Narrow Results

CRISPR-Cas9 is a revolutionary genome-editing tool. Understanding how Cas9 recognizes DNA and how to control its function will be critical in improving the system. We used single-molecule FRET to elucidate a key validation step during DNA target recognition. We also used X-ray crystallography to show how a Cas9 inhibitor is able to permit DNA binding but prevent cleavage. Finally, CRISPR research is notable not just for the exciting applications, but also for its profound ethical implications.

There is significant interest in developing machine learning methods to model protein-ligand interactions but a scarcity of experimentally resolved protein-ligand structures to learn from. Protein self-contacts are a much larger source of structural data that could be leveraged, but currently it is not well understood how this data source differs from the target domain. Here, we characterize the 3D geometric patterns of protein self-contacts as probability distributions. We then present a flexible statistical framework to assess the transferability of these patterns to protein-ligand contacts. We observe that the level of transferability from protein self-contacts to protein-ligand contacts depends on contact type, with many contact types exhibiting high transferability. We then demonstrate the potential of leveraging information from these geometric patterns to aid in ligand pose-selection problems in protein-ligand docking. We publicly release our extracted data on geometric interaction patterns to enable further exploration of this problem.