Abstract: Protein-protein interactions mediate the majority of key cellular activities, and it is well established that disease causing mutations are enriched at these interfaces. Different computational approaches to study the effects of mutations on protein complexes have been proposed in recent years. We developed a platform of programs using a novel machine learning method that uses graph-based structural signatures called mCSM. This has been shown to be an accurate and high-throughput approach to predict the impact of mutations on protein structure and function, and was one of the first methods capable of assessing the impact of mutations on protein interaction binding affinity. Here we present mCSM-PPI2, an easy-to-use web server that implements an integrated computational approach for predicting effects of missense mutations in protein-protein affinity. Our method uses an optimised graph-based signature approach to better assess the molecular mechanism of the mutation, by modelling the effects of variations on the inter-residue non-covalent interaction network using graph kernels, evolutionary information, complex network metrics and energetic terms.