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

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Neveu , Emilie ; Ritchie , David ; Popov , Petr ; Grudinin , Sergei (2016)
  • Publisher: Oxford University Press (OUP)
  • Related identifiers: doi: 10.1093/bioinformatics/btw443
  • Subject: Protein Interactions | [ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation | Docking proteines

International audience; 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:
  • References (49)
    49 references, page 1 of 5

    Berman,H.M. et al. (2000) The protein data bank. Nucleic Acids Res., 28, 235-242.

    Bo¨ hm,H.J. (1994) The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known threedimensional structure. J. Comput. Aided Mol. Des., 8, 243-256.

    Bonvin,A.M.J.J. (2006) Flexible protein-protein docking. Curr. Opin. Struct. Biol., 16, 194-200. (

    Boyd,S. and Vandenberghe,L. (2004). Convex Optimization. Cambridge University Press, New York.

    Brooks,B.R. et al. (1983) Charmm: A program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem., 4, 187-217.

    Case,D.A. et al. (2005) The amber biomolecular simulation programs. J. Comput. Chem., 26, 1668-1688.

    Chae,M.H. et al. (2010) Predicting protein complex geometries with a neural network. Proteins Struct. Funct. Bioinf., 78, 1026-1039.

    Chaskar,P. et al. (2014) Toward on-the-fly quantum mechanical/molecular mechanical (qm/mm) docking: Development and benchmark of a scoring function. J. Chem. Inf. Model., 54, 3137-3152. PMID: 25296988.

    Chuang,G.Y. et al. (2008) Dars (decoys as the reference state) potentials for protein-protein docking. Biophys. J., 95, 4217-4227.

    Eldridge,M.D. et al. (1997) Empirical scoring functions: I. the development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J. Comput. Aided Mol. Des., 11, 425-445.

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