ProQ3: Improved model quality assessments using Rosetta energy terms

Karolis Uziela,Nanjiang Shu,Björn Wallner,Arne Elofsson

doi:10.1038/srep33509

Karolis Uziela, Nanjiang Shu + Show 2 more

Open Access

https://doi.org/10.1038/srep33509

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Abstract

Quality assessment of protein models using no other information than the structure of the model itself has been shown to be useful for structure prediction. Here, we introduce two novel methods, ProQRosFA and ProQRosCen, inspired by the state-of-art method ProQ2, but using a completely different description of a protein model. ProQ2 uses contacts and other features calculated from a model, while the new predictors are based on Rosetta energies: ProQRosFA uses the full-atom energy function that takes into account all atoms, while ProQRosCen uses the coarse-grained centroid energy function. The two new predictors also include residue conservation and terms corresponding to the agreement of a model with predicted secondary structure and surface area, as in ProQ2. We show that the performance of these predictors is on par with ProQ2 and significantly better than all other model quality assessment programs. Furthermore, we show that combining the input features from all three predictors, the resulting predictor ProQ3 performs better than any of the individual methods. ProQ3, ProQRosFA and ProQRosCen are freely available both as a webserver and stand-alone programs at http://proq3.bioinfo.se/.

Highlights

Protein Model Quality Assessment (MQA) has a long history in protein structure prediction
In 2006 we extended ProQ so that we estimated the quality of each residue in a protein model, and we estimated the quality of the entire model by summing up the quality for each residue[6]
We presented three novel model quality predictors: ProQRosFA, ProQRosCen and ProQ3. We show that these predictors by far outperform the original energy functions in Rosetta

Summary

Introduction

Protein Model Quality Assessment (MQA) has a long history in protein structure prediction. Methods to estimate free energies of protein models have been developed for more than 20 years[1,2,3] These methods are focused on identifying the native structure among a set of decoys and not necessarily have a good correlation with the relative quality of protein models. In 2006 we extended ProQ so that we estimated the quality of each residue in a protein model, and we estimated the quality of the entire model by summing up the quality for each residue[6] This method was shown to be rather successful in CASP77 and CASP88. Consensus estimators are based on the Pcons approach that we introduced in CASP511,12 In these methods, the quality of a model, or a residue, is estimated by comparing how similar it is to models generated by other methods. Various methods have been developed but the simplest methods such as 3D-Jury[13] and Pcons[14] are still among the best

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