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. Availability and implementation UEP algorithm can be found at: https://github.com/pepamengual/UEP. Supplementary information Supplementary data are available at Bioinformatics online.This work was supported by a predoctoral fellowship from the Government of Catalonia (2018FI_B_00873 to P.A.-R.) and grants from the Spanish government ‘Programa Estatal I+D+i’ (BIO2016-79930-R), (PID2019-110167RB-I00) and (CTQ2016-79138-R), and by grant PIREPRED from the EU European Regional Development Fund program Interreg V-A Spain-France-Andorra (POCTEFA). This work was also received funding from the IBM-BSC Deep Learning Center (2016).Peer reviewe