Darwin vs. Covid: Using evolutionary algorithms to generate and optimize antiviral peptides against the SARS-CoV2

Peptides have great potential to be used in antibiotic and antiviral therapies, including against the SARS-CoV2. These naturally occurring biomolecules are safer, more selective, and more specific than conventional synthetic drugs. Their larger size and flexibility also enable them to better inhibit protein-protein interactions. However, the rational design of peptides remains challenging.
Evolutionary algorithms are powerful and versatile computational tools for optimization based on Darwin's "survival of the fittest" theory. Here we propose a custom evolutionary algorithm to repeatedly test and optimize peptide sequences using Rosetta's flexpepdock protocol to evaluate and score their interactions with SARS-CoV-2 main protease (MPRO). Our fitness function considered both the affinity and the number of contacts the peptides made with the MPRO binding site. The initial results show that the algorithm succeeds in generating peptides with higher affinity and selectiveness to the desired binding site.