Protein Structure Prediction by Protein Threading

  • Ying Xu
  • Zhijie Liu
  • Liming Cai
  • Dong Xu
Part of the Biological and Medical Physics, Biomedical Engineering book series (BIOMEDICAL)


The seminal work of Bowie, Lüthy, and Eisenberg (Bowie et al., 1991) on “the inverse protein folding problem” laid the foundation of protein structure prediction by protein threading. By using simple measures for fitness of different amino acid types to local structural environments defined in terms of solvent accessibility and protein secondary structure, the authors derived a simple and yet profoundly novel approach to assessing if a protein sequence fits well with a given protein structural fold. Their follow-up work (Elofsson et al., 1996; Fischer and Eisenberg, 1996; Fischer et al., 1996a,b) and the work by Jones, Taylor, and Thornton (Jones et al., 1992) on protein fold recognition led to the development of a new brand of powerful tools for protein structure prediction, which we now term “protein threading.” These computational tools have played a key role in extending the utility of all the experimentally solved structures by X-ray crystallography and nuclear magnetic resonance (NMR), providing structural models and functional predictions for many of the proteins encoded in the hundreds of genomes that have been sequenced up to now.


Energy Function Query Sequence Tree Decomposition Protein Structure Prediction Query Protein 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Alexandrov, N., and I. Shindyalov. 2003. PDP: protein domain parser. Bioinformatics 19:429–430.CrossRefGoogle Scholar
  2. Altschul, S.F., and W. Gish. 1996. Local alignment statistics. Methods Enzymol 266:460–480.CrossRefGoogle Scholar
  3. Altschul, S.F., T.L. Madden, A.A. Schaffer, J. Zhang, Z. Zhang, W. Miller, and D.J. Lipman. 1997. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25:3389–3402.CrossRefGoogle Scholar
  4. Andreeva, A., D. Howorth, S.E. Brenner, T. J. Hubbard, C. Chothia, and A.G. Murzin. 2004. SCOP database in 2004: Refinements integrate structure and sequence family data. Nucleic Acids Res. 32:D226–D229.CrossRefGoogle Scholar
  5. Apic, G., J. Gough, and S.A. Teichmann. 2001a. Domain combinations in archaeal, eubacterial and eukaryotic proteomes. J. Mol. Biol. 310:311–325.CrossRefGoogle Scholar
  6. Apic, G., J. Gough, and S.A. Teichmann. 2001b. An insight into domain combinations. Bioinformatics 17(Suppl. l):83–89.Google Scholar
  7. Arnborg, S., and A. Proskurowski. 1989. Linear time algorithms for NP-hard problems restricted to partial k-tree. Discrete Appl Math. 23:11–24.MATHMathSciNetCrossRefGoogle Scholar
  8. Bairoch, A., R. Apweiler, C.H. Wu, W.C. Barker, B. Boeckmann, S. Ferro, E. Gasteiger, H. Huang, R. Lopez, M. Magrane, M.J. Martin, D.A. Natale, C. O’Donovan, N. Redaschi, and L.S. Yeh. 2005. The Universal Protein Resource (UniProt). Nucleic Acids Res. 33:D154–D159.CrossRefGoogle Scholar
  9. Baker, D., and A. Sali. 2001. Protein structure prediction and structural genomics. Science 294:93–96.ADSCrossRefGoogle Scholar
  10. Barton, G.J., and M.J. Sternberg. 1987. A strategy for the rapid multiple alignment of protein sequences. Confidence levels from tertiary structure comparisons. J. Mol. Biol. 198:327–337.CrossRefGoogle Scholar
