In 2019, we released Missense3D which identifies stereochemical features that are disrupted by a missense variant, such as introducing a buried charge. Missense3D analyses the effect of a missense variant on a single structure and thus may fail to identify as damaging surface variants disrupting a protein interface i.e., a protein–protein interaction (PPI) site. Here we present Missense3D-PPI designed to predict missense variants at PPI interfaces.

Our development dataset comprised of 1,279 missense variants (pathogenic n = 733, benign n = 546) in 434 proteins and 545 experimental structures of PPI complexes. Benchmarking of Missense3D-PPI was performed after dividing the dataset in training (320 benign and 320 pathogenic variants) and testing (226 benign and 413 pathogenic). Structural features affecting PPI, such as disruption of interchain bonds and introduction of unbalanced charged interface residues, were analysed to assess the impact of the variant at PPI.

The performance of Missense3D-PPI was superior to that of Missense3D: sensitivity 44 % versus 8% and accuracy 58% versus 40%, p = 4.23 × 10−16. However, the specificity of Missense3D-PPI was lower compared to Missense3D (84% versus 98%). On our dataset, Missense3D-PPI’s accuracy was superior to BeAtMuSiC (p = 3.4 × 10−5), mCSM-PPI2 (p = 1.5 × 10−12) and MutaBind2 (p = 0.0025).

Missense3D-PPI represents a valuable tool for predicting the structural effect of missense variants on biological protein networks and is available at the Missense3D web portal (http://missense3d.bc.ic.ac.uk).