PT  - JOURNAL ARTICLE
AU  - Chloé Quignot
AU  - Pierre Granger
AU  - Pablo Chacón
AU  - Raphael Guerois
AU  - Jessica Andreani
TI  - Atomic-level evolutionary information improves protein-protein interface scoring
AID  - 10.1101/2020.10.26.355073
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.10.26.355073
4099  - http://biorxiv.org/content/early/2020/10/26/2020.10.26.355073.short
4100  - http://biorxiv.org/content/early/2020/10/26/2020.10.26.355073.full
AB  - 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 StatementThe authors have declared no competing interest.