  11. Barton, G.J., and M.J. Sternberg. 1990. Flexible protein sequence patterns. A sensitive method to detect weak structural similarities. J. Mol. Biol. 212:389–402.CrossRefGoogle Scholar
  12. Bodlaender, H.L. 1996. A linear time algorithm for finding tree-decompositions of small treewidth. SIAMJ. Comput. 25:1305–1317.MATHMathSciNetCrossRefGoogle Scholar
  13. Bourne, P.E., and H. Weissig (eds.). 2003. Structural Bioinformatics. New York, Wiley-Liss.Google Scholar
  14. Bowie, J.U., R. Luthy, and D. Eisenberg. 1991. A method to identify protein sequences that fold into a known three-dimensional structure. Science 253:164–170.ADSCrossRefGoogle Scholar
  15. Branden, C., and J. Tooze. 1999. Introduction to Protein Structure, 2nd ed. New York, Garland Publishing.Google Scholar
  16. Brassard, G., and P. Bratley. 1996. Fundamentals of Algorithmes. Upper Saddle River, NJ, Prentice-Hall, pp. 265–266.Google Scholar
  17. Brenner, S.E., C. Chothia, T.J. Hubbard, and A.G. Murzin. 1996. Understanding protein structure: Using scop for fold interpretation. Methods Enzymol. 266:635–643.CrossRefGoogle Scholar
  18. Bryant, S.H., and S.F. Altschul. 1995. Statistics of sequence-structure threading. Curr. Opin. Struct. Biol. 5:236–244.CrossRefGoogle Scholar
  19. Calland, P.Y. 2003. On the structural complexity of a protein. Protein Eng. 16:79–86.CrossRefGoogle Scholar
  20. Chen, W., L. Mirny, and E.I. Shakhnovich. 2003. Fold recognition with minimal gaps. Proteins 51:531–543.CrossRefGoogle Scholar
  21. Clore, G.M., M.A. Robien, and A.M. Gronenborn. 1993. Exploring the limits of precision and accuracy of protein structures determined by nuclear magnetic resonance spectroscopy. J. Mol. Biol. 231:82–102.CrossRefGoogle Scholar
  22. Cohen, F.E., and M.J. Sternberg. 1980. On the use of chemically derived distance constraints in the prediction of protein structure with myoglobin as an example. J. Mol. Biol. 137:9–22.CrossRefGoogle Scholar
  23. Coulson, A.F., and J. Moult. 2002. A unifold, mesofold, and superfold model of protein fold use. Proteins 46:61–71.CrossRefGoogle Scholar
  24. de Bakker, P.I., A. Bateman, D.F. Burke, R.N. Miguel, K. Mizuguchi, J. Shi, H. Shirai, and T.L. Blundell. 2001. HOMSTRAD: Adding sequence information to structure-based alignments of homologous protein families. Bioinformatics 17:748–749.CrossRefGoogle Scholar
  25. de Haan, CA., K. Stadler, G.J. Godeke, B.J. Bosch, and P.J. Rottier. 2004. Cleavage inhibition of the murine coronavirus spike protein by a furin-like enzyme affects cell-cell but not virus-cell fusion. J. Virol 78:6048–6054.CrossRefGoogle Scholar
  26. De Witte, R.S., and E.I. Shakhnovich. 1996. SMoG: de novo design method based on simple, fast, and accurate free energy estimates. 1. Methodology and supporting evidence. J. Am. Chem. Soc. 118:11733–11744.CrossRefGoogle Scholar
  27. Dietmann, S., and L. Holm. 2001. Identification of homology in protein structure classification. Nat. Struct. Biol. 8:953–957.CrossRefGoogle Scholar
  28. Ding, C.H., and I. Dubchak. 2001. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17:349–358.CrossRefGoogle Scholar
  29. Doolittle, R.F. 1995. The multiplicity of domains in proteins. Annu. Rev. Biochem. 64:287–314.CrossRefGoogle Scholar
