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Updated: Jun 23, 2026
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A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
QMEANclust: estimation of protein model quality by combining a composite scoring function with structural density
Pascal Benkert1, Torsten Schwede, Silvio Ce Tosatto
1Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056 Basel, Switzerland. pascal.benkert@unibas.ch
BMC Structural Biology
|May 22, 2009
Summary
Combining scoring functions improves protein model selection. Pre-filtering models with QMEAN enhances consensus scoring (QMEANclust), leading to more accurate protein structure predictions.
Area of Science:
- Computational Biology
- Structural Bioinformatics
- Protein Modeling
Background:
- Accurate protein model selection is vital for protein structure prediction.
- Existing scoring functions estimate single model quality or use ensemble information.
- Consensus methods excel but struggle with diverse model sets.
Purpose of the Study:
- To improve protein model selection by combining single-model and consensus scoring approaches.
- To introduce and evaluate the QMEANclust scoring function.
Main Methods:
- Enhanced QMEAN composite scoring function with an all-atom interaction potential.
- Pre-filtering models using QMEAN for structural consensus calculation (QMEANclust).
- Benchmarking on CASP7 targets and the MOULDER test set.
Main Results:
- QMEANclust achieved a 0.9 correlation coefficient for predicted quality and GDT_TS on CASP7 targets.
- QMEANclust outperformed other methods in the CASP7 quality estimation category.
- QMEAN excelled on individual models; QMEANclust requires near-native conformations in the ensemble.
Conclusions:
- Combining single-model scoring (QMEAN) with consensus scoring (QMEANclust) improves model selection.
- Consensus method performance is sensitive to the quality and composition of the model ensemble.
- CASP-based performance estimates may overestimate real-world applicability with smaller model sets.
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