Mapping a Neutralizing Epitope on the SARS Coronavirus Spike Protein: Computational Prediction Based on Affinity-selected Peptides

Rapid elucidation of neutralizing antibody epitopes on emerging viral pathogens like severe acute respiratory syndrome (SARS) coronavirus (CoV) or highly pathogenic avian influenza H5N1 virus is of great importance for rational design of vaccines against these viruses. Here we combined screening of phage display random peptide libraries with a unique computer algorithm "Mapitope" to identify the discontinuous epitope of 80R, a potent neutralizing human anti-SARS monoclonal antibody against the spike protein. Using two different types of random peptide libraries which display cysteine-constrained loops or linear 13-15-mer peptides, independent panels containing 42 and 18 peptides were isolated, respectively. These peptides, which had no apparent homologous motif within or between the peptide pools and spike protein, were deconvoluted into amino acid pairs (AAPs) by Mapitope and the statistically significant pairs (SSPs) were defined. Mapitope analysis of the peptides was first performed on a theoretical model of the spike and later on the genuine crystal structure. Three clusters (A, B and C) were predicted on both structures with remarkable overlap. Cluster A ranked the highest in the algorithm in both models and coincided well with the sites of spike protein that are in contact with the receptor, consistent with the observation that 80R functions as a potent entry inhibitor. This study demonstrates that by using this novel strategy one can rapidly predict and identify a neutralizing antibody epitope, even in the absence of the crystal structure of its target protein.