PIPER: An FFT‐based protein docking program with pairwise potentials

By Dima Kozakov, Ryan Brenke, Stephen R. Comeau, Sandor Vajda. 2006

870 citations recorded by the source corpus.

The Fast Fourier Transform (FFT) correlation approach to protein–protein docking can evaluate the energies of billions of docked conformations on a grid if the energy is described in the form of a correlation function. Here, this restriction is removed, and the approach is efficiently used with pairwise interaction potentials that substantially improve the docking results. The basic idea is approximating the interaction matrix by its eigenvectors corresponding to the few dominant eigenvalues, resulting in an energy expression written as the sum of a few correlation functions, and solving the problem by repeated FFT calculations. In addition to describing how the method is implemented, we present a novel class of structure‐based pairwise intermolecular potentials. The DARS (Decoys As the Reference State) potentials are extracted from structures of protein–protein complexes and use large sets of docked conformations as decoys to derive atom pair distributions in the reference state. The current version of the DARS potential works well for enzyme–inhibitor complexes. With the new FFT‐based program, DARS provides much better docking results than the earlier approaches, in many cases generating 50% more near‐native docked conformations. Although the potential is far from optimal for antibody–antigen pairs, the results are still slightly better than those given by an earlier FFT method. The docking program PIPER is freely available for noncommercial applications. Proteins 2006. © 2006 Wiley‐Liss, Inc.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes (2020, background): “are based on a probability distribution of defined spatial features (eg, interatomic distances and bond orientations) computed for a set of known protein complex structures.(15-18) Recently, several scoring functions have been developed using machine learning methods.”
An iterative knowledge‐based scoring function for protein–protein recognition (2008, methodology): “Therefore, in addition to being used during the docking process,53 the knowledge-based potentials are also extensively used in the postdocking stage of docking programs (e.g., 3D-DOCK, BiGGER) for rapid pose refinement.”
Protein-protein docking: from interaction to interactome. (2014, neutral): “(10,11), and statistics-based propensities (12,13).”
Recent progress and future directions in protein-protein docking. (2008, methodology): “…approaches such as threading, phylogenetic profiling, gene neighbourhood and gene fusion analysis, and in silico two-hybrid methods are being used with increasing success to predict PPIs directly from gene sequences of yeast and other organisms (for recent reviews see e.g., references [8-13]).”
How Good is Automated Protein Docking? (2013, methodology): “4 Unless specified otherwise in advanced options, the server generates four types of models using the scoring schemes called (1) balanced, (2) electrostatic favored, (3) hydrophobic favored, and (4) van der Waals 1 electrostatics.”
What Method to Use for Protein-Protein Docking? (2019, methodology): “ClusPro uses the FFT-based program PIPER [25], but substantially differs from ZDOCK and pyDOCK because the 1000 lowest energy structures generated by PIPER are clustered using pairwise RMSD as the distance measure, and centers of the largest clusters rather than the lowest energy structures are…”
Application of asymmetric statistical potentials to antibody-protein docking (2012, methodology): “D ¼ max i ðr vdw i Þ, where r vdw i is Van der Waals radii of atom i. PIPER (Kozakov et al., 2006) is able to incorporate symmetric pairwise potentials within its energy function.”
Accelerating Protein Docking in ZDOCK Using an Advanced 3D Convolution Library (2011, methodology): “1 with the program PIPER (which has pairwise potential terms based on docking decoys [26]) found ZDOCK 3.”
Harnessing protein folding neural networks for peptide–protein docking (2021, methodology): “Based on this finding, we developed the first high resolution, global blind peptide docking protocol, PIPER-FlexPepDock (PFPD) (21), in which a fragment conformer ensemble extracted from monomer structures (using the Rosetta Fragment Picker, (22)) is used to represent the peptide conformation, exhau”