QMEANDisCo—distance constraints applied on model quality estimation

By Gabriel Studer, Christine Rempfer, Andrew M. Waterhouse, Rafal Gumienny, J. Haas, Torsten Schwede. 2019

820 citations recorded by the source corpus.

Abstract Motivation Methods that estimate the quality of a 3D protein structure model in absence of an experimental reference structure are crucial to determine a model’s utility and potential applications. Single model methods assess individual models whereas consensus methods require an ensemble of models as input. In this work, we extend the single model composite score QMEAN that employs statistical potentials of mean force and agreement terms by introducing a consensus-based distance constraint (DisCo) score. Results DisCo exploits distance distributions from experimentally determined protein structures that are homologous to the model being assessed. Feed-forward neural networks are trained to adaptively weigh contributions by the multi-template DisCo score and classical single model QMEAN parameters. The result is the composite score QMEANDisCo, which combines the accuracy of consensus methods with the broad applicability of single model approaches. We also demonstrate that, despite being the de-facto standard for structure prediction benchmarking, CASP models are not the ideal data source to train predictive methods for model quality estimation. For performance assessment, QMEANDisCo is continuously benchmarked within the CAMEO project and participated in CASP13. For both, it ranks among the top performers and excels with low response times. Availability and implementation QMEANDisCo is available as web-server at https://swissmodel.expasy.org/qmean. The source code can be downloaded from https://git.scicore.unibas.ch/schwede/QMEAN. Supplementary information Supplementary data are available at Bioinformatics online.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Learning from Protein Structure with Geometric Vector Perceptrons (2020, background): “FaeNNz 8 (Studer et al., 2020), ProQ3D (Uziela et al., 2017), and VoroMQA 9 (Olechnoviˇc & Venclovas, 2017) learn a multi-layer perceptron or statistical potential on top of such structural features.”
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models (2021, methodology): “Several other protein structure prediction resources also use lDDT-based metrics (15,16).”