Adaptive Evolution of Peptide Inhibitors for Mutating SARS-CoV-2

Area of Science:

• Computational biology
• Virology
• Drug discovery

Background:

• Zoonotic disease outbreaks are increasing globally.
• The SARS-CoV-2 pandemic highlights the need for adaptive therapeutics.
• Targeting the interaction between the viral spike protein and human ACE2 is a key strategy.

Purpose of the Study:

• To develop a computational method for adaptively evolving peptides.
• To create peptides that selectively inhibit SARS-CoV-2 spike protein receptor binding domain (RBD) from binding to human ACE2.
• To generate a library of optimized therapeutics against SARS-CoV-2.

Main Methods:

• Utilized a computational strategy based on selected ACE2 segments as peptide templates.
• Employed adaptive evolution with random mutations and Monte Carlo simulations.
• Performed atomistic molecular dynamics simulations to assess RBD-binding free energies.

Main Results:

• Developed a method to iteratively modify peptide templates.
• Identified mutations that maximize peptide-RBD binding free energies.
• Generated optimized peptide candidates for therapeutic development.

Conclusions:

• The computational approach can generate effective peptide inhibitors of SARS-CoV-2.
• This strategy offers a pathway for developing adaptive therapeutics against mutating viruses.
• The findings contribute to global efforts to control the SARS-CoV-2 pandemic.