  30. Dutta, S., and H.M. Berman. 2005. Large macromolecular complexes in the Protein Data Bank: A status report. Structure 13:381–388.CrossRefGoogle Scholar
  31. Ekman, D., A.K. Bjorklund, J. Frey-Skott, and A. Elofsson. 2005. Multi-domain proteins in the three kingdoms of life: Orphan domains and other unassigned regions. J. Mol. Biol. 348:231–243.CrossRefGoogle Scholar
  32. Elofsson, A., D. Fischer, D.W. Rice, S.M. Le Grand, and D. Eisenberg. 1996. A study of combined structure/sequence profiles. Fold. Des. 1:451–461.CrossRefGoogle Scholar
  33. Fetrow, J.S., A. Giammona, A. Kolinski, and J. Skolnick. 2002. The protein folding problem: A biophysical enigma. Curr. Pharm. Biotechnol. 3:329–347.CrossRefGoogle Scholar
  34. Finkelstein, A.V, and O.B. Ptitsyn. 1987. Why do globular proteins fit the limited set of folding patterns? Prog. Biophys. Mol. Biol. 50: 171–190.CrossRefGoogle Scholar
  35. Fischer, D. 2000. Hybrid fold recognition: Combining sequence derived properties with evolutionary information. Pacific Symp. Biocomputing, Hawaii, pp. 119–130, World Scientific.Google Scholar
  36. Fischer, D. 2003. 3D-SHOTGUN: A novel, cooperative, fold-recognition metapredictor. Proteins 51:434–441.CrossRefGoogle Scholar
  37. Fischer, D., and D. Eisenberg. 1996. Protein fold recognition using sequence-derived predictions. Protein. Sci. 5:947–955.CrossRefGoogle Scholar
  38. Fischer, D., A. Elofsson, D. Rice, and D. Eisenberg. 1996a. Assessing the performance of fold recognition methods by means of a comprehensive benchmark. Pac. Symp. Biocomput. 300–318.Google Scholar
  39. Fischer, D., D. Rice, J.U. Bowie, and D. Eisenberg. 1996b. Assigning amino acid sequences to 3-dimensional protein folds. FASEB J. 10:126–136.Google Scholar
  40. Frederickson, G.N. 1991. Planar graph decomposition and all pairs shortest paths. J. Assoc. Comput. Mach. 38:162–204.MATHMathSciNetGoogle Scholar
  41. Gaasterland, T. 1998. Structural genomics: Bioinformatics in the driver’s seat. Nat. Biotechnol. 16:625–627.CrossRefGoogle Scholar
  42. Gelfand, M.S., E.V Koonin, and A.A. Mironov. 2000. Prediction of transcription regulatory sites in Archaea by a comparative genomic approach. Nucleic Acids Res. 28:695–705.CrossRefGoogle Scholar
  43. Gerlt, J.A., and P.C. Babbitt. 2000. Can sequence determine function? Genome Biol. l(5):reviews 0005.1-0005.10.Google Scholar
  44. Gerstein, M. 1997. A structural census of genomes: Comparing bacterial, eukaryotic, and archaeal genomes in terms of protein structure. J. Mol. Biol. 274:562–576.CrossRefGoogle Scholar
  45. Gerstein, M. 1998. How representative are the known structures of the proteins in a complete genome? A comprehensive structural census. Fold. Des. 3:497–512.CrossRefGoogle Scholar
  46. Gerstein, M., and H. Hegyi. 1998. Comparing genomes in terms of protein structure: Surveys of a finite parts list. FEMS Microbiol. Rev. 22:277–304.CrossRefGoogle Scholar
  47. Godzik, A. 2003. Fold recognition methods. Methods Biochem Anal. 44:525–546.Google Scholar
  48. Guo, J.T., K. Elliott, W.J. Chung, D. Xu, S. Passovets, and Y. Xu. 2004. PROSPECT-PSPP: An automatic computational pipeline for protein structure prediction. Nucleic Acids Res. 32(Web Server issue):W522–525.CrossRefGoogle Scholar
  49. Hobohm, U., M. Scharf, R. Schneider, and C. Sander. 1992. Selection of representative protein data sets. Protein Sci. 1:409–417.CrossRefGoogle Scholar
