Alignment of virus-host protein-protein interaction networks by integer linear programming: SARS-CoV-2.

Area of Science:

• Virology
• Computational Biology
• Network Science

Background:

• The COVID-19 pandemic, caused by SARS-CoV-2, has intensified research into virus-host interactions.
• Understanding virus-host protein-protein interactions is crucial for identifying therapeutic targets and drug discovery.

Purpose of the Study:

• To adapt and apply an integer linear programming model for aligning virus-host protein-protein interaction networks.
• To analyze the conserved and novel interactions between SARS-CoV-1, SARS-CoV-2, and human proteins.

Main Methods:

• Utilized an integer linear programming model for network alignment.
• Applied the model to SARS-CoV-1 and SARS-CoV-2 virus-host protein-protein interaction networks.
• Generated a consensus alignment to identify shared and functionally similar host proteins.

Main Results:

• Achieved a consensus alignment of SARS-CoV-1 and SARS-CoV-2 virus-host networks.
• Identified aligned human proteins with functions relevant to viral infection, despite limited shared proteins and interactions.
• Highlighted functionally similar human proteins interacting with viral proteins, suggesting potential therapeutic targets.

Conclusions:

• Network alignment is a valuable approach for studying virus-host interactions, even with divergent viral proteins.
• The identified host proteins represent potential targets for broad-spectrum antiviral drug development against coronaviruses.
• This method can guide future research in understanding viral pathogenesis and designing antiviral therapies.