X-ray crystallography experimental data screening based on convolutional neural network algorithms

Abstract

BackgroundSerial X-ray crystallography has developed rapidly due to its advantages of data collection at room temperature, low radiation damage and time resolution. To solve protein structures by using the serial X-ray crystallography, a large amount of produced diffraction data needs to be screened for finding the effective diffraction patterns. The use of convolutional neural networks (CNN) can not only automate the data screening process, but also improve the accuracy of data classification comparing with the traditional "point finding method".PurposeThis study aims to explore five types of popular convolutional neural networks, i.e., AlexNet, GoogleNet, MobileNets, Vgg16, ResNet, for screening crystallographic diffraction patterns, and compare the accuracy and efficiency of them to build up a fast and accurate convolutional neural network tool for screening the diffraction patterns of different protein crystal samples.MethodsFirstly, the primitive data for model training extracted from the coherent X-ray image database, collected by Linac Coherent Light Source (LCLS) and Spring-8 Angstrom Compact free electron laser (SACLA), were pre-processed by gray level equalization and data enhancement. The deep learning models were trained by iteration of the preprocessed data. Then, the selected convolutional neural network through the comparison of accuracy and efficiency was used to process further the experimental data of protein crystals diffractions.ResultsThe results show that MobileNets not only has the accuracy similar to large networks such as ResNet, GoogleNet-Inception, but also runs faster.ConclusionsMobileNets provides an effective and convenient screening tool for serial X-ray crystallography experimental data.

Keywords

• serial x-ray crystallography
• convolutional neural network
• machine learning
• mobilenets