Bioinformatics pp 225-242 | Cite as

Protein Structure Prediction



Owing to significant efforts in genome sequencing over nearly three decades (McPherson et al. 2001; Venter et al. 2001), gene sequences from many organisms have been deduced. Over 100 million nucleotide sequences from over 300 thousand different organisms have been deposited in the major DNA databases, DDBJ/EMBL/GenBank (Benson et al. 2003; Miyazaki et al. 2003; Kulikova et al. 2004), totaling almost 200 billion nucleotide bases (about the number of stars in the Milky Way). Over 5 million of these nucleotide sequences have been translated into amino acid sequences and deposited in the UniProtKB database (Release 12.8) (Bairoch et al. 2005). The protein sequences in UniParc triple this number. However, the protein sequences themselves are usually insufficient for determining protein function as the biological function of proteins is intrinsically linked to three dimensional protein structure (Skolnick et al. 2000).


Protein Data Bank Protein Structure Prediction Free Modeling Pfam Family Generalize Born 
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.


  1. Bairoch A, Apweiler R, Wu CH, Barker WC, Boeckmann B, Ferro S et al (2005) The Universal Protein Resource (UniProt). Nucleic Acids Res 33(Database issue):D154–D159CrossRefPubMedGoogle Scholar
  2. Bateman A, Coin L, Durbin R, Finn RD, Hollich V, Griffiths-Jones S et al (2004) The Pfam protein families database. Nucleic Acids Res 32(Database issue):D138–D141CrossRefPubMedGoogle Scholar
  3. Battey JN, Kopp J, Bordoli L, Read RJ, Clarke ND, Schwede T (2007) Automated server predictions in CASP7. Proteins 69(S8):68–82CrossRefPubMedGoogle Scholar
  4. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL (2003) GenBank. Nucleic Acids Res 31(1):23–27CrossRefPubMedGoogle Scholar
  5. Berendsen HJC, Postma JPM, van Gunsteren WF, Hermans J (1981) Interaction models for water in relation to protein hydration. Intermolecular forces, Reidel, Dordrecht, The NetherlandsGoogle Scholar
  6. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H et al (2000) The Protein Data Bank. Nucleic Acids Res 28(1):235–242CrossRefPubMedGoogle Scholar
  7. Bowie JU, Eisenberg D (1994) An evolutionary approach to folding small alpha-helical proteins that uses sequence information and an empirical guiding fitness function. Proc Natl Acad Sci U S A 91(10):4436–4440CrossRefPubMedGoogle Scholar
  8. Bowie JU, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253:164–170CrossRefPubMedGoogle Scholar
  9. Bradley P, Misura KM, Baker D (2005) Toward high-resolution de novo structure prediction for small proteins. Science 309(5742):1868–1871CrossRefPubMedGoogle Scholar
  10. Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4(2):187–217CrossRefGoogle Scholar
  11. Burley SK, Almo SC, Bonanno JB, Capel M, Chance MR, Gaasterland T et al (1999) Structural genomics: beyond the human genome project. Nat Genet 23(2):151–157CrossRefPubMedGoogle Scholar
  12. Case DA, Pearlman DA, Caldwell JA, Cheatham TE, Ross WS (1997) AMBER 5.0. University of California, San Francisco, CAGoogle Scholar
  13. Chandonia JM, Brenner SE (2006) The impact of structural genomics: expectations and outcomes. Science 311(5759):347–351CrossRefPubMedGoogle Scholar
  14. Chen J, Brooks CL III (2007) Can molecular dynamics simulations provide high-resolution refinement of protein structure? Proteins 67(4):922–930CrossRefPubMedGoogle Scholar
