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Atomic-level evolutionary information improves protein-protein interface scoring

View ORCID ProfileChloé Quignot, View ORCID ProfilePierre Granger, View ORCID ProfilePablo Chacón, View ORCID ProfileRaphael Guerois, View ORCID ProfileJessica Andreani
doi: https://doi.org/10.1101/2020.10.26.355073
Chloé Quignot
1Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
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Pierre Granger
1Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
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Pablo Chacón
2Department of Biological Chemical Physics, Rocasolano Institute of Physical Chemistry C.S.I.C, Madrid, Spain
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Raphael Guerois
1Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
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  • For correspondence: jessica.andreani@cea.fr guerois@cea.fr
Jessica Andreani
1Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
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  • For correspondence: jessica.andreani@cea.fr guerois@cea.fr
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Abstract

The crucial role of protein interactions and the difficulty in characterising them experimentally strongly motivates the development of computational approaches for structural prediction. Even when protein-protein docking samples correct models, current scoring functions struggle to discriminate them from incorrect decoys. The previous incorporation of conservation and coevolution information has shown promise for improving protein-protein scoring. Here, we present a novel strategy to integrate atomic-level evolutionary information into different types of scoring functions to improve their docking discrimination.

We applied this general strategy to our residue-level statistical potential from InterEvScore and to two atomic-level scores, SOAP-PP and Rosetta interface score (ISC). Including evolutionary information from as few as ten homologous sequences improves the top 10 success rates of these individual scores by respectively 6.5, 6 and 13.5 percentage points, on a large benchmark of 752 docking cases. The best individual homology-enriched score reaches a top 10 success rate of 34.4%. A consensus approach based on the complementarity between different homology-enriched scores further increases the top 10 success rate to 40%.

All data used for benchmarking and scoring results, as well as pipelining scripts, are available at http://biodev.cea.fr/interevol/interevdata/

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Funding This work was supported by Agence Nationale de la Recherche [ANR‐15‐CE11‐0008 to R.G., ANR-18-CE45-0005 to J.A]; IDEX Paris-Saclay [IDI 2017 to C.Q.]; MINECO [BFU2016-76220-P to P.C.]; and AEI/FEDER, UE [PID2019-109041GB-C21 to P.C.].

  • http://biodev.cea.fr/interevol/interevdata/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted October 26, 2020.
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Atomic-level evolutionary information improves protein-protein interface scoring
Chloé Quignot, Pierre Granger, Pablo Chacón, Raphael Guerois, Jessica Andreani
bioRxiv 2020.10.26.355073; doi: https://doi.org/10.1101/2020.10.26.355073
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Atomic-level evolutionary information improves protein-protein interface scoring
Chloé Quignot, Pierre Granger, Pablo Chacón, Raphael Guerois, Jessica Andreani
bioRxiv 2020.10.26.355073; doi: https://doi.org/10.1101/2020.10.26.355073

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