Our long-term goal is to provide a solution to the protein-ligand binding affinity and pose prediction problems. The protein-ligand docking and scoring problem is one of the central problems in computational biology because of its importance in understanding intermolecular interactions, and because of its practical payoff. The transformative impact molecular docking and scoring can have in the design of next generation medicines cannot be overstated. If we could routinely and accurately design molecules using these approaches it would revolutionize drug discovery by winnowing out compounds with no activity while focusing more effort and scrutiny on highly active compounds. In this proposal we describe a novel method we call Movable Type (MT), which addresses the protein ligand binding and scoring problem using fundamental statistical mechanics combined with a novel way to generate the ensemble of a ligand in a protein binding pocket. Via a rapid assembly of the necessary partition functions we directly obtain binding free energies and the low free energy poses. Conceptually, the MT method is analogous to block and type set printing, which allows us to efficiently evaluate partition functions describing regions or systems of interest. In this approach we construct two databases that 1) describe the probability of certain pairwise interactions as a function of r obtained from a knowledge base (Protein Databank (PDB) or the Cambridge Structural Database (CSD)) and 2) the energetics of the pairwise interactions as a function of r obtained from empirical potentials, which can be either derived from the probabilities or can utilize extant pairwise potentials like AMBER. Overall, the MT method is a general one and can use a broad range of two-body potential functions and can be extended to higher-order interactions if so desired. In the present project we will extend and further validat the MT method and develop commercial quality software to deliver this methodology to users via the web and GUI. This will involve collaboration between the academic laboratory and the industrial laboratory, development of a new implementation of the method in order to commercially deploy the technology, construction of a graphical user interface based on MOE along with a web-based interface, and finally use of this software in real life structure-based drug discovery problems on-site with our pharmaceutical collaborators (see Letters of Support).
The successful completion of the SBIR grant will have a major impact on improving human health. It will improve the quality of protein structures, facilitate our understanding of bio-molecular structure and function and will provide higher quality structural insights into protein/ligand (drug) interactions which will enhance our ability o rationally design novel therapeutics for human diseases.
