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PEPSI-Dock: a detailed data-driven protein–protein interaction potential accelerated by polar Fourier correlation

Emilie Neveu 1 David Ritchie 2 Petr Popov 3, 1 Sergei Grudinin 1
1 NANO-D - Algorithms for Modeling and Simulation of Nanosystems
Grenoble INP [2007-2019] - Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019], LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
2 CAPSID - Computational Algorithms for Protein Structures and Interactions
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : 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 pair-wise 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: Contact:
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Emilie Neveu, David Ritchie, Petr Popov, Sergei Grudinin. PEPSI-Dock: a detailed data-driven protein–protein interaction potential accelerated by polar Fourier correlation. Bioinformatics, Oxford University Press (OUP), 2016, 32 (7), pp.i693-i701. ⟨10.1093/bioinformatics/btw443⟩. ⟨hal-01358645⟩



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