Proceeding talk – Theme: Proteins.
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. PEPSI-Dock (Polynomial Expansion of Protein Structures and Interactions for Docking) 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 re-scoring 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 162 hetero-dimers, 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 minutes on a modern laptop and can easily be extended to other types of interactions.
Authors
Emilie Neveu, INRIA, France
David Ritchie, Inria, France
Petr Popov, Moscow Institute of Physics and Technology, Russian Federation
Sergei Grudinin, Inria, France