Improved model quality assessment using ProQ2

  title={Improved model quality assessment using ProQ2},
  author={Arjun Ray and Erik Lindahl and Bj{\"o}rn Wallner},
  journal={BMC Bioinformatics},
  pages={224 - 224}
BackgroundEmploying methods to assess the quality of modeled protein structures is now standard practice in bioinformatics. In a broad sense, the techniques can be divided into methods relying on consensus prediction on the one hand, and single-model methods on the other. Consensus methods frequently perform very well when there is a clear consensus, but this is not always the case. In particular, they frequently fail in selecting the best possible model in the hard cases (lacking consensus) or… 

ProQ2: estimation of model accuracy implemented in Rosetta

Based on results from CASP11 and CAMEO-QE, a continuous benchmark of quality estimation methods, it is clear that ProQ2 is the single-model method that performs best in both local and global model accuracy.

Machine learning methods for evaluating the quality of a single protein model using energy and structural properties

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Protein model quality assessment by learning-to-rank

A novel method has been presented to rank the models by their relative qualities and it is demonstrated that the proposed method is an effective methodology for model quality assessment and provides the state-of-the-art performance.

Machine Learning Approaches for Quality Assessment of Protein Structures

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Large-scale model quality assessment for improving protein tertiary structure prediction

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MASS: predict the global qualities of individual protein models using random forests and novel statistical potentials

MASS outperforms almost all of the four CASP11 top-performing single-model methods for global quality assessment in terms of all ofthe four evaluation criteria officially used by CASP, which measure the abilities to assign relative and absolute scores, identify the best model from decoys, and distinguish between good and bad models.

Smooth orientation-dependent scoring function for coarse-grained protein quality assessment

A novel single-model QA method called SBROD (Smooth Backbone-Reliant Orientation-Dependent) method, which uses only the backbone protein conformation, and hence it can be applied to scoring coarse-grained protein models, which is potentially applicable to continuous gradient-based optimization of protein conformations.



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Model quality assessment for membrane proteins

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MetaMQAP: A meta-server for the quality assessment of protein models

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Global and local model quality estimation at CASP8 using the scoring functions QMEAN and QMEANclust

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Evaluation of model quality predictions in CASP9

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Identification of correct regions in protein models using structural, alignment, and consensus information

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Model quality assessment using distance constraints from alignments

A new approach is presented, based on distance constraints extracted from alignments to templates of known structure, and is implemented in the Undertaker program for protein structure prediction, that is at least comparable with the best MQA methods that were assessed at CASP7.

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Toward the estimation of the absolute quality of individual protein structure models

The ability of the newly introduced QMEAN Z-score to detect experimentally solved protein structures containing significant errors, as well as to evaluate theoretical protein models is demonstrated.