Analyzing and Comparing Omicron Lineage Variants Protein-Protein Interaction Network Using Centrality Measure

Area of Science:

• Bioinformatics
• Systems Biology
• Virology

Background:

• Omicron variants are spreading globally, necessitating an understanding of cellular processes and protein interactions.
• Protein-protein interaction networks (PPINs) are vital for identifying key regulatory proteins and potential drug targets.
• No prior studies have analyzed PPINs specifically for the Omicron lineage and its variants.

Purpose of the Study:

• To identify significant proteins within Omicron variant protein-protein interaction networks.
• To explore potential therapeutic targets for Omicron variants through network analysis.
• To understand the regulatory roles of proteins in Omicron variant cellular processes.

Main Methods:

• Construction of a PPIN for Omicron variants using 68 nodes (proteins) and 52 edges (interactions).
• Application of graph analysis techniques, including centrality measures (PageRank, Degree, Closeness, Betweenness) and Local Clustering Coefficient.
• Identification of network clusters using Markov clustering.

Main Results:

• Analysis revealed 8 significant proteins (candidate genes) among the 68 proteins studied.
• Key proteins identified include AHSG, KCNK1, KCNQ1, MAPT, NR1H4, PSMC2, PTPN11, and UBE21.
• The MAPT protein was found to have the most significant impact within the Omicron variant PPIN.

Conclusions:

• The study successfully identified critical proteins in Omicron variant networks, highlighting MAPT as a key player.
• These findings provide a foundation for developing targeted drugs against Omicron variants.
• Understanding protein interactions in Omicron variants is crucial for future therapeutic strategies.