期刊论文详细信息
BMC Bioinformatics
The utility of comparative models and the local model quality for protein crystal structure determination by Molecular Replacement
Marcin Pawlowski1  Janusz M Bujnicki2 
[1] Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, Warsaw, PL-02-109, Poland
[2] Laboratory of Bioinformatics, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Umultowska 89, Poznan, PL-61-614, Poland
关键词: Protein structure prediction;    Model quality assessment;    MQAP;    MR;    Molecular replacement;   
Others  :  1088081
DOI  :  10.1186/1471-2105-13-289
 received in 2012-07-04, accepted in 2012-10-29,  发布年份 2012
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【 摘 要 】

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.

【 授权许可】

   
2012 Pawlowski and Bujnicki; licensee BioMed Central Ltd.

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【 参考文献 】
  • [1]Rossmann MG, Blow DM: The detection of sub-units within the crystallographic asymmetric unit. Acta Crystallographica 1962, 15(1):24-31.
  • [2]Crowther R: The Molecular Replacement Method. Edited by Rossmann MG. New York: Gordon and Breach; 2010:271-282.
  • [3]Fujinaga M, Read R: Experiences with a new translation-function program. Journal of Applied Crystallography 1987, 20(6):517-521.
  • [4]McCoy AJ, Grosse-Kunstleve RW, Storoni LC, Read RJ: Likelihood-enhanced fast translation functions. Acta Crystallographica Section D: Biological Crystallography 2005, 61(4):458-464.
  • [5]Navaza J, Vernoslova E: On the fast translation functions for molecular replacement. Acta Crystallographica Section A: Foundations of Crystallography 1995, 51(4):445-449.
  • [6]Read RJ: Pushing the boundaries of molecular replacement with maximum likelihood. Acta Crystallographica Section D: Biological Crystallography 2001, 57(10):1373-1382.
  • [7]Storoni LC, McCoy AJ, Read RJ: Likelihood-enhanced fast rotation functions. Acta Crystallographica Section D: Biological Crystallography 2004, 60(3):432-438.
  • [8]Schwarzenbacher R, Godzik A, Grzechnik SK, Jaroszewski L: The importance of alignment accuracy for molecular replacement. Acta Crystallogr D: Biol Crystallogr 2004, 60(Pt 7):1229-1236.
  • [9]Raimondo D, Giorgetti A, Giorgetti A, Bosi S, Tramontano A: Automatic procedure for using models of proteins in molecular replacement. Proteins 2007, 66(3):689-696.
  • [10]Giorgetti A, Raimondo D, Miele AE, Tramontano A: Evaluating the usefulness of protein structure models for molecular replacement. Bioinformatics 2005, 21(Suppl 2):ii72-ii76.
  • [11]Lazaridis T, Karplus M: Effective energy functions for protein structure prediction. Curr Opin Struct Biol 2000, 10(2):139-145.
  • [12]McGuffin LJ: Benchmarking consensus model quality assessment for protein fold recognition. BMC Bioinforma 2007, 8(1):345. BioMed Central Full Text
  • [13]Pawlowski M, Gajda MJ, Matlak R, Bujnicki JM: MetaMQAP: a meta-server for the quality assessment of protein models. BMC Bioinforma 2008, 9(1):403. BioMed Central Full Text
  • [14]Holm L, Sander C: Protein structure comparison by alignment of distance matrices. J Mol Biol 1993, 233(1):123-138.
  • [15]Sali A, Blundell TL: Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 1993, 234(3):779-815.
  • [16]Read RJ: Assessing high accuracy models. CASP 7th meeting 2006. http://predictioncenterorg/casp7/meeting/presentations/Presentations_assessors/CASP7_HA_RJReadpdf webcite
  • [17]Zemla A: LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Research 2003, 31(13):3370.
  • [18]Luthy R, Bowie JU, Eisenberg D: Assessment of protein models with three-dimensional profiles. Nature 1992, 356(6364):83-85.
  • [19]Sippl MJ: Recognition of errors in three-dimensional structures of proteins. Proteins 1993, 17(4):355-362.
  • [20]Krishnamoorthy B, Tropsha A: Development of a four-body statistical pseudo-potential to discriminate native from non-native protein conformations. Bioinformatics 2003, 19(12):1540-1548.
  • [21]Melo F, Feytmans E: Assessing protein structures with a non-local atomic interaction energy. J Mol Biol 1998, 277(5):1141-1152.
  • [22]Pontius J, Richelle J, Wodak SJ: Deviations from standard atomic volumes as a quality measure for protein crystal structures. J Mol Biol 1996, 264(1):121-136.
  • [23]Lin K, May AC, Taylor WR: Threading using neural nEtwork (TUNE): the measure of protein sequence-structure compatibility. Bioinformatics 2002, 18(10):1350-1357.
  • [24]Boniecki M, Rotkiewicz P, Skolnick J, Kolinski A: Protein fragment reconstruction using various modeling techniques. J Comput Aided Mol Des 2003, 17(11):725-738.
  • [25]Wallner B, Elofsson A: Identification of correct regions in protein models using structural, alignment, and consensus information. Protein Sci 2006, 15(4):900-913.
  • [26]Benkert P, Schwede T, Tosatto SCE: QMEANclust: estimation of protein model quality by combining a composite scoring function with structural density information. BMC Structural Biology 2009, 9(1):35. BioMed Central Full Text
  • [27]Ginalski K, Elofsson A, Fischer D, Rychlewski L: 3D-Jury: a simple approach to improve protein structure predictions. Bioinformatics 2003, 19(8):1015-1018.
  • [28]Kryshtafovych A, Fidelis K, Tramontano A: Evaluation of model quality predictions in CASP9. Proteins: Structure, Function, and Bioinformatics 2011.
  • [29]Vagin A, Teplyakov A: MOLREP: an automated program for molecular replacement. Journal of Applied Crystallography 1997, 30(6):1022-1025.
  • [30]Navaza J: AMoRe: an automated package for molecular replacement. Acta Crystallographica Section A: Foundations of Crystallography 1994, 50(2):157-163.
  • [31]The CCP4 suite: programs for protein crystallography Acta Crystallogr D Biol Crystallogr 1994, 50(Pt 5):760-763.
  • [32]McCoy AJ, Grosse-Kunstleve RW, Adams PD, Winn MD, Storoni LC, Read RJ: Phaser crystallographic software. Journal of Applied Crystallography 2007, 40(4):658-674.
  • [33]Murshudov G, Vagin AA, Dodson EJ: Refinement of macromolecular structures by the maximum-likelihood method. Acta Crystallographica Section D: Biological Crystallography 1997, 53(3):240-255.
  • [34]Perrakis A, Harkiolaki M, Wilson KS, Lamzin VS: ARP/wARP and molecular replacement. Acta Crystallographica Section D: Biological Crystallography 2001, 57(10):1445-1450.
  • [35]Zemla A: LGA: A method for finding 3D similarities in protein structures. Nucleic Acids Res 2003, 31(13):3370-3374.
  • [36]Hanley JA: Characteristic (ROC) Curvel. Radiology 1982, 743:29-36.
  • [37]Soding J: Protein homology detection by HMM-HMM comparison. Bioinformatics 2005, 21(7):951-960.
  • [38]Kurowski MA, Bujnicki JM: GeneSilico protein structure prediction meta-server. Nucleic Acids Res 2003, 31(13):3305-3307.
  • [39]Schwarzenbacher R, Godzik A, Jaroszewski L: The JCSG MR pipeline: optimized alignments, multiple models and parallel searches. Acta Crystallogr D: Biol Crystallogr 2008, 64(Pt 1):133-140.
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