MERS-CoV Spike Protein Variants: A Computational Perspective

Abstract

Middle East Respiratory Syndrome Coronavirus (MERS-CoV), first identified in 2012, remains a significant public health concern with a case fatality rate exceeding 35%. The spike (S) protein, responsible for viral entry into host cells, represents the primary target for vaccine and therapeutic development. Understanding spike protein genetic variability is crucial for predicting viral evolution, immune escape mechanisms, and designing pan-coronavirus interventions.

Computational approaches have emerged as indispensable tools for analyzing MERS-CoV spike variants, offering rapid, cost-effective methods for global surveillance and predictive modeling. This review synthesizes current computational research on MERS-CoV spike protein variants, examining sequence-based phylogenetic analyses, structural modeling studies, epitope prediction algorithms, and machine learning approaches. We highlight how computational methods have revealed conserved regions suitable for vaccine targeting, tracked viral evolution patterns, and predicted the functional impact of mutations.

While computational studies have provided valuable insights into MERS-CoV spike protein diversity and evolution, significant gaps remain in global sampling, experimental validation of predictions, and integration of recombination events. Future directions include enhanced AI-driven surveillance systems, pan-coronavirus vaccine design platforms, and improved computational-experimental integration for accelerated antiviral development.