Computational characterization of B-cell epitopes

Characterizing B-cell epitopes is a fundamental step for understanding the immunological basis of bio-recognition. To date, epitope analyses have either been based on limited structural data, or sequence data alone. In this study, our null hypothesis was that the surface of the antigen is homogeneously antigenic. To test this hypothesis, a large dataset of antibody–antigen complex structures, together with crystal structures of the native antigens, has been compiled. Computational methods were developed and applied to detect and extract physico-chemical, structural, and geometrical properties that may distinguish an epitope from the remaining antigen surface. Rigorous statistical inference was able to clearly reject the null hypothesis showing that epitopes are distinguished from the remaining antigen surface in properties such as amino acid preference, secondary structure composition, geometrical shape, and evolutionary conservation. Specifically, epitopes were found to be significantly enriched with tyrosine and tryptophan, and to show a general preference for charged and polar amino acids. Additionally, epitopes were found to show clear preference for residing on planar parts of the antigen that protrude from the surface, yet with a rugged surface shape at the atom level. The effects of complex formation on the structural properties of the antigen were also computationally characterized and it is shown that epitopes undergo compression upon antibody binding. This correlates with the finding that epitopes are enriched with unorganized secondary structure elements that render them flexible. Thus, this study extends the understanding of the underlying processes required for antibody binding, and reveals new aspects of the antibody–antigen interaction.