An Approach for Engineering Peptides for Competitive Inhibition of the SARS-COV-2 Spike Protein.

Area of Science:

• Biochemistry and Molecular Biology
• Computational Biology and Bioinformatics
• Virology

Background:

• The COVID-19 pandemic, caused by SARS-CoV-2, has necessitated the development of novel therapeutic strategies.
• Peptides represent a promising class of agents for inhibiting viral activity, but efficient design remains a challenge.

Purpose of the Study:

• To present a novel algorithm for peptide engineering aimed at inhibiting SARS-CoV-2.
• To design peptides that mimic the human ACE2 interaction site with the SARS-CoV-2 spike protein.

Main Methods:

• Utilized a genetic algorithm incorporating random mutation and protein-peptide docking (PyRosetta).
• Peptide optimization was based on interface contacts.
• Evaluated the algorithm through three case studies and compared results with existing literature.
• Performed molecular dynamics (MD) simulations (200 ns each) to assess peptide-spike protein interactions.

Main Results:

• The developed algorithm systematically mutates, docks, and selects peptides.
• Case studies demonstrated the tool's parameter evaluation capabilities.
• MD simulations provided evidence for potential interaction between designed peptides and the spike protein.

Conclusions:

• The proposed peptide engineering methodology shows potential for designing effective SARS-CoV-2 inhibitors.
• This computational strategy offers a promising avenue for developing new antiviral therapies.