Abstract
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 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.ResultsHere, 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.ConclusionsProQ2 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.
Highlights
Employing methods to assess the quality of modeled protein structures is standard practice in bioinformatics
Modeling of protein structure is a central challenge in structural bioinformatics, and holds the promise to identify classes of structure, and to provide detailed information about the specific structure and biological function of molecules
One of the most common prediction approaches in use today is to produce many alternative models, either from different alignments and templates [1,2,3,4] or by sampling different regions of the conformational space [5]. Given this set of models, some kind of scoring function is used to rank the different models based on their structural properties
Summary
Employing methods to assess the quality of modeled protein structures is standard practice in bioinformatics. One of the most common prediction approaches in use today is to produce many alternative models, either from different alignments and templates [1,2,3,4] or by sampling different regions of the conformational space [5]. Given this set of models, some kind of scoring function is used to rank the different models based on their structural properties. We mean methods that can be used for conformational sampling and that do not use any template information in the scoring of models
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