Dataset Information

Protein protein interaction inhibition (2P2I) combining high throughput and virtual screening: Application to the HIV-1 Nef protein.

ABSTRACT: Protein-protein recognition is the cornerstone of multiple cellular and pathological functions. Therefore, protein-protein interaction inhibition (2P2I) is endowed with great therapeutic potential despite the initial belief that 2P2I was refractory to small-molecule intervention. Improved knowledge of complex molecular binding surfaces has recently stimulated renewed interest for 2P2I, especially after identification of "hot spots" and first inhibitory compounds. However, the combination of target complexity and lack of starting compound has thwarted experimental results and created intellectual barriers. Here we combined virtual and experimental screening when no previously known inhibitors can be used as starting point in a structure-based research program that targets an SH3 binding surface of the HIV type I Nef protein. High-throughput docking and application of a pharmacophoric filter on one hand and search for analogy on the other hand identified drug-like compounds that were further confirmed to bind Nef in the micromolar range (isothermal titration calorimetry), to target the Nef SH3 binding surface (NMR experiments), and to efficiently compete for Nef-SH3 interactions (cell-based assay, GST pull-down). Initial identification of these compounds by virtual screening was validated by screening of the very same library of compounds in the cell-based assay, demonstrating that a significant enrichment factor was attained by the in silico screening. To our knowledge, our results identify the first set of drug-like compounds that functionally target the HIV-1 Nef SH3 binding surface and provide the basis for a powerful discovery process that should help to speed up 2P2I strategies and open avenues for new class of antiviral molecules.

SUBMITTER: Betzi S

PROVIDER: S-EPMC2148277 | biostudies-literature | 2007 Dec

REPOSITORIES: biostudies-literature

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Publications

Protein protein interaction inhibition (2P2I) combining high throughput and virtual screening: Application to the HIV-1 Nef protein.

Betzi Stéphane S Restouin Audrey A Opi Sandrine S Arold Stefan T ST Parrot Isabelle I Guerlesquin Françoise F Morelli Xavier X Collette Yves Y

Proceedings of the National Academy of Sciences of the United States of America 20071127 49

Protein-protein recognition is the cornerstone of multiple cellular and pathological functions. Therefore, protein-protein interaction inhibition (2P2I) is endowed with great therapeutic potential despite the initial belief that 2P2I was refractory to small-molecule intervention. Improved knowledge of complex molecular binding surfaces has recently stimulated renewed interest for 2P2I, especially after identification of "hot spots" and first inhibitory compounds. However, the combination of targ ...[more]

PMID: 18042718

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Project description:Over the past decade, protein-protein interactions have emerged as attractive but challenging targets for therapeutic intervention using small molecules. Due to the relatively flat surfaces that typify protein interaction sites, modern virtual screening tools developed for optimal performance against "traditional" protein targets perform less well when applied instead at protein interaction sites. Previously, we described a docking method specifically catered to the shallow binding modes characteristic of small-molecule inhibitors of protein interaction sites. This method, called DARC (Docking Approach using Ray Casting), operates by comparing the topography of the protein surface when "viewed" from a vantage point inside the protein against the topography of a bound ligand when "viewed" from the same vantage point. Here, we present five key enhancements to DARC. First, we use multiple vantage points to more accurately determine protein-ligand surface complementarity. Second, we describe a new scheme for rapidly determining optimal weights in the DARC scoring function. Third, we incorporate sampling of ligand conformers "on-the-fly" during docking. Fourth, we move beyond simple shape complementarity and introduce a term in the scoring function to capture electrostatic complementarity. Finally, we adjust the control flow in our GPU implementation of DARC to achieve greater speedup of these calculations. At each step of this study, we evaluate the performance of DARC in a "pose recapitulation" experiment: predicting the binding mode of 25 inhibitors each solved in complex with its distinct target protein (a protein interaction site). Whereas the previous version of DARC docked only one of these inhibitors to within 2 Å RMSD of its position in the crystal structure, the newer version achieves this level of accuracy for 12 of the 25 complexes, corresponding to a statistically significant performance improvement (p < 0.001). Collectively then, we find that the five enhancements described here--which together make up DARC 2.0--lead to dramatically improved speed and performance relative to the original DARC method.

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.

Dataset Information

Protein protein interaction inhibition (2P2I) combining high throughput and virtual screening: Application to the HIV-1 Nef protein.

Publications

Protein protein interaction inhibition (2P2I) combining high throughput and virtual screening: Application to the HIV-1 Nef protein.

Similar Datasets

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