The study of protein/ligand binding is one of the central problems in computational biology because of its importance in understanding intermolecular interactions, and because of its practical payoff in drug discovery efforts. The transformative impact accurate target/ligand structure 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. Determining the structure of a small molecule (drug candidate or lead compound) bound to a biological receptor (protein implicated in disease) is a necessary step in this approach to drug discovery. In this proposal we describe a novel method we call MovableType (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. Recent work with the MT method has led to the launch of three core product modules: MTScore (both endstate and ensemble binding affinity prediction), MTDock (ligand placement), and MTCS (ligand conformational search). In this project, we will extend our MT product line and deliver this methodology to X-ray crystallographers and computational chemists for use in automated sidechain rotamer and target loop sampling within and around the active site, accurate binding affinity prediction and minima selection, and crystallographic density matching and placement. This work will involve development of a new, integrated tool for automated structure/model preparation, rotamer/loop selection, rotamer/loop generation (?MTFlex proper?), loop/totamer minimization, and analysis. We will commercially deploy the technology, which we will call MTFlex, construct graphical user interfaces for use in MOE, Phenix, and our web-based cloud platform. Finally, this software will be used in real life structure-based drug discovery problems 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 biomolecular structure and function and will provide higher quality structural insights into protein/ligand (drug) interactions which will enhance our ability to rationally design novel therapeutics for human diseases.
