DockQ: A Quality Measure for Protein-Protein Docking Models

By Sankar Basu, Björn Wallner. 2016

436 citations recorded by the source corpus.

The state-of-the-art to assess the structural quality of docking models is currently based on three related yet independent quality measures: Fnat, LRMS, and iRMS as proposed and standardized by CAPRI. 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. a model with relatively poor LRMS (>10Å) might still qualify as 'acceptable' with a descent Fnat (>0.50) and iRMS (<3.0Å). 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. 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. Here, we present DockQ, a continuous protein-protein docking model quality measure derived by combining Fnat, LRMS, and iRMS to a single score in the range [0, 1] that can be used to assess the quality of protein 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. An average PPV of 94% at 90% Recall demonstrating that there is no need to apply predefined ad-hoc cutoffs to classify docking models. 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. 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. DockQ is available at http://github.com/bjornwallner/DockQ/

Subfields: Computational Theory and Mathematics, Molecular Biology, Computational Biology & Bioinformatics, Computational Chemistry & Drug Discovery

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Citations in the corpus

AFsample: improving multimer prediction with AlphaFold using massive sampling (2023, methodology): “The DockQ (Basu and Wallner 2016) scores for all methods that participated in CASP15 were downloaded from the CASP15 website.”
Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment (2019, neutral): “This protocol was complemented with the DockQ score, 59 a continuous quality metric that integrates the main quality measures of the standard CAPRI protocol, as detailed below.”
Improved multimer prediction using massive sampling with AlphaFold in CASP15 (2023, methodology): “To analyze our performance in CASP15, we used an updated version of DockQ (Mirabello and 98 Wallner, 2023), that given a chain mapping, calculates a global DockQ score by averaging the 99 DockQ (Basu and Wallner, 2016) score for each interface weighted by the size of the interface.”
New prediction categories in CASP15 (2023, background): “51 Notably, DockQ evaluates pairwise interfaces, necessitating the introduction of a weighted average metric — termed DockQ-wave — to effectively score higher-order complexes.”
Harnessing protein folding neural networks for peptide–protein docking (2021, methodology): “The DockQ model quality metric was computed with the default settings and parameters, using a two-chain con ﬁ gura-tion (receptor: A, peptide: B) 40 .”
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (2021, background): “…several steps: ﬁrst, they sample thousands or millions of complex candidates; second, they use a scoring function for ranking (Moal et al., 2013; Basu and Wallner, 2016; Launay et al., 2020; Eismann et al., 2020); ﬁnally, top-ranked candidates undergo a structure reﬁnement process using energy…”
pyconsFold: a fast and easy tool for modeling and docking using distance predictions (2021, background): “Replacing the interchain distances with predicted contacts significantly lowers the DockQ-score (Basu and Wallner, 2016), but the individual models TMscore does not decrease to the same extent, indicating that interchain contact predictions are less accurate.”
Performance and its limits in rigid body protein-protein docking (2020, positive): “More recently a continuous score called DockQ was developed that encapsulated the above three measures (Basu and Wallner, 2016).”