Computational mapping of proteins for the binding of ligands

Sponsor: National Institutes of Health (NIH)

Award Number: 2R01GM064700-13

PI: Sandor Vajda

The proposal requests the renewal of the grant “Computational Mapping of Proteins for the Binding of Ligands”. 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 pursue four aims. First, we 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 be used for large scale mapping calculations to answer interesting biological questions such as the existence of druggable cryptic sites in the kinome. Second, we 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 drug targets, in the process generating fragment binding data needed for improving computational methods. Third, we develop an algorithm for virtual fragment screening in order to reduce the number of fragments that need to be experimentally tested. The resulting methods will 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. Fourth, we will further improve the accuracy of mapping by explicit modeling of solvation. The method is based on decomposing the protein into flexible side chains with multiple conformers, the rest of the protein, two copies of a probe in a number of conformations, and two water molecules. We sample the interaction energies for all feasible relative orientations of all pairs using very efficient fast Fourier tranform correlation methods, and store the detailed energy grids in lookup tables, compressed using wavelet transforms. Due to the additivity of pairwise interaction energies, the energy of any conformation can be quickly evaluated by adding pre-calculated internal energy components to interaction energies, all extracted from lookup tables. The use of energy tables will speed up the calculation of partition functions, refinement of fragment positions using Monte Carlo simulations, and determining escape times by stochastic roadmap simulation. All new algorithms will be implemented in our FTMap server (http://ftmap.bu.edu), which already has over 1200 registered users.

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