This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper and its Supporting Information files.įunding: This work was funded by the Swedish Research Council (621-2012-5270) and the Swedish e-Science Research Center. Received: FebruAccepted: AugPublished: August 25, 2016Ĭopyright: © 2016 Basu, Wallner. PLoS ONE 11(8):Įditor: Yaakov Koby Levy, Weizmann Institute of Science, ISRAEL DockQ is available at Ĭitation: Basu S, Wallner B (2016) DockQ: A Quality Measure for Protein-Protein Docking Models. The possibility to directly correlate a quality measure to a scoring function has been crucial for the development of scoring functions for protein structure prediction, and DockQ should be useful in a similar development in the protein docking field. Since DockQ recapitulates the CAPRI classification almost perfectly, it can be viewed as a higher resolution version of the CAPRI classification, making it possible to estimate model quality in a more quantitative way using Z-scores or sum of top ranked models, which has been so valuable for the CASP community. An average PPV of 94% at 90% Recall demonstrating that there is no need to apply predefined ad-hoc cutoffs to classify docking models. By using DockQ on CAPRI models it is possible to almost completely reproduce the original CAPRI classification into Incorrect, Acceptable, Medium and High quality. Here, we present DockQ, a continuous protein-protein docking model quality measure derived by combining F nat, LRMS, and iRMS to a single score in the range that can be used to assess the quality of protein docking models. This classification has been useful in CAPRI, but since models are grouped in only four bins it is also rather limiting, making it difficult to rank models, correlate with scoring functions or use it as target function in machine learning algorithms. This is also the reason why the so called CAPRI criteria for assessing the quality of docking models is defined by applying various ad-hoc cutoffs on these measures to classify a docking model into the four classes: Incorrect, Acceptable, Medium, or High quality. a model with relatively poor LRMS (>10Å) might still qualify as 'acceptable' with a descent F nat (>0.50) and iRMS (<3.0Å). These quality measures quantify different aspects of the quality of a particular docking model and need to be viewed together to reveal the true quality, e.g. © 2008 Wiley Periodicals, Inc.The state-of-the-art to assess the structural quality of docking models is currently based on three related yet independent quality measures: F nat, LRMS, and iRMS as proposed and standardized by CAPRI. The success rate of Q-Dock employing a pocket-specific potential is 6.3 times higher than that previously reported for the Dolores method, another low-resolution docking approach. To further improve docking accuracy against low-quality protein models, we propose a pocket-specific protein–ligand interaction potential derived from weakly homologous threading holo-templates. In decoy-docking against distorted receptor models with a root-mean-square deviation, RMSD, from native of ∼3 Å, Q-Dock recovers on average 15-20% more specific contacts and 25–35% more binding residues than all-atom methods. All-atom models reconstructed from Q-Dock's low-resolution models can be further refined by even a simple all-atom energy minimization. Self-docking experiments using crystal structures reveals satisfactory accuracy, comparable with all-atom docking. In this spirit, we describe the development and optimization of a knowledge-based potential implemented in Q-Dock, a low-resolution flexible ligand docking approach. Low-resolution ligand docking techniques have been developed to deal with structural inaccuracies in predicted receptor models. Typically, docking accuracy falls off dramatically when apo or modeled receptors are used in docking experiments. The rapidly growing number of theoretically predicted protein structures requires robust methods that can utilize low-quality receptor structures as targets for ligand docking.
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