  50. Holm, L., and C. Sander. 1996a. Mapping the protein universe. Science 273:595–603.ADSCrossRefGoogle Scholar
  51. Holm, L., and C. Sander. 1996b. The FSSP database: Fold classification based on structure-structure alignment of proteins. Nucleic Acids Res. 24:206–209.CrossRefGoogle Scholar
  52. Jacobson, M.P., D.L. Pincus, C.S. Rapp, T.J. Day, B. Honig, D.E. Shaw, and R.A. Friesner. 2004. A hierarchical approach to all-atom protein loop prediction. Proteins 55:351–367.CrossRefGoogle Scholar
  53. Jiang, T., Y. Xu, and M. Zhang (eds.). 2002. Current Topics in Computational Molecular Biology. Cambridge, MA, MIT Press.Google Scholar
  54. Jones, D.T. 1999a. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292:195–202.CrossRefGoogle Scholar
  55. Jones, D.T. 1999b. GenTHREADER: An efficient, and reliable protein fold recognition method for genomic sequences. J. Mol. Biol. 287:797–815.CrossRefGoogle Scholar
  56. Jones, D.T., W.R. Taylor, and J.M. Thornton. 1992. A new approach to protein fold recognition. Nature 358:86–89.ADSCrossRefGoogle Scholar
  57. Kim, D, D. Xu, J.T. Guo, K. Ellrott, and Y. Xu. 2003. PROSPECT II: Protein structure prediction program for genome-scale applications. Protein Eng. 16:641–650.CrossRefGoogle Scholar
  58. Kinch, L.N., J.O. Wrabl, S.S. Krishna, I. Majumdar, R.I. Sadreyev, Y. Qi, J. Pei, H. Cheng, and N.V Grishin. 2003. CASP5 assessment of fold recognition target predictions. Proteins 53(Suppl.6):395–409.CrossRefGoogle Scholar
  59. Koonin, E.V, Y.I. Wolf, and G.P. Karev. 2002. The structure of the protein universe and genome evolution. Nature 420:218–223.ADSCrossRefGoogle Scholar
  60. Laskowski, R.A., M.W. MacArthur, D.S. Moss, and J.M. Thornton. 1993. PROCHECK: A program to check the stereochemical quality of protein structures. J.Appl. Crystallogr. 26:283–291.CrossRefGoogle Scholar
  61. Lathrop, R.H. 1994. The protein threading problem with sequence amino acid interaction preferences is NP-complete. Protein Eng. 7:1059–1068.CrossRefGoogle Scholar
  62. Lesk, A. 2001. Introduction to Protein Architecture: The Structural Biology of Proteins. London, Oxford University Press.Google Scholar
  63. Levitt, M., and M. Gerstein. 1998. A unified statistical framework for sequence comparison and structure comparison. Proc. Natl. Acad. Sci. USA 95:5913–5920.ADSCrossRefGoogle Scholar
  64. Li, H., R. Helling, C. Tang, and N. Wingreen. 1996. Emergence of preferred structures in a simple model of protein folding. Science 273:666–669.ADSCrossRefGoogle Scholar
  65. Li, H., C. Tang, and N.S. Wingreen. 1998. Are protein folds atypical? Proc. Natl. Acad. Sci. USA 95:4987–4990.ADSCrossRefGoogle Scholar
  66. Li, H., C. Tang, and N.S. Wingreen. 2002. Designability of protein structures: A lattice-model study using the Miyazawa-Jernigan matrix. Proteins 49:403–412.CrossRefGoogle Scholar
  67. Lu, H., and J. Skolnick. 2001. A distance-dependent atomic knowledge-based potential for improved protein structure selection. Proteins 44:223–232.CrossRefGoogle Scholar
  68. Li, X., and J. Liang. 2005. Geometric cooperativity and anti-cooperativity of three-body interactions in native proteins. Proteins 60:46–65.CrossRefGoogle Scholar
