Computational identification of antibody-binding epitopes from mimotope datasets

A fundamental challenge in computational vaccinology is that most B-cell epitopes are conformational, and therefore harder to predict. Another significant challenge is that a great deal of the amino acid sequence of a viral surface protein might not in fact be antigenic. Therefore, identifying the regions of a protein that are most promising for vaccine design based on the degree of surface exposure may not lead to a clinically relevant immune response. Linear peptides selected by phage display experiments that have high affinity to the monoclonal antibody of interest usually have similar physicochemical properties to the antigen epitope corresponding to that antibody. The sequences of these linear peptides can be used to find possible epitopes on the surface of the antigen structure or a homology model of the antigen in the absence of an antigen-antibody complex structure. Herein we describe two novel methods for mapping mimotopes to epitopes. The first is an ensemble approach, which combines the prediction results from two existing methods. The second is a novel algorithm named MimoTree, that allows for gaps in the epitopes on the antigen to which the mimotopes match. MimoTree is a fully automated epitope detection algorithm suitable for the identification of conformational as well as linear epitopes.2025-01-15T00:00:00