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Label-free detection of G protein-SNARE interactions and screening for small molecule modulators.

ABSTRACT: G(i/o)-coupled presynaptic GPCRs are major targets in neuropsychiatric diseases. For example, presynaptic auto- or heteroreceptors include the D(2) dopamine receptor, H(3) histamine receptor, 5HT(1) serotonin receptors, M(4) acetylcholine receptors, GABA(B) receptors, Class II and III metabotropic glutamate receptors, opioid receptors, as well as many other receptors. These GPCRs exert their influence by decreasing exocytosis of synaptic vesicles. One mechanism by which they act is through direct interaction of the Gβγ subunit with members of the SNARE complex downstream of voltage-dependent calcium channels, and specifically with the C-terminus of SNAP25 and the H3 domain of syntaxin1A(1-3). Small molecule inhibitors of the Gβγ-SNARE interaction would allow the study of the relative importance of this mechanism in more detail. We have utilized novel, label-free technology to detect this protein-protein interaction and screen for several small molecule compounds that perturb the interaction, demonstrating the viability of this approach. Interestingly, the screen also produced enhancers of the Gβγ-SNARE interaction.

SUBMITTER: Wells CA

PROVIDER: S-EPMC3285104 | biostudies-other | 2012 Jan

REPOSITORIES: biostudies-other

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Project description:Protein-protein interactions play important roles in virtually all cellular processes, making them enticing targets for modulation by small-molecule therapeutics: specific examples have been well validated in diseases ranging from cancer and autoimmune disorders, to bacterial and viral infections. Despite several notable successes, however, overall these remain a very challenging target class. Protein interaction sites are especially challenging for computational approaches, because the target protein surface often undergoes a conformational change to enable ligand binding: this confounds traditional approaches for virtual screening. Through previous studies, we demonstrated that biased "pocket optimization" simulations could be used to build collections of low-energy pocket-containing conformations, starting from an unbound protein structure. Here, we demonstrate that these pockets can further be used to identify ligands that complement the protein surface. To do so, we first build from a given pocket its "exemplar": a perfect, but nonphysical, pseudoligand that would optimally match the shape and chemical features of the pocket. In our previous studies, we used these exemplars to quantitatively compare protein surface pockets to one another. Here, we now introduce this exemplar as a template for pharmacophore-based screening of chemical libraries. Through a series of benchmark experiments, we demonstrate that this approach exhibits comparable performance as traditional docking methods for identifying known inhibitors acting at protein interaction sites. However, because this approach is predicated on ligand/exemplar overlays, and thus does not require explicit calculation of protein-ligand interactions, exemplar screening provides a tremendous speed advantage over docking: 6 million compounds can be screened in about 15 min on a single 16-core, dual-GPU computer. The extreme speed at which large compound libraries can be traversed easily enables screening against a "pocket-optimized" ensemble of protein conformations, which in turn facilitates identification of more diverse classes of active compounds for a given protein target.

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Label-free detection of G protein-SNARE interactions and screening for small molecule modulators.

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