Single-image super-resolution improvement of X-ray single-particle diffraction images using convolutional neural network

Femtosecond X-ray pulse lasers are promising probes for elucidating the multi-conformational states of biomolecules because they enable snapshots of single biomolecules to be observed as coherent diffraction images. Multi-image processing using an X-ray free electron laser has proven to be a successful structural analysis method for viruses. However, some difficulties remain in single-particle analysis (SPA) for flexible biomolecules with sizes of 100 nm or less. Owing to the multi-conformational states of biomolecules and the noisy character of diffraction images, diffraction image improvement by multi-image processing is not always effective for such molecules. Here, a single-image super-resolution (SR) model was constructed using a SR convolutional neural network (SRCNN). Data preparation was performed in silico to consider the actual observation situation with unknown molecular orientations, and fluctuation of molecular structure and incident X-ray intensity. It was demonstrated that the trained SRCNN model improved the single-particle diffraction image quality, which corresponded to an observed image with an incident X-ray intensity; i.e., approximately three to seven times higher than the original X-ray intensity, while retaining the individuality of the diffraction images. The feasibility of SPA for flexible biomolecules with sizes of 100 nm or less was dramatically increased by introducing the SRCNN improvement at the beginning of the variety structural analysis schemes.