UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes

Motivation: Single protein residue mutations may reshape the binding affinity of protein-protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein-protein complexes trained on interactome data. Results: Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein-protein mutations using UEP, a fast and reliable open-source code.

Automated summary

This study introduces the UEP classifier trained on interactome data for predicting beneficial and detrimental mutations in protein-protein complexes. Despite its simplicity, the UEP algorithm shows competitive results with gold standard methods in the field while also proposing a consensus selection procedure for higher classification accuracy. Overall, the analysis of interactome data demonstrates the effectiveness of UEP as a fast and reliable tool for predicting the impact of protein-protein mutations.