PLoS ONE (Jan 2021)

Effect of identified non-synonymous mutations in DPP4 receptor binding residues among highly exposed human population in Morocco to MERS-CoV through computational approach.

  • Anass Abbad,
  • Latifa Anga,
  • Abdellah Faouzi,
  • Nadia Iounes,
  • Jalal Nourlil

DOI
https://doi.org/10.1371/journal.pone.0258750
Journal volume & issue
Vol. 16, no. 10
p. e0258750

Abstract

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Dipeptidyl peptidase 4 (DPP4) has been identified as the main receptor of MERS-CoV facilitating its cellular entry and enhancing its viral replication upon the emergence of this novel coronavirus. DPP4 receptor is highly conserved among many species, but the genetic variability among direct binding residues to MERS-CoV restrained its cellular tropism to humans, camels and bats. The occurrence of natural polymorphisms in human DPP4 binding residues is not well characterized. Therefore, we aimed to assess the presence of potential mutations in DPP4 receptor binding domain (RBD) among a population highly exposed to MERS-CoV in Morocco and predict their effect on DPP4 -MERS-CoV binding affinity through a computational approach. DPP4 synonymous and non-synonymous mutations were identified by sanger sequencing, and their effect were modelled by mutation prediction tools, docking and molecular dynamics (MD) simulation to evaluate structural changes in human DPP4 protein bound to MERS-CoV S1 RBD protein. We identified eight mutations, two synonymous mutations (A291 =, R317 =) and six non-synonymous mutations (N229I, K267E, K267N, T288P, L294V, I295L). Through docking and MD simulation techniques, the chimeric DPP4 -MERS-CoV S1 RBD protein complex models carrying one of the identified non-synonymous mutations sustained a stable binding affinity for the complex that might lead to a robust cellular attachment of MERS-CoV except for the DPP4 N229I mutation. The latter is notable for a loss of binding affinity of DPP4 with MERS-CoV S1 RBD that might affect negatively on cellular entry of the virus. It is important to confirm our molecular modelling prediction with in-vitro studies to acquire a broader overview of the effect of these identified mutations.