Biomolecules binding is influenced by many factors and its assessment constitutes a very hard challenge in computational structural biology. In this respect, the evaluation of shape complementarity at molecular interfaces is one of the key factors to be considered. Focusing on the peculiar case of antibody-antigen interaction, we designed a novel computational strategy based on in-silico mutagenesis of antibody binding site residues, where a Monte Carlo procedure aims at increasing the shape complementarity between the antibody paratope and a given epitope on a target protein surface. To quantify the complementarities occurring at the interface, we relied on a method we recently developed, which employs the 2D Zernike descriptors. To this end, we preliminary considered an experimental structural dataset of antibody-antigen complexes, where our method statistically identifies, in terms of shape complementarity, pairs of interacting regions from non-interacting ones. We thus constructed our protocol against a molecular region in the N-terminal domain of SARS-CoV-2 Spike protein, already experimentally identified in interaction with antibodies in humans. We, therefore, optimized the shape of several possible template antibodies for the interaction with such a region. Lastly, we performed an independent molecular docking validation of the results of our protocol, thus evaluating also if the mutagenesis protocol introduced residues with chemical characteristics compatible with the target region.
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