An improved Fuzzy based GWO algorithm for predicting the potential host receptor of COVID-19 infection

Area of Science:

• Virology
• Computational Biology
• Genomics

Background:

• Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) utilizes host cell receptors like Angiotensin-converting enzyme 2 (ACE2) and Transmembrane serine protease 2 (TMPRSS2) for entry.
• Understanding viral entry mechanisms, including receptor interactions and protease processing, is crucial for addressing COVID-19 pathogenesis and tropism.

Purpose of the Study:

• To identify novel viral receptors and membrane proteins transcriptionally and biologically similar to ACE2 and TMPRSS2.
• To enhance the understanding of SARS-CoV-2 entry pathways and host-pathogen interactions.

Main Methods:

• A fuzzy clustering technique employing the Grey Wolf Optimizer (GWO) algorithm was utilized to find optimal cluster centers.
• The GWO algorithm was hybridized with evolutionary algorithm operators for improved exploration and exploitation, and genetic diversity was enhanced.
• Analysis involved single-cell transcriptomics (Seurat R toolkit), mass spectrometry (MS), and immunohistochemistry (IHC) to compare expression profiles.

Main Results:

• The study identified 58 novel viral receptors and 816 membrane proteins associated with SARS-CoV-2 pathogenesis.
• Cells expressing high ACE2 levels were found to be more susceptible to SARS-CoV-2 infection, affecting organs like the lungs, intestine, and heart.
• Potential co-receptors including Neuropilins (NRP1) and Basigin (CD147) were confirmed, and heparin derivatives were shown to inhibit viral replication.

Conclusions:

• The identified novel receptors and proteins offer potential therapeutic targets for inhibiting SARS-CoV-2 spread.
• Specific membrane proteins like NDST2, EXT1/2/3, GLCE, and XYLT1/2 present viable targets for antiviral therapies.
• Drugs such as carboplatin and gemcitabine may be effective in targeting these pathways.