An inter-residue network model to identify mutational-constrained regions on the Ebola coat glycoprotein

• Main Authors: Quinlan, Devin Scott (Contributor), Raman, Rahul (Contributor), Tharakaraman, Kannan (Contributor), Subramanian, Vidya (Contributor), del Hierro, Gabriella A. (Contributor), Sasisekharan, Ram (Contributor)
• Other Authors: Harvard University- (Contributor), Massachusetts Institute of Technology. Department of Biological Engineering (Contributor), Massachusetts Institute of Technology. Department of Chemical Engineering (Contributor)
• Format: Article
• Language: English
• Published: Nature Publishing Group, 2017-06-21T13:14:50Z.
• Subjects: Article
• Online Access: Get fulltext

Recently, progress has been made in the development of vaccines and monoclonal antibody cocktails that target the Ebola coat glycoprotein (GP). Based on the mutation rates for Ebola virus given its natural sequence evolution, these treatment strategies are likely to impose additional selection pressure to drive acquisition of mutations in GP that escape neutralization. Given the high degree of sequence conservation among GP of Ebola viruses, it would be challenging to determine the propensity of acquiring mutations in response to vaccine or treatment with one or a cocktail of monoclonal antibodies. In this study, we analyzed the mutability of each residue using an approach that captures the structural constraints on mutability based on the extent of its inter-residue interaction network within the three-dimensional structure of the trimeric GP. This analysis showed two distinct clusters of highly networked residues along the GP1-GP2 interface, part of which overlapped with epitope surfaces of known neutralizing antibodies. This network approach also permitted us to identify additional residues in the network of the known hotspot residues of different anti-Ebola antibodies that would impact antibody-epitope interactions.
National Institutes of Health (U.S.) (MERIT award R37 GM057073-13)
National Institutes of Health (U.S.) (Research Project Grant 1R01AI111395-01)