PEPSI-Dock: a detailed data-driven protein–protein interaction potential accelerated by polar Fourier correlation
Autor: | Petr Popov, David W. Ritchie, Emilie Neveu, Sergei Grudinin |
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Přispěvatelé: | Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Moscow Institute of Physics and Technology [Moscow] (MIPT) |
Rok vydání: | 2016 |
Předmět: |
0301 basic medicine
Statistics and Probability Binding free energy Protein Conformation Computer science Molecular Conformation Docking proteines Machine learning computer.software_genre Biochemistry Molecular Docking Simulation Molecular conformation Protein–protein interaction Machine Learning 03 medical and health sciences Protein structure DOCK Native state Macromolecular docking Protein Interactions Molecular Biology business.industry Proteins Models Theoretical [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation Computer Science Applications Computational Mathematics 030104 developmental biology Computational Theory and Mathematics Docking (molecular) Artificial intelligence business computer Algorithm Algorithms Protein Binding |
Zdroj: | Bioinformatics Bioinformatics, 2016, 32 (7), pp.i693-i701. ⟨10.1093/bioinformatics/btw443⟩ Bioinformatics, Oxford University Press (OUP), 2016, 32 (7), pp.i693-i701. ⟨10.1093/bioinformatics/btw443⟩ |
ISSN: | 1367-4811 1367-4803 |
Popis: | Motivation Docking prediction algorithms aim to find the native conformation of a complex of proteins from knowledge of their unbound structures. They rely on a combination of sampling and scoring methods, adapted to different scales. Polynomial Expansion of Protein Structures and Interactions for Docking (PEPSI-Dock) improves the accuracy of the first stage of the docking pipeline, which will sharpen up the final predictions. Indeed, PEPSI-Dock benefits from the precision of a very detailed data-driven model of the binding free energy used with a global and exhaustive rigid-body search space. As well as being accurate, our computations are among the fastest by virtue of the sparse representation of the pre-computed potentials and FFT-accelerated sampling techniques. Overall, this is the first demonstration of a FFT-accelerated docking method coupled with an arbitrary-shaped distance-dependent interaction potential. Results First, we present a novel learning process to compute data-driven distant-dependent pairwise potentials, adapted from our previous method used for rescoring of putative protein–protein binding poses. The potential coefficients are learned by combining machine-learning techniques with physically interpretable descriptors. Then, we describe the integration of the deduced potentials into a FFT-accelerated spherical sampling provided by the Hex library. Overall, on a training set of 163 heterodimers, PEPSI-Dock achieves a success rate of 91% mid-quality predictions in the top-10 solutions. On a subset of the protein docking benchmark v5, it achieves 44.4% mid-quality predictions in the top-10 solutions when starting from bound structures and 20.5% when starting from unbound structures. The method runs in 5–15 min on a modern laptop and can easily be extended to other types of interactions. Availability and Implementation https://team.inria.fr/nano-d/software/PEPSI-Dock. Contact sergei.grudinin@inria.fr |
Databáze: | OpenAIRE |
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