Human health has benefited tremendously from the therapeutic application of monoclonal antibodies (mAb), treating painful and devastating diseases such as rheumatoid arthritis and cancer, among others. However, mAb development is a laborious and time consuming process. The health benefits gained from faster mAb development are clear, creating a great need for tools to guide scientists toward discovering the most promising antigenic targets-particularly with regard to B-cell epitopes (the part of an antigen recognized by an antibody). The critical barrier to progress in this domain is the inability to deduce the conformational characteristics of protein sequence in the absence of known structure for predicting linear B-cell epitopes-the largest, most diverse, and pharmaceutically valuable class of known epitopes. The general criticism of existing prediction methods is that they are inaccurate and do not address the conformational nature of B-cell epitopes. DNASTAR proposes to create a software pipeline that guides the prediction of B-cell epitopes, models the dynamic structural interface between a monoclonal antibody and its experimentally identified antigen, and screens in silico site-directed mutations to engineer more potent antibodies with enhanced binding affinity. The Phase I goal is to improve the prediction of antigenic peptides from target protein sequences and experimental or predicted structures. Toward this goal, DNASTAR has established collaborations with experts in monoclonal antibody production, 3D structure prediction, and protein structure and dynamics, including access to their experimental methods, data, and software tools. Our predictive models will benefit from three key innovations: 1) a superior data set and professional insights into monoclonal antibody production, 2) the introduction of state of the art 3D structure prediction for training our epitope predictors, and 3) the first use of structure-based protein dynamics in B-cell epitope prediction. At the conclusion of Phase I, we will deliver an enhanced sequence-only B-cell epitope prediction model when compared to current top prediction methods (Aim 1) and a superior sequence and structure-based epitope prediction model using 3D structure prediction and protein dynamics (Aim 2). In creating these models, we will account for the chemical and physical properties of a protein sequence and the biophysics that mediate protein-protein interactions, including solvent accessibility, hydrogen bonding, residue flexibility, binding nuclei, and geometric contours of the molecular surface. The proposed software pipeline will be built upon Protean 3D, our new molecular structure and simulation viewer, and will elevate the technical capability of a broad range of experimental scientists to estimate key antigenic structural properties from proteins without known structure-all on their desktop computer. Upon achieving these aims, scientists will recognize that it is no longer adequate to describe B-cell epitopes using amino acid frequencies or propensity scales alone.
Monoclonal antibodies are invaluable tools for diagnosing and treating human diseases. Unfortunately, the experimental methods used today to identify the most promising immunogenic targets are time consuming and less than totally effective. By taking the novel approach of incorporating both protein sequence information and structural features derived from high quality 3D structure predictions within our desktop computer software product, we propose to advance the ability of a broad range of life scientists to properly predict B-cell epitopes (the part of an antigen recognized by an antibody) applicable to their area of interest. This will accelerate the discovery of new monoclonal antibody pharmaceuticals, leading to improved human health across many diseases.