How does a small molecule bind at a cryptic binding site?
By Yibing Shan, Venkatesh P. Mysore, Abba E. Leffler, Eric T. Kim, Shiori Sagawa, David E. Shaw. 2021
45 citations recorded by the source corpus.
Protein-protein interactions (PPIs) are ubiquitous biomolecular processes that are central to virtually all aspects of cellular function. Identifying small molecules that modulate specific disease-related PPIs is a strategy with enormous promise for drug discovery. The design of drugs to disrupt PPIs is challenging, however, because many potential drug-binding sites at PPI interfaces are “cryptic”: When unoccupied by a ligand, cryptic sites are often flat and featureless, and thus not readily recognizable in crystal structures, with the geometric and chemical characteristics of typical small-molecule binding sites only emerging upon ligand binding. The rational design of small molecules to inhibit specific PPIs would benefit from a better understanding of how such molecules bind at PPI interfaces. To this end, we have conducted unbiased, all-atom MD simulations of the binding of four small-molecule inhibitors (SP4206 and three SP4206 analogs) to interleukin 2 (IL2)—which performs its function by forming a PPI with its receptor—without incorporating any prior structural information about the ligands’ binding. In multiple binding events, a small molecule settled into a stable binding pose at the PPI interface of IL2, resulting in a protein–small-molecule binding site and pose virtually identical to that observed in an existing crystal structure of the IL2-SP4206 complex. Binding of the small molecule stabilized the IL2 binding groove, which when the small molecule was not bound emerged only transiently and incompletely. Moreover, free energy perturbation (FEP) calculations successfully distinguished between the native and non-native IL2–small-molecule binding poses found in the simulations, suggesting that binding simulations in combination with FEP may provide an effective tool for identifying cryptic binding sites and determining the binding poses of small molecules designed to disrupt PPI interfaces by binding to such sites.
Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging
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Citations in the corpus
- State-specific protein–ligand complex structure prediction with a multiscale deep generative model (2022, positive): “Nevertheless, the computational modelling of protein–ligand complexes that are coupled to substantial changes in receptor conformation is hampered by the prohibitive cost of simulating slow protein state transitions 20,21 .”
- Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models (2022, background): “…prediction of protein-ligand structures that are coupled to receptor conformational responses is still hampered by the prohibitive cost of physically simulating slow protein state transitions [7, 8], as well as the static nature of existing protein folding prediction algorithms [9, 10].”