Predicting Binding Poses and Affinities in the CSAR 2013―2014 Docking Exercises Using the Knowledge-Based Convex-PL Potential

Sergei Grudinin 1 Petr Popov 2, 1 Emilie Neveu 1 Georgy Cheremovskiy 1, 2
1 NANO-D - Algorithms for Modeling and Simulation of Nanosystems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : The 2013–2014 CSAR docking exercise was the opportunity to assess the performance of the novel knowledge-based potential we are developing, named Convex-PL. The data used to derive the potential consists only of structural information from protein–ligand interfaces found in the PDBBind database. As expected, our potential proved to be very efficient in the near-native pose detection exercises, where we correctly predicted two near-native poses in the 2013 exercise and also ranked 22 near-native poses first and 2 second in the 2014 exercise. Somewhat more surprisingly, we obtained a fair performance in some of the CSAR affinity ranking exercises, where the Spearman correlation coefficients between our predictions and the experiments are greater than 0.5 for several protein–ligand sets. Nonetheless, affinity prediction exercises turned out to be a challenge, and significant progress in the development of our method is needed before we can successfully predict binding constants.
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Journal of Chemical Information and Modeling, American Chemical Society, 2016, 56 (6), pp.1053-1062. 〈10.1021/acs.jcim.5b00339〉
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https://hal.inria.fr/hal-01258022
Contributeur : Nano-D Equipe <>
Soumis le : lundi 18 janvier 2016 - 15:14:21
Dernière modification le : mercredi 11 avril 2018 - 01:58:31

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Sergei Grudinin, Petr Popov, Emilie Neveu, Georgy Cheremovskiy. Predicting Binding Poses and Affinities in the CSAR 2013―2014 Docking Exercises Using the Knowledge-Based Convex-PL Potential. Journal of Chemical Information and Modeling, American Chemical Society, 2016, 56 (6), pp.1053-1062. 〈10.1021/acs.jcim.5b00339〉. 〈hal-01258022〉

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