Predicting X-ray Diffraction Quality of Protein Crystals Using a Deep-Learning Method

Over the past few decades, significant advancements in protein crystallography have led to a steady increase in the number of determined protein structures. The X-ray diffraction experiment remains one of the primary methods for investigating protein crystal structures. To obtain information about crystal structures, a sufficient number of high-quality crystals are typically required. At present, X-ray diffraction experiments on protein crystals primarily rely on manual selection by experimenters. However, each experiment is not only costly but also time-consuming. To address the urgent need for automatic selection of the proper protein crystal candidates for X-ray diffraction experiments, a protein-crystal-quality classification network, leveraging the ConvNeXt network architecture, is proposed. Subsequently, a new database is created, which includes protein crystal images and their corresponding X-ray diffraction images. Additionally, a novel method for categorizing protein quality based on the number of diffraction spots and the resolution is introduced. To further enhance the network's focus on essential features of protein crystal images, a CBAM (Convolutional Block Attention Module) attention mechanism is incorporated between convolution layers. The experimental results demonstrate that the network achieves significant improvement in performing the prediction task, thereby effectively enhancing the probability of high-quality crystals being selected by experimenters.