  69. Lu, L., A.K. Arakaki, H. Lu, and J. Skolnick. 2003. Multimeric threading-based prediction of protein-protein interactions on a genomic scale: Application to the Saccharomyces cerevisiae proteome. Genome Res. 13(6A): 1146–1154.CrossRefGoogle Scholar
  70. Lund, O., K. Frimand, J. Gorodkin, H. Bohr, J. Bohr, J. Hansen, and S. Brunak. 1997. Protein distance constraints predicted by neural networks and probability density functions. Protein Eng. 10:1241–1248.CrossRefGoogle Scholar
  71. Lundstrom, J., L. Rychlewski, J. Bujnicki, A. Elofsson. 2001. Peons: A neuralnetwork-based consensus predictor that improves fold recognition. Protein Sci. 10:2354–2362.CrossRefGoogle Scholar
  72. Madej, T., M.S. Boguski, and S.H. Bryant. 1995. Threading analysis suggests that the obese gene product may be a helical cytokine. FEBSLett. 373:13–18.CrossRefGoogle Scholar
  73. Makarova, K.S., L. Aravind, M.Y. Galperin, N.V Grishin, R.L. Tatusov, Y.I. Wolf, and E.V Koonin. 1999. Comparative genomics of the Archaea (Euryarchaeota): Evolution of conserved protein families, the stable core, and the variable shell. Genome Res. 9:608–628.Google Scholar
  74. May, R.M. 1988. How many species are there on earth. Science 241:1441–1449.ADSCrossRefGoogle Scholar
  75. McGuffin, L.J., and D.T Jones. 2003. Improvement of the GenTHREADER method for genomic fold recognition. Bioinformatics 19:874–881.CrossRefGoogle Scholar
  76. McGuffin, L.J., S.A. Street, K. Bryson, S.A. Sorensen, and D.T. Jones. 2004. The Genomic Threading Database: A comprehensive resource for structural annotations of the genomes from key organisms. Nucleic Acids Res. 32(Database issue):D196–199.CrossRefGoogle Scholar
  77. Melo, F., and E. Feytmans. 1997. Novel knowledge-based mean force potential at atomic level. J. Mol. Biol. 267:207–222.CrossRefGoogle Scholar
  78. Mirny, L.A., A.V. Finkelstein, and E.I. Shakhnovich. 2000. Statistical significance of protein structure prediction by threading. Proc. Natl. Acad. Sci. USA 97:9978–9983.ADSCrossRefGoogle Scholar
  79. Munson, P.I., and R.K. Singh. 1997. Statistical significance of hierarchical multi-body potentials based on Delaunay tessellation and their application in sequence-structure alignment. Protein Sci. 6:1467–1481.CrossRefGoogle Scholar
  80. Murzin, A.G., S.E. Brenner, T. Hubbard, and C. Chothia. 1995. SCOP: A structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol. 247:536–540.Google Scholar
  81. Orengo, CA., D.T. Jones, and J.M. Thornton. 1994. Protein superfamilies and domain superfolds. Nature 372:631–634.ADSCrossRefGoogle Scholar
  82. Orengo, CA., A.D. Michie, S. Jones, D.T. Jones, M.B. Swindells, and J.M. Thornton. 1997. CATH—A hierarchic classification of protein domain structures. Structure 5:1093–1108.CrossRefGoogle Scholar
  83. Orengo, C.A., and W.R. Taylor. 1993. A local alignment method for protein structure motifs. J. Mol. Biol. 233:488–497.CrossRefGoogle Scholar
  84. Panchenko, A., A. Marchler-Bauer, and S.H. Bryant. 1999. Threading with explicit models for evolutionary conservation of structure and sequence. Proteins Suppl. 3:133–140.CrossRefGoogle Scholar
  85. Panchenko, A.R., A. Marchler-Bauer, and S.H. Bryant. 2000. Combination of threading potentials and sequence profiles improves fold recognition. J. Mol. Biol. 296:1319–1331.CrossRefGoogle Scholar
