An expanded view of ligandability in the allosteric enzyme PTP1B from computational reanalysis of large-scale crystallographic data.
Tamar Skaist MehlmanHelen Mary GinnDaniel A KeedyPublished in: bioRxiv : the preprint server for biology (2024)
The recent advent of crystallographic small-molecule fragment screening presents the opportunity to obtain unprecedented numbers of ligand-bound protein crystal structures from a single high-throughput experiment, mapping ligandability across protein surfaces and identifying useful chemical footholds for structure-based drug design. However, due to the low binding affinities of most fragments, detecting bound fragments from crystallographic datasets has been a challenge. Here we report a trove of 65 new fragment hits across 59 new liganded crystal structures for PTP1B, an "undruggable" therapeutic target enzyme for diabetes and cancer. These structures were obtained from computational analysis of data from a large crystallographic screen, demonstrating the power of this approach to elucidate many (∼50% more) "hidden" ligand-bound states of proteins. Our new structures include a fragment hit found in a novel binding site in PTP1B with a unique location relative to the active site, one that validates another new binding site recently identified by simulations, one that links adjacent allosteric sites, and, perhaps most strikingly, a fragment that induces long-range allosteric protein conformational responses via a previously unreported intramolecular conduit. Altogether, our research highlights the utility of computational analysis of crystallographic data, makes publicly available dozens of new ligand-bound structures of a high-value drug target, and identifies novel aspects of ligandability and allostery in PTP1B.
Keyphrases
- small molecule
- protein protein
- high throughput
- high resolution
- electronic health record
- big data
- amino acid
- type diabetes
- data analysis
- squamous cell carcinoma
- emergency department
- papillary thyroid
- single cell
- machine learning
- gene expression
- adverse drug
- staphylococcus aureus
- deep learning
- rna seq
- young adults
- squamous cell
- cystic fibrosis
- drug induced
- weight loss
- quantum dots
- lymph node metastasis