Accurate epitope prediction is important for the development of antibody-based therapies. When multiple new antibodies are discovered against the whole antigen, their epitopes and, therefore, potential novelty and mechanism of action are usually unknown Site-directed mutagenesis, the routine method for epitope mapping, requires 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 antibody. Our starting point is PIPER, a protein-protein docking program licensed by Acpharis from Boston University. PIPER is the docking engine in the software packages BioLuminate by Schrodinger and the CyrusBench Suite of Cyrus Biotechnology, as well as in the public server ClusPro. PIPER has a special option for antibody-antigen docking, and has been used for epitope prediction. However, in its present form the software generally results in a high number of putative epitopes, and more accurate prediction requires substantial experimental efforts, e.g., by site-directed mutagenesis. We 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 structures. The number of potential epitopes will be further reduced by a template-based approach based on vector contact maps to characterize antibody-antigen interfaces. We also explore predicting the epitope based on models of the CDR regions. Generating large ensembles of docked structures with a large variety of CDR conformations will reduce the sensitivity of the method to inevitable modeling and docking uncertainty. By increasing the reliability of the predicted epitopes, we expect to reduce or even to eliminate the need for mutagenesis experiments. Finally, we will develop a machine- learning algorithm for the mapping of amino acid composition of CDR regions into epitope composition, a method that can be used when only the antibody sequence available and structure prediction is uncertain due to the lack of suitable templates.
Public Health Relevance Statement: When multiple new antibodies are discovered against a whole antigen, their epitopes and, therefore, potential novelty and mechanism of action are usually unknown. Computational software tools can significantly reduce experimental efforts required for epitope determination by guiding experimental efforts toward most probable epitope locations. We propose new computational capabilities based on protein-protein docking and machine learning, which are specifically designed to improve the accuracy of epitope prediction.
Project Terms: Address; Amino Acid Sequence; Amino Acids; Antibodies; Antibody Binding Sites; Antibody Therapy; Antigen-Antibody Complex; Antigens; artificial neural network; B-Lymphocyte Epitopes; base; Base Sequence; Biotechnology; Boston; Collection; Computer software; Consensus; Crystallization; Data; Data Set; Databases; design; Development; Docking; Epitope Mapping; Epitopes; experimental study; Goals; improved; Location; Machine Learning; machine learning algorithm; Maps; Measures; Methodology; Methods; Modeling; Molecular Conformation; Mutagenesis; mutant; Probability; programs; Property; Proteins; relating to nervous system; Scheme; Site-Directed Mutagenesis; Software Tools; Specific qualifier value; Structure; Surface Antigens; Techniques; Testing; Training; Uncertainty; Universities; vector; Vertebral column