  86. Papadimitriou, C., and H. Christos. 1998. Combinatorial Optimization: Algorithms and Complexity. New York, Dover Publications.Google Scholar
  87. Prestegard, J.H. 1998. New techniques in structural NMR-anisotropic interactions. Nat. Struct. Biol. 5(Suppl.):517–522.CrossRefGoogle Scholar
  88. Qu, Y., J.T. Guo, V. Olman, and Y. Xu. 2004a. Protein fold recognition through application of residual dipolar coupling data. Pac. Symp. Biocomput. pp. 459–470.Google Scholar
  89. Qu, Y., J.T. Guo, V. Olman, and Y. Xu. 2004b. Protein structure prediction using sparse dipolar coupling data. Nucleic Acids Res. 32:551–561.CrossRefGoogle Scholar
  90. Richardson, J.S. 1981. The anatomy and taxonomy of protein structure. Adv. Protein Chem. 34:167–339.CrossRefGoogle Scholar
  91. Robertson, N., and P.D. Seymour. 1986. Graph minors.2. algorithmic aspects of tree-width. J. Algorithm 7:309–322.MATHMathSciNetCrossRefGoogle Scholar
  92. Rost, B., R. Schneider, and C Sander. 1997. Protein fold recognition by predictionbased threading. J. Mol. Biol. 270:471–480.CrossRefGoogle Scholar
  93. Sali, A., and T.L. Blundell. 1990. Definition of general topological equivalence in protein structures. A procedure involving comparison of properties and relationships through simulated annealing and dynamic programming. J. Mol. Biol. 212:403–428.CrossRefGoogle Scholar
  94. Samudrala, R., and J. Moult. 1998. An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction. J. Mol. Biol. 275:895–916.CrossRefGoogle Scholar
  95. Shi, J., L. Blund, and K. Mizuguchi. 2001. FUGUE: Sequence-structure homology recognition using environment-specific substitution tables and structuredependent gap penalties. J. Mol. Biol. 310:243–257.CrossRefGoogle Scholar
  96. Sippl, M.J.. 1990. Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins. J. Mol. Biol. 213:859–883.CrossRefGoogle Scholar
  97. Sippl, M.J., P. Lackner, F.S. Domingues, A. Prlic, R. Malik, A. Andreeva, and M. Wiederstein. 2001. Assessment of the CASP4 fold recognition category. Proteins Suppl. 5:55–67.Google Scholar
  98. Skolnick, J., J.S. Fetrow, and A. Kolinski. 2000. Structural genomics and its importance for gene function analysis. Nat. Biotechnol 18:283–287.CrossRefGoogle Scholar
  99. Skolnick, J., and D. Kihara. 2001. Defrosting the frozen approximation: PROSPECTOR: A new approach to threading. Proteins 42:319–331.CrossRefGoogle Scholar
  100. Sommer, I., A. Zien, N. von Ohsen, R. Zimmer, and T. Lengauer. 2002. Confidence measures for protein fold recognition. Bioinformatics 18:802–812.CrossRefGoogle Scholar
  101. Song, Y., K. Ellrott, C. Liu, J. Guo, Y. Xu, and L. Cai. 2005. Tree decomposition based protein threading. Submitted.Google Scholar
  102. Sorenson, J.M., and T. Head-Gordon. 1999. Redesigning the hydrophobic core of a model beta-sheet protein: Destabilizing traps through a threading approach. Proteins 37:582–591.CrossRefGoogle Scholar
  103. Tatusov, R.L., M.Y. Galperin, D.A. Natale, and E.V. Koonin. 2000. The COG database: A tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28:33–36.CrossRefGoogle Scholar
  104. Taylor, W.R., and C.A. Orengo. 1989. Protein structure alignment. J. Mol. Biol. 208:1–22.CrossRefGoogle Scholar
  105. Tolman, J.R., J.M. Flanagan, M.A. Kennedy, and J.H. Prestegard. 1995. Nuclear magnetic dipole interactions in field-oriented proteins: Information for structure determination in solution. Proc. Natl. Acad. Sci. USA 92:9279–9283.ADSCrossRefGoogle Scholar
