Atomic-level evolutionary information improves protein-protein interface scoring

Chloé Quignot (1, 2) , Pierre Granger (1, 2) , Pablo Chacón (3) , Raphael Guerois (1, 2) , Jessica Andreani (1, 2)

1 I2BC - Institut de Biologie Intégrative de la Cellule
2 JOLIOT - Institut des Sciences du Vivant Frédéric JOLIOT
3 IQFR - Instituto de Química Física Rocasolano

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: [e-mail] or [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.

Mots clés: protein-protein interactions; protein docking; protein scoring; protein evolution; protein structure; structural bioinformatics

Domaines: Bio-informatique [q-bio.QM]; Biochimie [q-bio.BM]; Biochimie, Biologie Moléculaire; Biophysique

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