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Atomic-level evolutionary information improves protein-protein interface scoring.

Chloé Quignot1, Pierre Granger1, Pablo Chacón2

  • 1Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France.

Bioinformatics (Oxford, England)
|April 26, 2021
PubMed
Summary
This summary is machine-generated.

Integrating evolutionary information into protein-protein docking scoring functions significantly enhances discrimination accuracy. This computational approach improves the identification of correct protein complex models, aiding structural prediction.

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Bioinformatics

Background:

  • Experimental characterization of protein interactions is challenging, necessitating computational methods for structural prediction.
  • Current protein-protein docking scoring functions often fail to distinguish correct models from decoys.
  • Incorporating evolutionary information, such as conservation and coevolution, has shown potential for improving scoring accuracy.

Purpose of the Study:

  • To develop and evaluate a novel strategy for integrating atomic-level evolutionary information into protein-protein scoring functions.
  • To enhance the discrimination capabilities of docking scoring functions for improved structural prediction of protein complexes.

Main Methods:

  • A general strategy was developed to integrate atomic-level evolutionary data into existing scoring functions.
  • The strategy was applied to residue-level statistical potential (InterEvScore) and atomic-level scores (SOAP-PP, Rosetta interface score - ISC).
  • Homology information from as few as 10 sequences was utilized.

Main Results:

  • Inclusion of evolutionary information improved the top 10 success rates of SOAP-PP by 6% and Rosetta ISC by 13.5% on a benchmark of 752 cases.
  • The best individual homology-enriched score achieved a top 10 success rate of 34.4%.
  • A consensus approach combining multiple homology-enriched scores further boosted the top 10 success rate to 40%.

Conclusions:

  • Integrating atomic-level evolutionary information is an effective strategy to improve protein-protein docking scoring functions.
  • This approach significantly enhances the ability to discriminate correct protein complex models.
  • The developed methods and data are available for further research and application.