Useful experimental aspects of small-wedge synchrotron crystallography for accurate structure analysis of protein...

Recent advances in low-emittance synchrotron X-ray technology and highly sensitive photon-counting detectors have revolutionized protein micro-crystallography in structural biology. These developments and improvements to sample-exchange robots and beamline control have paved the way for automated and efficient unattended data collection. This study analyzed protein crystal structures such as type 2 angiotensin II receptor, CNNM/CorC membrane proteins and polyhedral protein crystals using small-wedge synchrotron crystallography (SWSX), which dramatically improves measurement efficiency through automated measurement. We evaluated the data quality using SWSX, focusing on `massive data collection'. In this context, `massive' refers to data sets with a multiplicity exceeding 100. The findings could potentially lead to the development of more efficient experimental conditions, such as obtaining high-resolution data using a smaller number of crystals. We have demonstrated that the application of machine learning, a modern key component of data science, to classify data groups is an integral part of the analysis process and may play a crucial role in improving data quality. These results indicate that SWSX is one of the essential candidates for crystal structure analysis methods for difficult-to-analyze samples: it can enable diverse and complex protein functional analysis.