Assessing the accuracy of template-based structure prediction metaservers by comparison with structural genomics structures

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• 作者：Dominik Gront (1) (2) (3)
  Marek Grabowski (1) (3)
  Matthew D. Zimmerman (1) (3)
  John Raynor (1) (3)
  Karolina L. Tkaczuk (1) (3)
  Wladek Minor (1) (3)
  
• 关键词：Structural genomics ; Protein structure ; Protein structure prediction ; Protein ; Structure modeling
• 刊名：Journal of Structural and Functional Genomics
• 出版年：2012
• 出版时间：December 2012
• 年：2012
• 卷：13
• 期：4
• 页码：213-225
• 全文大小：1502KB
• 参考文献：1. Grabowski M et al (2007) Structural genomics: keeping up with expanding knowledge of the protein universe. Curr Opin Struct Biol 17(3):347-53 CrossRef
  2. Levitt M (2009) Nature of the protein universe. Proc Natl Acad Sci USA 106(27):11079-1084 CrossRef
  3. Anfinsen CB (1973) Principles that govern the folding of protein chains. Science 181(96):223-30 CrossRef
  4. Bradley P, Misura KM, Baker D (2005) Toward high-resolution de novo structure prediction for small proteins. Science 309(5742):1868-871 CrossRef
  5. Vitkup D et al (2001) Completeness in structural genomics. Nat Struct Biol 8(6):559-66 CrossRef
  6. Moult J (2005) A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. Curr Opin Struct Biol
  7. Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234(3):779-15 CrossRef
  8. Kolinski A, Gront D (2007) Comparative modeling without implicit sequence alignments. Bioinformatics 23(19):2522-527 CrossRef
  9. Bujnicki JM et al (2001) Structure prediction meta server. Bioinformatics 17(8):750-51 CrossRef
  10. Chen L et al (2004) TargetDB: a target registration database for structural genomics projects. Bioinformatics 20(16):2860-862 CrossRef
  11. Ginalski K et al (2003) 3D-Jury: a simple approach to improve protein structure predictions. Bioinformatics 19(8):1015-018 CrossRef
  12. Kurowski MA, Bujnicki JM (2003) GeneSilico protein structure prediction meta-server. Nucleic Acids Res 31(13):3305-307 CrossRef
  13. Wallner B, Elofsson A (2005) Pcons5: combining consensus, structural evaluation and fold recognition scores. Bioinformatics 21(23):4248-254 CrossRef
  14. Alexandrov N, Shindyalov I (2003) PDP: protein domain parser. Bioinformatics 19(3):429-30 CrossRef
  15. Kabsch W (1976) Solution for best rotation to relate two sets of vectors. Acta Crystallogr A 32:922-23 CrossRef
  16. Gront D, Kolinski A (2006) BioShell—a package of tools for structural biology computations. Bioinformatics 22(5):621-22 CrossRef
  17. Gront D, Kolinski A (2008) Utility library for structural bioinformatics. Bioinformatics 24(4):584-85 CrossRef
  18. Smith TF, Waterman MS (1981) Identification of common molecular subsequences. J Mol Biol 147(1):195-97 CrossRef
  19. Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks. Proc Nat Acad Sci USA 89(22):10915-0919 CrossRef
  20. Sander C, Schneider R (1991) Database of homology-derived protein sequences and the structural meaning of sequence alignment. Proteins: Struct Function Genetics 9:56-8 CrossRef
  21. Altschul SF et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389-402 CrossRef
  22. Moult J et al (2009) Critical assessment of methods of protein structure prediction-Round VIII. Prot-Struct Funct Bioinform 77:1- CrossRef
  23. Venclovas C, Margelevicius M (2009) The use of automatic tools and human expertise in template-based modeling of CASP8 target proteins. Prot-Struct Funct Bioinform 77:81-8 CrossRef
  24. Moult J et al (2011) Critical assessment of methods of protein structure prediction (CASP)—round IX. Proteins 79(Suppl 10):1- CrossRef
  25. Kryshtafovych A, Fidelis K, Moult J (2011) CASP9 results compared to those of previous CASP experiments. Proteins 79(Suppl 10):196-07 CrossRef
  26. Moult J et al (2009) Critical assessment of methods of protein structure prediction-Round VIII. Proteins 77(Suppl 9):1- CrossRef
  27. Soding J (2005) Protein homology detection by HMM–HMM comparison. Bioinformatics 21(7):951-60 CrossRef
  28. Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9:40 CrossRef
  29. Xu D, Zhang Y (2012) Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins 80(7):1715-735
  30. Gront D et al (2011) Generalized fragment picking in Rosetta: design, protocols and applications. PLoS ONE 6(8):e23294 CrossRef
  31. Cameo Project (2012) Available from: www.cameo3d.org
  32. Kolinski A (2004) Protein modeling and structure prediction with a reduced representation. Acta Biochim Pol 51(2):349-71
  33. Kim DE, Chivian D, Baker D (2004) Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res 32(Web Server issue):W526-31
  34. Levitt M (2007) Growth of novel protein structural data. Proc Natl Acad Sci USA 104(9):3183-188 CrossRef
  35. Murzin AG et al (1995) SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 247(4):536-40
  36. Baker D, Sali A (2001) Protein structure prediction and structural genomics. Science 294(5540):93-6 CrossRef
  
• 作者单位：Dominik Gront (1) (2) (3)
  Marek Grabowski (1) (3)
  Matthew D. Zimmerman (1) (3)
  John Raynor (1) (3)
  Karolina L. Tkaczuk (1) (3)
  Wladek Minor (1) (3)
  
  1. Department of Molecular Physiology and Biological Physics, University of Virginia, 1340 Jefferson Park Avenue, Charlottesville, VA, 22908, USA
  2. Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
  3. Midwest Center for Structural Genomics, New York, NY, USA
  

文摘

The explosion of the size of the universe of known protein sequences has stimulated two complementary approaches to structural mapping of these sequences: theoretical structure prediction and experimental determination by structural genomics (SG). In this work, we assess the accuracy of structure prediction by two automated template-based structure prediction metaservers (genesilico.pl and bioinfo.pl) by measuring the structural similarity of the predicted models to corresponding experimental models determined a posteriori. Of 199 targets chosen from SG programs, the metaservers predicted the structures of about a fourth of them “correctly.-(In this case, “correct-was defined as placing more than 70?% of the alpha carbon atoms in the model within 2?? of the experimentally determined positions.) Almost all of the targets that could be modeled to this accuracy were those with an available template in the Protein Data Bank (PDB) with more than 25?% sequence identity. The majority of those SG targets with lower sequence identity to structures in the PDB were not predicted by the metaservers with this accuracy. We also compared metaserver results to CASP8 results, finding that the models obtained by participants in the CASP competition were significantly better than those produced by the metaservers.