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Atomic-level evolutionary information improves protein-protein interface scoring

Abstract : Motivation: 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. Results: 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 individual atomic-level scores SOAP-PP and Rosetta ISC by respectively 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%. Availability: All data used for benchmarking and scoring results, as well as a Singularity container of the pipeline, are available at http://biodev.cea.fr/interevol/interevdata/ Contact: jessica.andreani@cea.fr or guerois@cea.fr Supplementary information: Supplementary data are available at Bioinformatics online.
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https://hal-cea.archives-ouvertes.fr/cea-02978447
Contributor : Jessica Andreani <>
Submitted on : Tuesday, April 27, 2021 - 8:00:52 AM
Last modification on : Thursday, May 6, 2021 - 10:52:47 AM

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Chloé Quignot, Pierre Granger, Pablo Chacón, Raphael Guerois, Jessica Andreani. Atomic-level evolutionary information improves protein-protein interface scoring. Bioinformatics, Oxford University Press (OUP), 2021, ⟨10.1093/bioinformatics/btab254⟩. ⟨cea-02978447v2⟩

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