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

ABSTRACT

MOTIVATION The crucial role of protein interactions and the difficulty in characterizing 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 10 homologous sequences improves the top 10 success rates of individual atomic-level scores SOAP-PP and Rosetta ISC by 6 and 13.5 percentage points, respectively, 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 AND IMPLEMENTATION 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/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.