  106. Tsigelny, I.F. (eds.). 2002. Protein Structure Prediction: Bioinformatic Approach. La Jolla, CA, International University Line Publishers.Google Scholar
  107. Venclovas, C., A. Zemla, K. Fidelis, and J. Moult. 2003. Assessment of progress over the CASP experiments. Proteins 53(Suppl. 6):585–595.CrossRefGoogle Scholar
  108. von Grotthuss, M., L.S. Wyrwicz, and L. Rychlewski. 2003. mRNA cap-1 methyltransferase in the SARS genome. Cell 113:701–702.CrossRefGoogle Scholar
  109. Vriend, G. 1990. WHAT IF: A molecular modelling and drug design program. J. Mol. Graph. 8:52–56.CrossRefGoogle Scholar
  110. Wan, X.F., D. Ataman, and D. Xu. 2005. Application of computational biology in understanding emerging infectious diseases: Inferring the biological function for S-M complex of SARS-CoV inProgress in Bioinformatics. New York, Nova Science Publishers, pp. 55–80.Google Scholar
  111. Wang, G., and R.L. Dunbrack, Jr. 2003. PISCES: A protein sequence culling server. Bioinformatics 19:1589–1591.CrossRefGoogle Scholar
  112. Wang, Z.X. 1996. How many fold types of protein are there in nature? Proteins 26:186–191.CrossRefGoogle Scholar
  113. Westhead, D.R., V.P. Collura, M.D. Eldridge, M.A. Firth, J. Li, and C.W. Murray. 1995. Protein fold recognition by threading: Comparison of algorithms and analysis of results. Protein Eng. 8:1197–1204.CrossRefGoogle Scholar
  114. Wetlaufer, D.B. 1973. Nucleation, rapid folding, and globular intrachain regions in proteins. Proc. Natl. Acad. Sci. USA 70:697–701.ADSCrossRefGoogle Scholar
  115. Xu, D., K. Baburaj, C.B. Peterson, and Y. Xu. 2001. Model for the three-dimensional structure of vitronectin: Predictions for the multi-domain protein from threading and docking. Proteins 44:312–320.CrossRefGoogle Scholar
  116. Xu, D., D. Kim, P. Dam, M. Shah, E.C. Uberbacher, and Y. Xu. 2003. Characterization of protein structure and function at genome scale with a computational prediction pipeline. In Genetic Engineering, Principles and Methods, Vol. 25, J.K. Setlow (ed.). New York, Kluwer Academic/Plenum Publishers, pp. 269–293.Google Scholar
  117. Xu, D., M.A. Unseren, Y. Xu, and C. Uberbacher. 2000. Sequence-structure specificity of a knowledge based energy function at the secondary structure level. Bioinformatics 16:257–268.CrossRefGoogle Scholar
  118. Xu, J., F. Jiao, and B. Berger. 2005. A tree decomposition approach to protein structure prediction. Proceedings of 2005 IEEE Computational Systems Bioinformatics Conference, pp. 247–256.Google Scholar
  119. Xu, J., and M. Li. 2003. Assessment of RAPTOR’s linear programming approach in CAFASP3. Proteins 53(Suppl. 6):579–584.CrossRefGoogle Scholar
  120. Xu, J., M. Li, D. Kim, and Y. Xu. 2003a. RAPTOR: Optimal protein threading by linear programming. J. Bioinform. Comput. Biol 1:95–117.CrossRefGoogle Scholar
  121. Xu, J., M. Li, G. Lin, D. Kim, and Y. Xu. 2003b. Protein threading by linear programming. Pac. Symp. Biocomput. pp. 264–275.Google Scholar
  122. Xu, Y., and E.C. Uberbacher. 1996. A polynomial-time algorithm for a class of protein threading problems. Comput. Appl. Biosci. 12:511–517.Google Scholar
  123. Xu, Y., and D. Xu. 2000. Protein threading using PROSPECT: Design and evaluation. Proteins 40:343–354.CrossRefGoogle Scholar
