Computer modeling of protein-protein interactions plays an increasingly important role in studies of biologics.  This work presents a novel algorithm for predicting likely antigen epitopes from protein-protein docking results using the MOE software package.  Protein structures are represented by a coarse-grained bead model which accurately reproduces the interaction energies of the corresponding all-atom model.  An additional aggregation scoring term is provided by mapping hydrophobic patches derived from surface analysis onto the model structure.  Automated sequence annotation is used to identify the antibody complementarity-determining regions to further constrain the docked poses.  This approach generates an ensemble of poses which represent the most favorable interactions.  Protein-protein residue contacts are then used to generate interaction fingerprints which serve to identify Boltzmann-weighted clusters of poses and extract consensus epitope residues.  This method produces at least one predicted epitope with significant overlap to the native structure ranked in the top five clusters in over 50% of test cases.

Predicting protein-protein binding sites and epitope mapping

256th National Meeting (2018)

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