Predicting the changes in neutralizing antibody interaction with G protein derived from Bangladesh isolates of Nipah virus: molecular dynamics based approach.
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引用次数: 0
Abstract
The infectious Nipah virus (NiV) is categorized into NiV-M (Malaysia) and NiV-B (Bangladesh) groups based on its genome comparison, pathogenicity, and mortality rate. The development of therapeutic molecules has used NiV-M-derived data in multiple studies than NiV-B. In continuation with this, the protein level investigation is also less explored to understand the interaction with therapeutic neutralizing antibodies for NiV-B. So, this study focuses on understanding the impact of NiV-B-specific mutations on the interaction of therapeutic neutralizing antibodies with the G protein. The population-based comparative analysis of NiV-B G protein sequences with NiV-M sequence identified twenty-six mutations. These predominantly polar mutations were then used to model the mutant protein (G_MT). In a comparative study, the G protein G_MT and reference protein G_WT (Malaysian origin) were subjected to a protein docking with neutralizing human monoclonal antibody HENV26. The binding affinity and the free binding energy of the glycoprotein in complex with G-WT and G_MT were calculated using PRODIGY and MM/PBSA tools respectively. Based on the PRODIGY report, G-WT showed stronger binding (-13.8 kcal/mol) compared to that of the G_MT (-9.0 kcal/mol) with the HENV26 antibody. The stability of the complexes was evaluated using MM/PBSA which showed higher binding energy with HENV26 for G_WT (-75.11 kcal/mol) in contrast to G_MT (-41.66 kcal/mol). The results indicate that the mutant G protein has a reduced ability to bind to neutralizing antibodies, resulting in a decreased effectiveness against strains carrying these mutations.Communicated by Ramaswamy H. Sarma.
期刊介绍:
The Journal of Biomolecular Structure and Dynamics welcomes manuscripts on biological structure, dynamics, interactions and expression. The Journal is one of the leading publications in high end computational science, atomic structural biology, bioinformatics, virtual drug design, genomics and biological networks.