Pinc: a simple probabilistic AlphaFold interaction score

MotivationScreening of interacting proteins with AlphaFold has become widespread in biological research owing to its utility in generating and testing hypotheses. While several model quality and interaction confidence metrics have been developed, their interpretation is not always straightforward. ResultsHere, building on a previously published method, we address this limitation by converting predicted aligned errors of an AlphaFold model into conditional contact probabilities. We show that, without additional parametrisation, the contact probabilities are readily calibrated to the fraction of native contacts observed across experimentally determined protein dimers. We find that the average contact probability for interacting chains, termed Pinc (probability of interface native contacts), is more sensitive to interactions involving smaller interfaces than many commonly used scores. We provide an R script to calculate Pinc for AlphaFold models, and propose its use as an alternative scoring metric for interaction screens and for prioritising interface residues for experimental validation. Availability and implementationAn R script and a Colab notebook are available at https://git.mpi-cbg.de/tothpetroczylab/Pinc