Atomic-level evolutionary information improves protein–protein interface scoring
Author(s) -
Chloé Quignot,
Pierre Granger,
Pablo Chacón,
Raphaël Guérois,
Jessica Andréani
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab254
Subject(s) - benchmarking , casp , computer science , docking (animal) , protein structure prediction , macromolecular docking , coevolution , data mining , artificial intelligence , machine learning , computational biology , protein structure , biology , ecology , medicine , biochemistry , business , nursing , marketing
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.
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