TITLE:
Network-Based Structural Analysis of SARS-CoV-2 Spike Variants Using Protein Structure Networks
AUTHORS:
Michelle Fuentes-Acosta, Jorge Mulia-Rodríguez, Daniel Osorio-González
KEYWORDS:
SARS-CoV-2, Spike Protein, Protein Structure Networks, Structural Bioinformatics, Graph Theory, Viral Variants
JOURNAL NAME:
Journal of Biosciences and Medicines,
Vol.14 No.4,
April
15,
2026
ABSTRACT: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a zoonotic virus responsible for the respiratory disease COVID-19. The rapid spread of the virus has led to the emergence of variants containing mutations that modify viral dynamics, increase transmissibility, and affect vaccine effectiveness. Among the structural proteins of SARS-CoV-2, the Spike glycoprotein plays a fundamental role in the infection process because it mediates recognition and binding to the human angiotensinconverting enzyme 2 (ACE2) receptor. In this work, structural and topological properties of Spike protein variants Alpha (B.1.1.7), Beta (B.1.351), Delta (B.1.617), and Omicron 23A (XBB.1.5) were analyzed using a protein structure network (PSN) framework. Three-dimensional structural models were generated from experimental templates, and residue interaction networks were constructed using alpha-carbon atoms as nodes and a distance threshold of 7 Å. Graph-theoretical metrics—including degree, betweenness, closeness, clustering coefficient, eigenvector centrality, and eccentricity—were calculated to identify key insights involved in structural communication pathways. Preliminary analysis indicates that mutations associated with major variants alter network topology and may affect long-range communication between structural domains of the Spike protein. These findings provide a computational framework for detecting mutation-sensitive regions and potential targets for antiviral intervention.