  15. Cheng J, Baldi P (2006) A machine learning information retrieval approach to protein fold recognition. Bioinformatics 22(12):1456–1463CrossRefPubMedGoogle Scholar
  16. Das R, Qian B, Raman S, Vernon R, Thompson J, Bradley P et al (200) Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home. Proteins 69(S8):118–128CrossRefGoogle Scholar
  17. Dominy BN, Brooks CL (2002) Identifying native-like protein structures using physics-based potentials. J Comput Chem 23(1):147–160Google Scholar
  18. Duan Y, Kollman PA (1998) Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution. Science 282(5389):740–744CrossRefPubMedGoogle Scholar
  19. Fan H, Mark AE (2004) Refinement of homology-based protein structures by molecular dynamics simulation techniques. Protein Sci 13(1):211–220Google Scholar
  20. Feig M, Brooks CL, 3rd (2002) Evaluating CASP4 predictions with physical energy functions. Proteins 49(2):232–245Google Scholar
  21. Felts AK, Gallicchio E, Wallqvist A, Levy RM (2002) Distinguishing native conformations of proteins from decoys with an effective free energy estimator based on the OPLS all-atom force field and the Surface Generalized Born solvent model. Proteins 48(2):404–422Google Scholar
  22. Fischer D (2003) 3D-SHOTGUN: a novel, cooperative, fold-recognition meta-predictor. Proteins 51(3):434–441CrossRefPubMedGoogle Scholar
  23. Fischer D (2006) Servers for protein structure prediction. Curr Opin Struct Biol 16(2):178–182CrossRefPubMedGoogle Scholar
  24. Fischer D, Rychlewski L, Dunbrack RL Jr, Ortiz AR, Elofsson A (2003) CAFASP3: the third critical assessment of fully automated structure prediction methods. Proteins 53(Suppl 6):503–516CrossRefPubMedGoogle Scholar
  25. Ginalski K, Pas J, Wyrwicz LS, von Grotthuss M, Bujnicki JM, Rychlewski L (2003) ORFeus: Detection of distant homology using sequence profiles and predicted secondary structure. Nucleic Acids Res 31(13):3804–3807CrossRefPubMedGoogle Scholar
  26. Helles G (2008) A comparative study of the reported performance of ab initio protein structure prediction algorithms. J R Soc Interface 5(21):387–396CrossRefPubMedGoogle Scholar
  27. Hsieh MJ, Luo R (2004) Physical scoring function based on AMBER force field and Poisson-Boltzmann implicit solvent for protein structure prediction. Proteins 56(3):475–486Google Scholar
  28. Im W, Lee MS, Brooks CL III (2003) Generalized born model with a simple smoothing function. J Comput Chem 24(14):1691–1702CrossRefPubMedGoogle Scholar
  29. Jaroszewski L, Rychlewski L, Li Z, Li W, Godzik A (2005) FFAS03: a server for profile–profile sequence alignments. Nucleic Acids Res 33(Web Server issue):W284–W288CrossRefPubMedGoogle Scholar
  30. Jauch R, Yeo HC, Kolatkar PR, Clarke ND (2007) Assessment of CASP7 structure predictions for template free targets. Proteins 69(Suppl 8):57–67CrossRefPubMedGoogle Scholar
  31. Jones DT (1999) GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences. J Mol Biol 287(4):797–815CrossRefPubMedGoogle Scholar
  32. Jones DT, Taylor WR, Thornton JM (1992) A new approach to protein fold recognition. Nature 358(6381):86–89CrossRefPubMedGoogle Scholar
  33. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935CrossRefGoogle Scholar
  34. Jorgensen WL, Tirado-Rives J (1988) The OPLS potential functions for proteins. Energy minimizations for crystals of cyclic peptides and crambin. J Am Chem Soc 110:1657–1666CrossRefGoogle Scholar
  35. Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 105:6474–6487CrossRefGoogle Scholar
  36. Karplus K, Barrett C, Hughey R (1998) Hidden Markov models for detecting remote protein homologies. Bioinformatics 14:846–856CrossRefPubMedGoogle Scholar
