Knowledge of Native Protein–Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates

Petr Popov 1, 2 Sergei Grudinin 2
2 NANO-D - Algorithms for Modeling and Simulation of Nanosystems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Selection of putative binding poses is a challenging part of virtual screening for protein–protein interactions. Predictive models to filter out binding candidates with the highest binding affinities comprise scoring functions that assign a score to each binding pose. Existing scoring functions are typically deduced by collecting statistical information about interfaces of native conformations of protein complexes along with interfaces of a large generated set of non-native conformations. However, the obtained scoring functions become biased toward the method used to generate the non-native conformations, i.e., they may not recognize near-native interfaces generated with a different method. The present study demonstrates that knowledge of only native protein–protein interfaces is sufficient to construct well-discriminative predictive models for the selection of binding candidates. Here we introduce a new scoring method that comprises a knowledge-based potential called KSENIA deduced from structural information about the native interfaces of 844 crystallographic protein–protein complexes. We derive KSENIA using convex optimization with a training set composed of native protein complexes and their near-native conformations obtained using deformations along the low-frequency normal modes. As a result, our knowledge-based potential has only marginal bias toward a method used to generate putative binding poses. Furthermore, KSENIA is smooth by construction, which allows it to be used along with rigid-body optimization to refine the binding poses. Using several test benchmarks, we demonstrate that our method discriminates well native and near-native conformations of protein complexes from non-native ones. Our methodology can be easily adapted to the recognition of other types of molecular interactions, such as protein–ligand, protein–RNA, etc. KSENIA will be made publicly available as a part of the SAMSON software platform at https://team.inria.fr/nano-d/software.
Type de document :
Article dans une revue
Journal of Chemical Information and Modeling, American Chemical Society, 2015, 55 (10), pp.2242-2255. 〈10.1021/acs.jcim.5b00372〉
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Contributeur : Nano-D Equipe <>
Soumis le : mardi 17 novembre 2015 - 13:33:03
Dernière modification le : mercredi 11 avril 2018 - 01:58:37

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Petr Popov, Sergei Grudinin. Knowledge of Native Protein–Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates. Journal of Chemical Information and Modeling, American Chemical Society, 2015, 55 (10), pp.2242-2255. 〈10.1021/acs.jcim.5b00372〉. 〈hal-01229886〉

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