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 using multiple scoring functions. Finally, it accepts or rejects the mutation by applying a consensus criterion based on the binding scores. The procedure is iterated with the aim to explore efficiently novel sequences with potential better affinities towards their targets. We also provide a tutorial for running and reproducing the methodology. Program summary: Program Title: PARCE CPC Library link to program files: http://dx.doi.org/10.17632/jcpj3j83rt.1 Developer's repository link: https://github.com/PARCE-project/PARCE-1 Licensing provisions: MIT License Programming language: Python 3 Nature of problem: Computational design of peptides and proteins as binders for diagnosis and therapeutics. Solution method: A protocol that performs random mutations in the binder sequence, samples the bound conformations using molecular dynamics simulations, and evaluates the protein–protein interactions from multiple scoring predictions in order to accept or reject the mutations. Additional comments including restrictions and unusual features: Subprograms used: Gromacs 5.1.4, Scwrl4, FASPR, PDB2PQR, Scoring functions source code. This article describes version 1.0. PARCE is available at: https://github.com/PARCE-project/PARCE-1, and a Docker container can be downloaded from: https://hub.docker.com/r/rochoa85/parce-1.
