SKEMPI 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation

By Justina Jankauskaite, Brian Jiménez-García, Justas Dapkunas, Juan Fernández-Recio, Iain H. Moal. 2018

372 citations recorded by the source corpus.

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 at https://life.bsc.es/pid/skempi2/

Subfields: Molecular Biology, Physical and Theoretical Chemistry

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Citations in the corpus

Learning inverse folding from millions of predicted structures (2022, methodology): “We evaluate models on zero-shot prediction of mutational effects on protein complex interfaces, using the Atom3D benchmark (Townshend et al., 2020) which incorporates binding free energy changes in the SKEMPI database (Jankauskait ˙ e et al., 2019) as a binary classification task.”
Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures (2022, background): “Over the years, considerable efforts have been dedicated to extracting and manually curating experimentally derived protein thermodynamics information from the literature, including the effects of mutations on stability [64–67] and interactions [68,69], and linking these to high-resolution protein s”
EvoDesign: Designing protein-protein binding interactions using evolutionary interface profiles in conjunction with an optimized physical energy function (2019, methodology): “Second, the 14 weights for E interResidueDiffChain were determined using the mutation-induced protein-protein binding affinity change data Δ Δ G bindingWTmut , which contained 2,204 non-redundant mutant samples from 177 dimeric complexes collected from the latest version of the SKEMPI database [27].”
ATOM3D: Tasks On Molecules in Three Dimensions (2020, methodology): “Mutation Stability Prediction (MSP) Effect of Mutation New, created from SKEMPI [Jankauskaitė et al., 2019]”
mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019, background): “…changes in thermodynamic and kinetic parameters on mutation for protein–protein complexes that have 3D structures deposited in the PDB. SKEMPI 2.0 (36) includes new mutations identified in the literature after its first release, including data available from three other databases: ABbind (37),…”