title: PEPSI-Dock: a detailed data-driven protein-protein interaction potential accelerated by polar Fourier correlation.
abstract: 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.
  
  
  
  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.
  
  
  
  https://team.inria.fr/nano-d/software/PEPSI-Dock
  
  
  
  sergei.grudinin@inria.fr.
authors: Inria/University Grenoble Alpes/LJK-CNRS, F-38000 Grenoble, France.
authors: Emilie
authors: E
authors: Neveu
authors: Inria Nancy - Grand Est, 54600 Villers-les-Nancy, France.
authors: David W
authors: DW
authors: Ritchie
authors: Inria/University Grenoble Alpes/LJK-CNRS, F-38000 Grenoble, France Moscow Institute of Physics and Technology, Dolgoprudniy, Russia.
authors: Petr
authors: P
authors: Popov
authors: Inria/University Grenoble Alpes/LJK-CNRS, F-38000 Grenoble, France.
authors: Sergei
authors: S
authors: Grudinin
mesh_terms: D000465:Algorithms; D000069550:Machine Learning; D008962:Models, Theoretical; D008968:Molecular Conformation; D062105:Molecular Docking Simulation; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins
chemical_list: D011506:Proteins