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Preprints, Working Papers, ... Year : 2020

Atomic-level evolutionary information improves protein-protein interface scoring


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
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Dates and versions

cea-02978447 , version 1 (26-10-2020)
cea-02978447 , version 2 (27-04-2021)



Chloé Quignot, Pierre Granger, Pablo Chacón, Raphael Guerois, Jessica Andreani. Atomic-level evolutionary information improves protein-protein interface scoring. 2020. ⟨cea-02978447v1⟩
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