  124. Xu, Y., D. Xu, O.H. Crawford, and J.R. Einstein. 2000c. A computational method for NMR-constrained protein threading. J. Comput. Biol. 7:449–467.CrossRefGoogle Scholar
  125. Xu, Y., D. Xu, O.H. Crawford, J.R. Einstein, F. Larimer, E. Uberbacher, M.A. Unseren, and G. Zhang. 1999. Protein threading by PROSPECT: A prediction experiment in CASP3. Protein Eng. 12:899–907.CrossRefGoogle Scholar
  126. Xu, Y., D. Xu, O.H. Crawford, J.R. Einstein, and E. Serpersu. 2000b. Protein structure determination using protein threading and sparse NMR data. Annual Conference on Research in Computational Molecular Biology, pp. 299–307.Google Scholar
  127. Xu, Y., D. Xu, and H.N. Gabow. 2000a. Protein domain decomposition using a graph-theoretic approach. Bioinformatics 16:1091–1104.CrossRefGoogle Scholar
  128. Xu, Y., D. Xu, and V Olman. 2002. A practical method for interpretation of threading scores: An application of neural network. Stat Sinica. 12:159–177.MATHMathSciNetGoogle Scholar
  129. Xu, Y., D. Xu, and E.C. Uberbacher. 1998. An efficient computational method for globally optimal threading. J. Comput. Biol. 5:597–614.CrossRefGoogle Scholar
  130. Yan, B., C. Pan, V.N. Olman, R.L. Hettich, and Y. Xu. 2005. A graph-theoretic approach for the separation of b and y ions in tandem mass spectra. Bioinformatics 21:563–574.CrossRefGoogle Scholar
  131. Ye, X., P.K. O’Neil, A.N. Foster, M.J. Gajda, J. Kosinski, M.A. Kurowski, J.M. Bujnicki, A.M. Friedman, and C. Bailey-Kellogg. 2004.Probabilistic cross-link analysis and experiment planning for high-throughput elucidation of protein structure. Protein Sci. 13:3298–3313.CrossRefGoogle Scholar
  132. Young, M.M., N. Tang, J.C. Hempel, C.M. Oshiro, E.W. Taylor, I.D. Kuntz, B.W. Gibson, and G. Dollinger. 2000. High throughput protein fold identification by using experimental constraints derived from intramolecular cross-links and mass spectrometry. Proc. Natl. Acad. Sci. USA 97:5802–5806.ADSCrossRefGoogle Scholar
  133. Zhang, B., L. Jaroszewski, L. Rychlewski, and A. Godzik. 1997. Similarities and differences between nonhomologous proteins with similar folds: Evaluation of threading strategies. Fold. Des. 2:307–317.CrossRefGoogle Scholar
  134. Zhang, C., and C. DeLisi. 1998. Estimating the number of protein folds. J. Mol. Biol. 284:1301–1305.CrossRefGoogle Scholar
  135. Zhang, Y., and J. Skolnick. 2004. Scoring function for automated assessment of protein structure template quality. Proteins 57:702–710.CrossRefGoogle Scholar
  136. Zhou, H.Y., and Y.Q. Zhou. 2002. Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci. 11:2714–2726.CrossRefGoogle Scholar
  137. Zhou, H., and Y. Zhou. 2005. Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments. Proteins 58:321–328.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Ying Xu
    • 1
  • Zhijie Liu
    • 2
  • Liming Cai
    • 3
  • Dong Xu
    • 4
  1. 1.Institute of Bioinformatics and Department of Biochemistry and Molecular BiologyUniversity of GeorgiaAthens
  2. 2.Department of Biochemistry and Molecular BiologyUniversity of GeorgiaAthens
  3. 3.Department of Computer ScienceUniversity of GeorgiaAthens
  4. 4.Computer Science DepartmentUniversity of Missouri-ColumbiaColumbia

Personalised recommendations