title: MetaMQAP: a meta-server for the quality assessment of protein models.
abstract: BACKGROUND
  Computational models of protein structure are usually inaccurate and exhibit significant deviations from the true structure. The utility of models depends on the degree of these deviations. A number of predictive methods have been developed to discriminate between the globally incorrect and approximately correct models. However, only a few methods predict correctness of different parts of computational models. Several Model Quality Assessment Programs (MQAPs) have been developed to detect local inaccuracies in unrefined crystallographic models, but it is not known if they are useful for computational models, which usually exhibit different and much more severe errors.
  
  
  RESULTS
  The ability to identify local errors in models was tested for eight MQAPs: VERIFY3D, PROSA, BALA, ANOLEA, PROVE, TUNE, REFINER, PROQRES on 8251 models from the CASP-5 and CASP-6 experiments, by calculating the Spearman's rank correlation coefficients between per-residue scores of these methods and local deviations between C-alpha atoms in the models vs. experimental structures. As a reference, we calculated the value of correlation between the local deviations and trivial features that can be calculated for each residue directly from the models, i.e. solvent accessibility, depth in the structure, and the number of local and non-local neighbours. We found that absolute correlations of scores returned by the MQAPs and local deviations were poor for all methods. In addition, scores of PROQRES and several other MQAPs strongly correlate with 'trivial' features. Therefore, we developed MetaMQAP, a meta-predictor based on a multivariate regression model, which uses scores of the above-mentioned methods, but in which trivial parameters are controlled. MetaMQAP predicts the absolute deviation (in Angströms) of individual C-alpha atoms between the model and the unknown true structure as well as global deviations (expressed as root mean square deviation and GDT_TS scores). Local model accuracy predicted by MetaMQAP shows an impressive correlation coefficient of 0.7 with true deviations from native structures, a significant improvement over all constituent primary MQAP scores. The global MetaMQAP score is correlated with model GDT_TS on the level of 0.89.
  
  
  CONCLUSIONS
  Finally, we compared our method with the MQAPs that scored best in the 7th edition of CASP, using CASP7 server models (not included in the MetaMQAP training set) as the test data. In our benchmark, MetaMQAP is outperformed only by PCONS6 and method QA_556 - methods that require comparison of multiple alternative models and score each of them depending on its similarity to other models. MetaMQAP is however the best among methods capable of evaluating just single models. We implemented the MetaMQAP as a web server available for free use by all academic users at the URL https://genesilico.pl/toolkit/
authors.affiliation: Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, PL-02-109 Warsaw, Poland. marcinp@genesilico.pl
authors.forename: Marcin
authors.initials: M
authors.lastname: Pawlowski
authors.forename: Michal J
authors.initials: MJ
authors.lastname: Gajda
authors.forename: Ryszard
authors.initials: R
authors.lastname: Matlak
authors.forename: Janusz M
authors.initials: JM
authors.lastname: Bujnicki
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D003628:Database Management Systems; D015201:Meta-Analysis as Topic; D008958:Models, Molecular; D015999:Multivariate Analysis; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry; D011786:Quality Control; D012044:Regression Analysis; D012107:Research Design / Q000706:statistics & numerical data*
chemical_list: D011506:Proteins