The research in our lab focuses on molecular recognition using computational methods and follow-up validation experiments. Our primary target areas are (1) protein-protein docking and (2) exploring binding properties of proteins by computational solvent mapping. Protein docking methods are needed because many important interactions occur in weak, transient complexes that are not amenable to direct experimental analysis. We have developed ClusPro, the best docking server currently available. While the server is heavily used, with over 350 research papers reporting models constructed by ClusPro, the methodology has several limitations. First, global docking of relatively rigid proteins usually generates structures within 10 interface RMSD from the native complex, but selecting and refining the best models frequently fail. Second, the methods are less accurate when docking peptides, proteins with flexible loops or unstructured regions, or homology models. Third, no reliable method is available for determining whether a docked structure represents a stable complex, and for calculating its binding free energy with any reasonable accuracy. Fourth, even these imperfect methods are too slow for proteome-wide analyses. We expect to address and solve all these problems. In addition, a new approach, based on pre-calculated pairwise interactions, will be developed for modeling complex systems, including aggregation and crowding effects. The second application considered in the proposal, computational solvent mapping, globally samples the surface of target proteins using fragment sized molecular probes. The general goals of mapping are determining binding hot spots, i.e., regions of proteins that are major contributors to the binding free energy, and identifying fragments with preferential binding to these hot spots. The main advantage of studying hot spots is that they are more conserved than binding sites are. We will improve the efficiency of flexible mapping by performing side chain search directly within the global mapping algorithm, and extend the algorithm to models with flexible loops and to homology models. The method will also be used for large scale mapping calculations. We will develop an effective combination of computational and experimental methods for the identification of fragments binding to a given hot spot, and working with collaborators attempt to find fragment hits for a number of important drug target proteins. The ultimate goals of this research are developing algorithms for virtual fragment screening in order to reduce the number of fragments that need to be experimentally tested, and expanding the method to provide direct input for fragment based ligand discovery (FBLD), thereby reducing the high costs of the approach and making it more accessible to academic laboratories and small companies.
Our research focuses on molecular recognition, primarily protein-protein interactions and the binding properties of proteins. The main goals are to develop reliable and robust computational methods that are based on rigorous biophysical principles and have the potential for substantially improving the efficiency of biomedical and pharmaceutical research, and to implement them as web-based servers to be validated by broad user communities.
| Zarbafian, Shahrooz; Moghadasi, Mohammad; Roshandelpoor, Athar et al. (2018) Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes. Sci Rep 8:5896 |
| Beglov, Dmitri; Hall, David R; Wakefield, Amanda E et al. (2018) Exploring the structural origins of cryptic sites on proteins. Proc Natl Acad Sci U S A 115:E3416-E3425 |
| Vajda, Sandor; Beglov, Dmitri; Wakefield, Amanda E et al. (2018) Cryptic binding sites on proteins: definition, detection, and druggability. Curr Opin Chem Biol 44:1-8 |
| Yueh, Christine; Hall, David R; Xia, Bing et al. (2017) ClusPro-DC: Dimer Classification by the Cluspro Server for Protein-Protein Docking. J Mol Biol 429:372-381 |
| Kozakov, Dima; Hall, David R; Xia, Bing et al. (2017) The ClusPro web server for protein-protein docking. Nat Protoc 12:255-278 |
| Bohnuud, Tanggis; Luo, Lingqi; Wodak, Shoshana J et al. (2017) A benchmark testing ground for integrating homology modeling and protein docking. Proteins 85:10-16 |
| Vajda, Sandor; Yueh, Christine; Beglov, Dmitri et al. (2017) New additions to the ClusPro server motivated by CAPRI. Proteins 85:435-444 |
| Im, Wonpil; Liang, Jie; Olson, Arthur et al. (2016) Challenges in structural approaches to cell modeling. J Mol Biol 428:2943-64 |
| Mamonov, Artem B; Moghadasi, Mohammad; Mirzaei, Hanieh et al. (2016) Focused grid-based resampling for protein docking and mapping. J Comput Chem 37:961-70 |
| Whitty, Adrian; Zhong, Mengqi; Viarengo, Lauren et al. (2016) Quantifying the chameleonic properties of macrocycles and other high-molecular-weight drugs. Drug Discov Today 21:712-7 |
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