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Motivation: Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein-protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering.
Results: We present SKEMPI 2.0, a major update to our database of binding free energy changes upon mutation for structurally resolved protein-protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations which abolish detectable binding.
Availability: The database is available as supplementary data and at https://life.bsc.es/pid/skempi2/.
Supplementary information: Supplementary data are available at Bioinformatics online.

Empirical models for the prediction of how changes in sequence alter protein-protein binding kinetics and thermodynamics can garner insights into many aspects of molecular biology. However, such models require empirical training data and proper validation before they can be widely applied. Previous databases contained few stabilizing mutations and no discussion of their inherent biases or how this impacts model construction or validation. We present SKEMPI, a database of 3047 binding free energy changes upon mutation assembled from the scientific literature, for protein-protein heterodimeric complexes with experimentally determined structures. This represents over four times more data than previously collected. Changes in 713 association and dissociation rates and 127 enthalpies and entropies were also recorded. The existence of biases towards specific mutations, residues, interfaces, proteins and protein families is discussed in the context of how the data can be used to construct predictive models. Finally, a cross-validation scheme is presented which is capable of estimating the efficacy of derived models on future data in which these biases are not present. The database is available online at http://life.bsc.es/pid/mutation_database/.