  37. Kihara D, Lu H, Kolinski A, Skolnick J (2001) TOUCHSTONE: An ab initio protein structure prediction method that uses threading-based tertiary restraints. Proc Natl Acad Sci U S A 98:10125–10130CrossRefPubMedGoogle Scholar
  38. Klepeis JL, Floudas CA (2003) ASTRO-FOLD: a combinatorial and global optimization framework for Ab initio prediction of three-dimensional structures of proteins from the amino acid sequence. Biophys J 85(4):2119–2146CrossRefPubMedGoogle Scholar
  39. Klepeis JL, Wei Y, Hecht MH, Floudas CA (2005) Ab initio prediction of the three-dimensional structure of a de novo designed protein: a double-blind case study. Proteins 58(3):560–570CrossRefPubMedGoogle Scholar
  40. Kopp J, Bordoli L, Battey JN, Kiefer F, Schwede T (2007) Assessment of CASP7 predictions for template-based modeling targets. Proteins 6(S8):38–56CrossRefGoogle Scholar
  41. Kulikova T, Aldebert P, Althorpe N, Baker W, Bates K, Browne P et al (2004) The EMBL nucleotide sequence database. Nucleic Acids Res 32(Database issue):D27–D30CrossRefPubMedGoogle Scholar
  42. Lazaridis T, Karplus M (1999) Effective energy function for proteins in solution. Proteins 35(2):133–152CrossRefPubMedGoogle Scholar
  43. Lee MR, Tsai J, Baker D, Kollman PA (2001) Molecular dynamics in the endgame of protein structure prediction. J Mol Biol 313(2):417–430CrossRefPubMedGoogle Scholar
  44. Lee MC, Duan Y (2004) Distinguish protein decoys by using a scoring function based on a new AMBER force field, short molecular dynamics simulations, and the generalized born solvent model. Proteins 55(3):620–634Google Scholar
  45. Levitt M, Hirshberg M, Sharon R, Daggett V (1995) Potential-energy function and parameters for simulations of the molecular-dynamics of proteins and nucleic-acids in solution. Comput Phys Commun 91(1–3):215–231CrossRefGoogle Scholar
  46. Lindahl E, Hess B, van der Spoel D (2001) GROMACS 3.0: A package for molecular simulation and trajectory analysis. J Mol Modeling 7:306–317Google Scholar
  47. Liwo A, Lee J, Ripoll DR, Pillardy J, Scheraga HA (1999) Protein structure prediction by global optimization of a potential energy function. Proc Natl Acad Sci U S A 96(10):5482–5485CrossRefPubMedGoogle Scholar
  48. Liwo A, Pincus MR, Wawak RJ, Rackovsky S, Scheraga HA (1993) Calculation of protein backbone geometry from alpha-carbon coordinates based on peptide-group dipole alignment. Protein Sci 2(10):1697–1714CrossRefPubMedGoogle Scholar
  49. MacKerell AD Jr, Bashford D, Bellott M, Dunbrack RL, Evanseck JD, Field MJ et al (1998) All-atom empirical potential for molecular Modeling and dynamics studies of proteins. J Phys Chem B 102(18):3586–3616CrossRefGoogle Scholar
  50. Marti-Renom MA, Stuart AC, Fiser A, Sanchez R, Melo F, Sali A (2000) Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biomol Struct 29:291–325CrossRefPubMedGoogle Scholar
  51. McPherson JD, Marra M, Hillier L, Waterston RH, Chinwalla A, Wallis J et al (2001) A physical map of the human genome. Nature 409(6822):934–941CrossRefPubMedGoogle Scholar
  52. Misura KM, Chivian D, Rohl CA, Kim DE, Baker D (2006) Physically realistic homology models built with ROSETTA can be more accurate than their templates. Proc Natl Acad Sci U S A 103(14):5361–5366CrossRefPubMedGoogle Scholar
  53. Miyazaki S, Sugawara H, Gojobori T, Tateno Y (2003) DNA Data Bank of Japan (DDBJ) in XML. Nucleic Acids Res 31(1):13–16CrossRefPubMedGoogle Scholar
  54. Moult J, Fidelis K, Kryshtafovych A, Rost B, Hubbard T, Tramontano A (2007) Critical assessment of methods of protein structure prediction-Round VII. Proteins 69(Suppl 8):3–9CrossRefPubMedGoogle Scholar
  55. Moult J, Fidelis K, Zemla A, Hubbard T (2001) Critical assessment of methods of protein structure prediction (CASP): round IV. Proteins Suppl 5:2–7CrossRefPubMedGoogle Scholar
  56. Nemethy G, Gibson KD, Palmer KA, Yoon CN, Paterlini G, Zagari A et al (1992) Energy Parameters in Polypeptides. 10. Improved geometric parameters and nonbonded interactions for use in the ECEPP/3 algorithm, with application to proline-containing peptides. J Phys Chem B 96:6472–6484CrossRefGoogle Scholar
  57. Neria E, Fischer S, Karplus M (1996) Simulation of activation free energies in molecular systems. J Chem Phys 105(5):1902–1921CrossRefGoogle Scholar
  58. Nilges M, Brunger AT (1991) Automated modeling of coiled coils: application to the GCN4 dimerization region. Protein Eng 4(6):649–659CrossRefPubMedGoogle Scholar
  59. Park B, Levitt M (1996) Energy functions that discriminate X-ray and near native folds from well-constructed decoys. J Mol Biol 258(2):367–392CrossRefPubMedGoogle Scholar
  60. Pieper U, Eswar N, Braberg H, Madhusudhan MS, Davis FP, Stuart AC et al (2004) MODBASE, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids Res 32(Database issue):D217–D222CrossRefPubMedGoogle Scholar
  61. Pieper U, Eswar N, Davis FP, Braberg H, Madhusudhan MS, Rossi A et al (2006) MODBASE: a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res 34(Database issue):D291–D295CrossRefPubMedGoogle Scholar
  62. Rychlewski L, Fischer D (2005) LiveBench-8: the large-scale, continuous assessment of automated protein structure prediction. Protein Sci 14(1):240–245CrossRefPubMedGoogle Scholar
  63. Sadreyev R, Grishin N (2003) COMPASS: a tool for comparison of multiple protein alignments with assessment of statistical significance. J Mol Biol 326(1):317–336CrossRefPubMedGoogle Scholar
  64. Sali A (1998) 100, 000 protein structures for the biologist. Nat Struct Biol 5(12):1029–1032CrossRefPubMedGoogle Scholar
  65. Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234(3):779–815CrossRefPubMedGoogle Scholar
  66. Shi J, Blundell TL, Mizuguchi K (2001) FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. J Mol Biol 310(1):243–257CrossRefPubMedGoogle Scholar
  67. Simons KT, Kooperberg C, Huang E, Baker D (1997) Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 268(1):209–225CrossRefPubMedGoogle Scholar
  68. Skolnick J, Fetrow JS, Kolinski A (2000) Structural genomics and its importance for gene function analysis. Nat Biotechnol 18(3):283–287CrossRefPubMedGoogle Scholar
  69. Skolnick J, Kihara D, Zhang Y (2004) Development and large scale benchmark testing of the PROSPECTOR 3.0 threading algorithm. Protein 56:502–518CrossRefGoogle Scholar
  70. Smaglik P (2000) Protein structure groups seek to draft common ground rules. Nature 403(6771):691CrossRefPubMedGoogle Scholar
  71. Soding J (2005) Protein homology detection by HMM-HMM comparison. Bioinformatics 21(7):951–960CrossRefPubMedGoogle Scholar
  72. Sorin EJ, Pande VS (2005) Exploring the helix-coil transition via all-atom equilibrium ensemble simulations. Biophys J 88(4):2472–2493CrossRefPubMedGoogle Scholar
  73. Stevens RC, Yokoyama S, Wilson IA (2001) Global efforts in structural genomics. Science 294(5540):89–92CrossRefPubMedGoogle Scholar
  74. Summa CM, Levitt M (2007) Near-native structure refinement using in vacuo energy minimization. Proc Natl Acad Sci U S A 104(9):3177–3182CrossRefPubMedGoogle Scholar
