Frontiers in Immunology (Dec 2022)

HBD-2 variants and SARS-CoV-2: New insights into inter-individual susceptibility

  • Mohammed Y. Behairy,
  • Mohamed A. Soltan,
  • Muhammad Alaa Eldeen,
  • Jawaher A. Abdulhakim,
  • Maryam M. Alnoman,
  • Mohamed M. Abdel-Daim,
  • Mohamed M. Abdel-Daim,
  • Hassan Otifi,
  • Saleh M. Al-Qahtani,
  • Mohamed Samir A. Zaki,
  • Mohamed Samir A. Zaki,
  • Ghadi Alsharif,
  • Sarah Albogami,
  • Ibrahim Jafri,
  • Eman Fayad,
  • Khaled M. Darwish,
  • Sameh S. Elhady,
  • Refaat A. Eid

DOI
https://doi.org/10.3389/fimmu.2022.1008463
Journal volume & issue
Vol. 13

Abstract

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BackgroundA deep understanding of the causes of liability to SARS-CoV-2 is essential to develop new diagnostic tests and therapeutics against this serious virus in order to overcome this pandemic completely. In the light of the discovered role of antimicrobial peptides [such as human b-defensin-2 (hBD-2) and cathelicidin LL-37] in the defense against SARS-CoV-2, it became important to identify the damaging missense mutations in the genes of these molecules and study their role in the pathogenesis of COVID-19.MethodsWe conducted a comprehensive analysis with multiple in silico approaches to identify the damaging missense SNPs for hBD-2 and LL-37; moreover, we applied docking methods and molecular dynamics analysis to study the impact of the filtered mutations.ResultsThe comprehensive analysis reveals the presence of three damaging SNPs in hBD-2; these SNPs were predicted to decrease the stability of hBD-2 with a damaging impact on hBD-2 structure as well. G51D and C53G mutations were located in highly conserved positions and were associated with differences in the secondary structures of hBD-2. Docking-coupled molecular dynamics simulation analysis revealed compromised binding affinity for hBD-2 SNPs towards the SARS-CoV-2 spike domain. Different protein–protein binding profiles for hBD-2 SNPs, in relation to their native form, were guided through residue-wise levels and differential adopted conformation/orientation.ConclusionsThe presented model paves the way for identifying patients prone to COVID-19 in a way that would guide the personalization of both the diagnostic and management protocols for this serious disease.

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