SBIR-STTR Award

The study of protein/ligand binding is one of the central problems in computational biology because of itsimportance in understanding intermolecular interactions, and because of its practical payoff in drug discoveryefforts. The transformative impact accurate target/ligand structure can have in the design of next generationmedicines cannot be overstated. If we could routinely and accurately design molecules using these approachesit would revolutionize drug discovery by winnowing out compounds with no activity while focusing more effortand scrutiny on highly active compounds.In this proposal we describe a novel method we call MovableType (MT) that for the first time will be coupled withcutting edge enhanced molecular dynamics (MD) methods (e.g., Simulated Tempering, Accelerated MD,Metadynamics, and Replica exchange MD) in Aims I.1 and II.1a, linear scaling quantum mechanics (forimproved electrostatics) in Aim I.2, and a new Monte Carlo sampling regime called Consecutive HistogramsMonte Carlo (CHMC) in Aim II.1b for increased speed. We expect this development to significantly expand thedomain applicability of MT in particular (and free energy methods in general) to include those situations whichrequire greater conformational sampling than can be provided by docking alone.MT addresses the protein ligand binding and scoring problem using fundamental statistical mechanics combinedwith a new way to generate the ensemble of a ligand in a protein binding pocket. Via a rapid assembly of thenecessary partition functions, with MT we directly obtain absolute binding free energies and the low free energyposes (versus most conventional free energy methods in commercial/industrial labs which usually obtain relativebinding free energies). Conceptually, the MT method is analogous to block and type set printing, which allowsus to efficiently evaluate partition functions describing regions or systems of interest. Overall, the MT method isa general one and can use a broad range of two-body potential functions and can be extended to higher-orderinteractions if so desired. Recent work with the MT method has led to the launch of three core product modules:MTScore (both end state and ensemble-based binding affinity prediction), MTDock (ligand placement), and MTCS(ligand conformational search). In this project, we will extend our MT product line by optimizing the method foruse with advanced sampling techniques and deliver this methodology to computational chemists for use in theirindustrial structure-based drug design campaigns. This work will involve development of a new, integrated toolfor automated structure/model preparation, integration with and optimization for several molecular dynamicsengines, addition an updated electrostatics engine (built on our mature, linear scaling, semi-empirical quantummechanics infrastructure), development of a new Monte Carlo method for increased speed, and cloud-baseddeployment on the GridMarkets platform (Aim II.2). Finally, in Aim II.3, we will commercially deploy thetechnology, construct graphical user interfaces for use in MOE, and validate its use in real life structure-baseddrug discovery problems with our pharmaceutical collaborators (see Letters of Support).

Public Health Relevance Statement:


Project narrative:
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.

Project Terms: