Identifying small-molecule inhibitors of protein interactions has traditionally presented a challenge for modern screening methods, despite interest stemming from the fact that such interactions comprise the underlying mechanisms for cell proliferation, differentiation, and survival. The objective of this application is to employ insights from computational methodology we have recently developed to address the distinct challenges associated with finding inhibitors of different classes of protein surface. Our central hypothesis is that exploring protein fluctuations leading to formation of surface pockets is criticl for understanding the regions of chemical space in which suitable inhibitory compounds may be found. We propose to meet our objective by pursuit of the following three specific aims: 1) Apply pocket optimization for selecting and characterizing protein targets. 2) Employ protein-ligand complementarity to build libraries enriched in protein interface inhibitors. 3) Extend these tools to allosteric inhibitors of protein interfaces. The proposed research is innovative in its ue of insight from protein fluctuations to identify binding pockets. By first confirming the ability o a target protein to form a suitable pocket and second assembling a complementary library, we collectively address the two main hurdles outlined above that have hitherto hindered identification of small molecules that directly inhibit protein-protein interactions. By combining this approach with in vitro biochemical screening, we expect to identify novel inhibitors of protei interactions involving each of three well-validated cancer targets: Bcl-xL, survivin, and b- TrCP.
This research is expected to have an important positive impact because it will provide new insights and tools to address the distinct challenges associated with finding small-molecule inhibitors of protein interactions. This contribution is significant because protein interactions have traditionally represented challenging targets for modern screening methods, despite interest stemming from the fact that such interactions comprise the underlying mechanisms for cell proliferation, differentiation, and survival.
|Malhotra, Shipra; Karanicolas, John (2017) When Does Chemical Elaboration Induce a Ligand To Change Its Binding Mode? J Med Chem 60:128-145|
|Dickson, Alex; Bailey, Christopher T; Karanicolas, John (2017) Optimal allosteric stabilization sites using contact stabilization analysis. J Comput Chem 38:1138-1146|
|Bazzoli, Andrea; Karanicolas, John (2017) ""Solvent hydrogen-bond occlusion"": A new model of polar desolvation for biomolecular energetics. J Comput Chem 38:1321-1331|
|Gowthaman, Ragul; Miller, Sven A; Rogers, Steven et al. (2016) DARC: Mapping Surface Topography by Ray-Casting for Effective Virtual Screening at Protein Interaction Sites. J Med Chem 59:4152-70|
|Johnson, David K; Karanicolas, John (2016) Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein-Protein Interactions. J Chem Inf Model 56:399-411|
|Gowthaman, Ragul; Lyskov, Sergey; Karanicolas, John (2015) DARC 2.0: Improved Docking and Virtual Screening at Protein Interaction Sites. PLoS One 10:e0131612|
|Bazzoli, Andrea; Kelow, Simon P; Karanicolas, John (2015) Enhancements to the Rosetta Energy Function Enable Improved Identification of Small Molecules that Inhibit Protein-Protein Interactions. PLoS One 10:e0140359|
|Johnson, David K; Karanicolas, John (2015) Selectivity by small-molecule inhibitors of protein interactions can be driven by protein surface fluctuations. PLoS Comput Biol 11:e1004081|
|Khar, Karen R; Goldschmidt, Lukasz; Karanicolas, John (2013) Fast docking on graphics processing units via Ray-Casting. PLoS One 8:e70661|
|Gowthaman, Ragul; Deeds, Eric J; Karanicolas, John (2013) Structural properties of non-traditional drug targets present new challenges for virtual screening. J Chem Inf Model 53:2073-81|
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