Mapping a neutralizing epitope on the SARS coronavirus spike protein: computational prediction based on...

Area of Science:

• Virology
• Immunology
• Structural Biology

Background:

• Rapidly identifying neutralizing antibody epitopes on emerging viruses like SARS-CoV and H5N1 is crucial for vaccine design.
• Existing methods can be time-consuming, especially when the target protein's structure is unknown.

Purpose of the Study:

• To develop and validate a novel computational strategy for rapid epitope identification.
• To identify the discontinuous epitope of the 80R neutralizing monoclonal antibody against the SARS-CoV spike protein.

Main Methods:

• Screening of phage display random peptide libraries (cysteine-constrained and linear).
• Deconvolution of identified peptides into amino acid pairs (AAPs) using the Mapitope algorithm.
• Analysis of AAPs against theoretical and crystal structures of the SARS-CoV spike protein.

Main Results:

• Two independent peptide panels (42 and 18 peptides) were isolated.
• Mapitope analysis predicted three clusters (A, B, C) with high overlap between theoretical and crystal structures.
• Cluster A, identified as statistically significant, corresponded to the receptor-binding site and correlated with the antibody's entry-inhibiting function.

Conclusions:

• The combined strategy of phage display and Mapitope enables rapid prediction and identification of neutralizing antibody epitopes.
• This approach is effective even without prior knowledge of the target protein's crystal structure.
• The identified epitope is critical for the 80R antibody's potent viral entry inhibition.