  The in silico design of peptides and proteins as binders is useful for
diagnosis and therapeutics due to their low adverse effects and major
specificity. To select the most promising candidates, a key matter is to
understand their interactions with protein targets. In this work, we present
PARCE, an open source Protocol for Amino acid Refinement through Computational
Evolution that implements an advanced and promising method for the design of
peptides and proteins. The protocol performs a random mutation in the binder
sequence, then samples the bound conformations using molecular dynamics
simulations, and evaluates the protein-protein interactions from multiple
scoring. Finally, it accepts or rejects the mutation by applying a consensus
criterion based on binding scores. The procedure is iterated with the aim to
explore efficiently novel sequences with potential better affinities toward
their targets. We also provide a tutorial for running and reproducing the
methodology.
