ACPHARIS — Department of Health and Human Services SBIR Phase I: 400

Description

Accurate epitope prediction is important for the development of antibody based therapiesWhen multiple new antibodies are discovered against the whole antigentheir epitopes andthereforepotential novelty and mechanism of action are usually unknown Site directed mutagenesisthe routine method for epitope mappingrequires testing a large number of mutants since any part of the antigen can potentially form an epitope The goal of this proposal is developing methodology and software for the accurate computational prediction of discontinuous B cell epitopes based on the structure of an antigen and the structure or sequence of an antibodyOur starting point is PIPERa protein protein docking program licensed by Acpharis from Boston UniversityPIPER is the docking engine in the software packages BioLuminate by Schrodinger and the CyrusBench Suite of Cyrus Biotechnologyas well as in the public server ClusProPIPER has a special option for antibody antigen dockingand has been used for epitope predictionHoweverin its present form the software generally results in a high number of putative epitopesand more accurate prediction requires substantial experimental effortse gby site directed mutagenesisWe will modify PIPER to maximize the information available from the docking by generating a large ensemble of low energy docked structures and calculating a contact map rather than discrete docked structuresThe number of potential epitopes will be further reduced by a template based approach based on vector contact maps to characterize antibody antigen interfacesWe also explore predicting the epitope based on models of the CDR regionsGenerating large ensembles of docked structures with a large variety of CDR conformations will reduce the sensitivity of the method to inevitable modeling and docking uncertaintyBy increasing the reliability of the predicted epitopeswe expect to reduce or even to eliminate the need for mutagenesis experimentsFinallywe will develop a machinelearning algorithm for the mapping of amino acid composition of CDR regions into epitope compositiona method that can be used when only the antibody sequence available and structure prediction is uncertain due to the lack of suitable templates When multiple new antibodies are discovered against a whole antigentheir epitopes andthereforepotential novelty and mechanism of action are usually unknownComputational software tools can significantly reduce experimental efforts required for epitope determination by guiding experimental efforts toward most probable epitope locationsWe propose new computational capabilities based on protein protein docking and machine learningwhich are specifically designed to improve the accuracy of epitope prediction