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Improved model quality assessment using ProQ2.

Ray A, Lindahl E, Wallner B - BMC Bioinformatics (2012)

Bottom Line: Improved performance is obtained by combining previously used features with updated structural and predicted features.The most important contribution can be attributed to the use of profile weighting of the residue specific features and the use features averaged over the whole model even though the prediction is still local.The absolute quality assessment of the models at both local and global level is also improved.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Theoretical Physics & Swedish eScience Research Center, Royal Institute of Technology, Stockholm, Sweden.

ABSTRACT

Background: Employing 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 in the easy cases where models are very similar. In contrast, single-model methods do not suffer from these drawbacks and could potentially be applied on any protein of interest to assess quality or as a scoring function for sampling-based refinement.

Results: Here, we present a new single-model method, ProQ2, based on ideas from its predecessor, ProQ. ProQ2 is a model quality assessment algorithm that uses support vector machines to predict local as well as global quality of protein models. Improved performance is obtained by combining previously used features with updated structural and predicted features. The most important contribution can be attributed to the use of profile weighting of the residue specific features and the use features averaged over the whole model even though the prediction is still local.

Conclusions: ProQ2 is significantly better than its predecessors at detecting high quality models, improving the sum of Z-scores for the selected first-ranked models by 20% and 32% compared to the second-best single-model method in CASP8 and CASP9, respectively. The absolute quality assessment of the models at both local and global level is also improved. The Pearson's correlation between the correct and local predicted score is improved from 0.59 to 0.70 on CASP8 and from 0.62 to 0.68 on CASP9; for global score to the correct GDT_TS from 0.75 to 0.80 and from 0.77 to 0.80 again compared to the second-best single methods in CASP8 and CASP9, respectively. ProQ2 is available at http://proq2.wallnerlab.org.

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Optimization of linear combination of ProQ2 and Pcons to improve model selection.
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Figure 1: Optimization of linear combination of ProQ2 and Pcons to improve model selection.

Mentions: where k was optimized to k=0.8 to maximize GDT1 (Figure 1). Other ways to combine the two scores were tried but this linear combination showed the best performance. Since both the ProQ2 and the Pcons score reflect model correctness, a linear combination makes sense. In the case of free-modeling targets the consensus score will be low and most of the selection will be made on the ProQ2 score. Analogously, in the case of easy comparative modeling targets the consensus score will be high but it will be high for most of the models, and the selection will again essentially be done by the ProQ2 score.


Improved model quality assessment using ProQ2.

Ray A, Lindahl E, Wallner B - BMC Bioinformatics (2012)

Optimization of linear combination of ProQ2 and Pcons to improve model selection.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3584948&req=5

Figure 1: Optimization of linear combination of ProQ2 and Pcons to improve model selection.
Mentions: where k was optimized to k=0.8 to maximize GDT1 (Figure 1). Other ways to combine the two scores were tried but this linear combination showed the best performance. Since both the ProQ2 and the Pcons score reflect model correctness, a linear combination makes sense. In the case of free-modeling targets the consensus score will be low and most of the selection will be made on the ProQ2 score. Analogously, in the case of easy comparative modeling targets the consensus score will be high but it will be high for most of the models, and the selection will again essentially be done by the ProQ2 score.

Bottom Line: Improved performance is obtained by combining previously used features with updated structural and predicted features.The most important contribution can be attributed to the use of profile weighting of the residue specific features and the use features averaged over the whole model even though the prediction is still local.The absolute quality assessment of the models at both local and global level is also improved.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Theoretical Physics & Swedish eScience Research Center, Royal Institute of Technology, Stockholm, Sweden.

ABSTRACT

Background: Employing 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 in the easy cases where models are very similar. In contrast, single-model methods do not suffer from these drawbacks and could potentially be applied on any protein of interest to assess quality or as a scoring function for sampling-based refinement.

Results: Here, we present a new single-model method, ProQ2, based on ideas from its predecessor, ProQ. ProQ2 is a model quality assessment algorithm that uses support vector machines to predict local as well as global quality of protein models. Improved performance is obtained by combining previously used features with updated structural and predicted features. The most important contribution can be attributed to the use of profile weighting of the residue specific features and the use features averaged over the whole model even though the prediction is still local.

Conclusions: ProQ2 is significantly better than its predecessors at detecting high quality models, improving the sum of Z-scores for the selected first-ranked models by 20% and 32% compared to the second-best single-model method in CASP8 and CASP9, respectively. The absolute quality assessment of the models at both local and global level is also improved. The Pearson's correlation between the correct and local predicted score is improved from 0.59 to 0.70 on CASP8 and from 0.62 to 0.68 on CASP9; for global score to the correct GDT_TS from 0.75 to 0.80 and from 0.77 to 0.80 again compared to the second-best single methods in CASP8 and CASP9, respectively. ProQ2 is available at http://proq2.wallnerlab.org.

Show MeSH
Related in: MedlinePlus