Literature DB >> 25586563

Inferring the microscopic surface energy of protein-protein interfaces from mutation data.

Iain H Moal1, Justas Dapkūnas, Juan Fernández-Recio.   

Abstract

Mutations at protein-protein recognition sites alter binding strength by altering the chemical nature of the interacting surfaces. We present a simple surface energy model, parameterized with empirical ΔΔG values, yielding mean energies of -48 cal mol(-1) Å(-2) for interactions between hydrophobic surfaces, -51 to -80 cal mol(-1) Å(-2) for surfaces of complementary charge, and 66-83 cal mol(-1) Å(-2) for electrostatically repelling surfaces, relative to the aqueous phase. This places the mean energy of hydrophobic surface burial at -24 cal mol(-1) Å(-2) . Despite neglecting configurational entropy and intramolecular changes, the model correlates with empirical binding free energies of a functionally diverse set of rigid-body interactions (r = 0.66). When used to rerank docking poses, it can place near-native solutions in the top 10 for 37% of the complexes evaluated, and 82% in the top 100. The method shows that hydrophobic burial is the driving force for protein association, accounting for 50-95% of the cohesive energy. The model is available open-source from http://life.bsc.es/pid/web/surface_energy/ and via the CCharpPPI web server http://life.bsc.es/pid/ccharppi/.
© 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  binding affinity; docking; empirical modeling; hydrophobic effect; interaction energy; mutation; protein-protein interactions

Mesh:

Substances:

Year:  2015        PMID: 25586563     DOI: 10.1002/prot.24761

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  4 in total

1.  Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2.

Authors:  Thom Vreven; Iain H Moal; Anna Vangone; Brian G Pierce; Panagiotis L Kastritis; Mieczyslaw Torchala; Raphael Chaleil; Brian Jiménez-García; Paul A Bates; Juan Fernandez-Recio; Alexandre M J J Bonvin; Zhiping Weng
Journal:  J Mol Biol       Date:  2015-07-29       Impact factor: 5.469

2.  IRaPPA: information retrieval based integration of biophysical models for protein assembly selection.

Authors:  Iain H Moal; Didier Barradas-Bautista; Brian Jiménez-García; Mieczyslaw Torchala; Arjan van der Velde; Thom Vreven; Zhiping Weng; Paul A Bates; Juan Fernández-Recio
Journal:  Bioinformatics       Date:  2017-06-15       Impact factor: 6.937

3.  A machine learning approach for ranking clusters of docked protein-protein complexes by pairwise cluster comparison.

Authors:  Erik Pfeiffenberger; Raphael A G Chaleil; Iain H Moal; Paul A Bates
Journal:  Proteins       Date:  2017-01-20

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

Authors:  Justina Jankauskaite; Brian Jiménez-García; Justas Dapkunas; Juan Fernández-Recio; Iain H Moal
Journal:  Bioinformatics       Date:  2019-02-01       Impact factor: 6.937

  4 in total

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