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1.  QA-RecombineIt: a server for quality assessment and recombination of protein models 
Nucleic Acids Research  2013;41(Web Server issue):W389-W397.
QA-RecombineIt provides a web interface to assess the quality of protein 3D structure models and to improve the accuracy of models by merging fragments of multiple input models. QA-RecombineIt has been developed for protein modelers who are working on difficult problems, have a set of different homology models and/or de novo models (from methods such as I-TASSER or ROSETTA) and would like to obtain one consensus model that incorporates the best parts into one structure that is internally coherent. An advanced mode is also available, in which one can modify the operation of the fragment recombination algorithm by manually identifying individual fragments or entire models to recombine. Our method produces up to 100 models that are expected to be on the average more accurate than the starting models. Therefore, our server may be useful for crystallographic protein structure determination, where protein models are used for Molecular Replacement to solve the phase problem. To address the latter possibility, a special feature was added to the QA-RecombineIt server. The QA-RecombineIt server can be freely accessed at http://iimcb.genesilico.pl/qarecombineit/.
doi:10.1093/nar/gkt408
PMCID: PMC3692112  PMID: 23700309
2.  The utility of comparative models and the local model quality for protein crystal structure determination by Molecular Replacement 
BMC Bioinformatics  2012;13:289.
Background
Computational models of protein structures were proved to be useful as search models in Molecular Replacement (MR), a common method to solve the phase problem faced by macromolecular crystallography. The success of MR depends on the accuracy of a search model. Unfortunately, this parameter remains unknown until the final structure of the target protein is determined. During the last few years, several Model Quality Assessment Programs (MQAPs) that predict the local accuracy of theoretical models have been developed. In this article, we analyze whether the application of MQAPs improves the utility of theoretical models in MR.
Results
For our dataset of 615 search models, the real local accuracy of a model increases the MR success ratio by 101% compared to corresponding polyalanine templates. On the contrary, when local model quality is not utilized in MR, the computational models solved only 4.5% more MR searches than polyalanine templates. For the same dataset of the 615 models, a workflow combining MR with predicted local accuracy of a model found 45% more correct solution than polyalanine templates. To predict such accuracy MetaMQAPclust, a “clustering MQAP” was used.
Conclusions
Using comparative models only marginally increases the MR success ratio in comparison to polyalanine structures of templates. However, the situation changes dramatically once comparative models are used together with their predicted local accuracy. A new functionality was added to the GeneSilico Fold Prediction Metaserver in order to build models that are more useful for MR searches. Additionally, we have developed a simple method, AmIgoMR (Am I good for MR?), to predict if an MR search with a template-based model for a given template is likely to find the correct solution.
doi:10.1186/1471-2105-13-289
PMCID: PMC3534383  PMID: 23126528
Molecular replacement; MR; MQAP; Model quality assessment; Protein structure prediction
3.  Identification and Classification of bcl Genes and Proteins of Bacillus cereus Group Organisms and Their Application in Bacillus anthracis Detection and Fingerprinting▿ †  
Applied and Environmental Microbiology  2009;75(22):7163-7172.
The Bacillus cereus group includes three closely related species, B. anthracis, B. cereus, and B. thuringiensis, which form a highly homogeneous subdivision of the genus Bacillus. One of these species, B. anthracis, has been identified as one of the most probable bacterial biowarfare agents. Here, we evaluate the sequence and length polymorphisms of the Bacillus collagen-like protein bcl genes as a basis for B. anthracis detection and fingerprinting. Five genes, designated bclA to bclE, are present in B. anthracis strains. Examination of bclABCDE sequences identified polymorphisms in bclB alleles of the B. cereus group organisms. These sequence polymorphisms allowed specific detection of B. anthracis strains by PCR using both genomic DNA and purified Bacillus spores in reactions. By exploiting the length variation of the bcl alleles it was demonstrated that the combined bclABCDE PCR products generate markedly different fingerprints for the B. anthracis Ames and Sterne strains. Moreover, we predict that bclABCDE length polymorphism creates unique signatures for B. anthracis strains, which facilitates identification of strains with specificity and confidence. Thus, we present a new diagnostic concept for B. anthracis detection and fingerprinting, which can be used alone or in combination with previously established typing platforms.
doi:10.1128/AEM.01069-09
PMCID: PMC2786505  PMID: 19767469
4.  MetaMQAP: A meta-server for the quality assessment of protein models 
BMC Bioinformatics  2008;9:403.
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 Ångströms) 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.
Conclusion
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
doi:10.1186/1471-2105-9-403
PMCID: PMC2573893  PMID: 18823532

Results 1-4 (4)