Development of the Movable Type free energy method for ligand placement in X-ray crystallography

Abstract The study of protein ligand binding is one of the central problems in computational biology because of its importance in understanding intermolecular interactionsand because of its practical payoff in drug discovery effortsThe transformative impact accurate target ligand structure can have in the design of next generation medicines cannot be overstatedIf 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 compoundsDetermining the structure of a small moleculedrug candidate or lead compoundbound to a biological receptorprotein implicated in diseaseis a necessary step in this approach to drug discoveryX ray techniques provide astounding insights into the structure of proteinligand complexesbut can be hampered by the resolution to which a crystal diffracts and the refinement process can be hampered by the lack of good potentials for novel small molecule compoundsWe have extended our linear scaling semiempirical quantum mechanicalQMX ray refinement approach and applied it to this field with great successThis approach has proven itself to be robust enough for routine QM based Xray refinement and it is currently being successfully marketedHoweversince refinement methods are ultimately built on optimization algorithms and do not include samplingthey all suffer from what is termed alimited radius of convergenceThereforecrystallographic workflowsautomatic and manualinclude ligand placement as part of the model building processConventional automatic procedures for ligand placement are resolution dependent and are unable to take into account the chemistry of the active siteFurtherthe ligand conformation is often so highly strained that X ray refinement aloneis unable to deduce the proper structureWhen this happenssignificant intervention on the part of the crystallographer is requiredwhich increases expense and decreases productivityIn this proposal we describe a novel method we call Movable TypeMTwhich 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 pocketVia a rapid assembly of the necessary partition functions we directly obtain binding free energies and the low free energy posesConceptuallythe MT method is analogous to block and type set printingwhich allows us to efficiently evaluate partition functions describing regions or systems of interestIn this approach we construct two databases thatdescribe the probability of certain pairwise interactions as a function of r obtained from a knowledge baseProtein DatabankPDBor the Cambridge Structural DatabaseCSDandthe energetics of the pairwise interactions as a function of r obtained from empirical potentialswhich can be either derived from the probabilities or can utilize extant pairwise potentials like AMBEROverallthe MT method is a general one and can use a broad range of twobody potential functions and can be extended to higher order interactions if so desiredIn this project we will extend the MT method and deliver this methodology to X ray crystallographers and computational chemists for use in automated ligand placement within the experimental density during X ray refinementThis work will involve development of a newautomated tool to find the active site ligand density and place the ligand within that density using the MT methodWe will commercially deploy the technologyconstruct graphical user interfaces for use in MOEPhenixand our web based cloud platformFinallythis software will be used in real life structure based drug discovery problems with our pharmaceutical collaboratorssee Letters of SupportProject Narrative We will enhance and deploy the movable type method to address the prediction of small molecule binding to drug targetsThe resultant methodology and computer program will impact our ability to rapidly and routinely identify biologically active molecules than could be useful in the treatment of a number of diseased states