protein through deep learning†

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.

Automated summary

In this study, a set of convolutional neural network-based deep learning models were developed to identify silk proteins and evaluate their conformation from FTIR spectra, showing higher accuracy and efficiency compared to conventional methods. The researchers also provided an open-source and user-friendly Python program for users to analyze their own FTIR data sets, encouraging interested researchers to utilize the CNN models.