SKEMPI

SKEMPI catalogs experimentally measured effects of mutations on protein–protein interactions to enable analysis of mutation impacts on binding affinity, kinetics, and thermodynamics in structure-resolved complexes.


Key Features:

  • Manually curated mutation dataset: Contains 7,085 manually curated single and multiple mutations mapped to protein–protein complexes.
  • Structure-resolved PPIs (PDB): Entries are linked to complex structures available in the Protein Data Bank (PDB).
  • Binding free energy changes (ΔΔG): Reports experimental binding free energy changes for mutations to quantify effects on affinity.
  • Kinetic rate constants: Provides changes in association and dissociation rate constants for 1,844 mutations.
  • Thermodynamic parameters: Includes enthalpy and entropy changes for 443 mutations.
  • Loss-of-binding annotations: Identifies 440 mutations that result in loss of detectable binding.

Scientific Applications:

  • Mutation-impact analysis: Enables study of how sequence alterations affect binding affinity, kinetics, and thermodynamics.
  • Protein engineering: Supports design and engineering of proteins with modified interaction properties.
  • Signaling and complex regulation studies: Facilitates investigation of cellular signaling pathways and molecular complex regulation affected by interaction changes.
  • Disease mechanism research: Aids analysis of how genetic mutations lead to loss or alteration of protein–protein interactions relevant to disease.

Methodology:

Manually curated experimental measurements of binding free energy, kinetic rate constants, and thermodynamic parameters are collected and mapped to PDB complex structures.

Topics

Details

License:
CC-BY-4.0
Maturity:
Mature
Cost:
Free of charge
Tool Type:
web application
Operating Systems:
Windows, Linux, Mac
Added:
8/27/2021
Last Updated:
11/24/2024

Operations

Publications

Jankauskaitė J, Jiménez-García B, Dapkūnas J, Fernández-Recio J, Moal IH. SKEMPI 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation. Bioinformatics. 2018;35(3):462-469. doi:10.1093/bioinformatics/bty635. PMID:30020414. PMCID:PMC6361233.

PMID: 30020414
PMCID: PMC6361233
Funding: - Future Leader Fellowship: BB/N011600/1 - MINECO: BIO2016-79930-R - Interreg POCTEFA: EFA086/15 - European Commission: 676566

Documentation

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