  75. Terwilliger TC, Waldo G, Peat TS, Newman JM, Chu K, Berendzen J (1998) Class-directed structure determination: foundation for a protein structure initiative. Protein Sci 7(9):1851–1856CrossRefPubMedGoogle Scholar
  76. Tsai J, Bonneau R, Morozov AV, Kuhlman B, Rohl CA, Baker D (2003) An improved protein decoy set for testing energy functions for protein structure prediction. Proteins 53(1):76–87CrossRefPubMedGoogle Scholar
  77. van Gunsteren WF, Billeter SR, Eising AA, Hunenberger PH, Kruger P, Mark AE et al (1996) Biomolecular Simulation: The GROMOS96 Manual and User Guide. Vdf Hochschulverlag AG an der ETH Zürich, ZürichGoogle Scholar
  78. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG et al (2001) The sequence of the human genome. Science 291(5507):1304–1351CrossRefPubMedGoogle Scholar
  79. Vieth M, Kolinski A, Brooks CL III, Skolnick J (1994) Prediction of the folding pathways and structure of the GCN4 leucine zipper. J Mol Biol 237(4):361–367CrossRefPubMedGoogle Scholar
  80. Vitkup D, Melamud E, Moult J, Sander C (2001) Completeness in structural genomics. Nat Struct Biol 8(6):559–566Google Scholar
  81. Wallner B, Elofsson A (2007) Prediction of global and local model quality in CASP7 using Pcons and ProQ. Proteins 69(S8):184–193CrossRefPubMedGoogle Scholar
  82. Wang JM, Cieplak P, Kollman PA (2000) How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? J Comput Chem 21(12):1049–1074CrossRefGoogle Scholar
  83. Weiner SJ, Kollman PA, Case DA, Singh UC, Ghio C, Alagona G et al (1984) A new force field for molecular mechanical simulation of nucleic acids and proteins. J Am Chem Soc 106:765–784CrossRefGoogle Scholar
  84. Wroblewska L, Skolnick J (2007) Can a physics-based, all-atom potential find a protein’s native structure among misfolded structures? I. Large scale AMBER benchmarking. J Comput Chem 28(12):2059–2066CrossRefPubMedGoogle Scholar
  85. Wu S, Skolnick J, Zhang Y (2007) Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biol 5:17CrossRefPubMedGoogle Scholar
  86. Wu S, Zhang Y (2007) LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acids Res 35(10):3375–3382CrossRefPubMedGoogle Scholar
  87. Wu S, Zhang Y (2008) MUSTER: Improving protein sequence profile-profile alignments by using multiple sources of structure information. Proteins 72(2):547–556CrossRefPubMedGoogle Scholar
  88. Zagrovic B, Snow CD, Shirts MR, Pande VS (2002) Simulation of folding of a small alpha-helical protein in atomistic detail using worldwide-distributed computing. J Mol Biol 323(5):927–937CrossRefPubMedGoogle Scholar
  89. Zhang Y (2007) Template-based modeling and free modeling by I-TASSER in CASP7. Proteins 69(Suppl 8):108–117CrossRefPubMedGoogle Scholar
  90. Zhang Y, Kolinski A, Skolnick J (2003) TOUCHSTONE II: A new approach to ab initio protein structure prediction. Biophys J 85:1145–1164CrossRefPubMedGoogle Scholar
  91. Zhang Y, Skolnick J (2004a) Automated structure prediction of weakly homologous proteins on a genomic scale. Proc Natl Acad Sci U S A 101:7594–7599CrossRefPubMedGoogle Scholar
  92. Zhang Y, Skolnick J (2004b) Scoring function for automated assessment of protein structure template quality. Proteins 57(4):702–710CrossRefPubMedGoogle Scholar
  93. Zhang Y, Skolnick J (2005a) The protein structure prediction problem could be solved using the current PDB library. Proc Natl Acad Sci U S A 102:1029–1034CrossRefPubMedGoogle Scholar
  94. Zhang Y, Skolnick J (2005b) TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res 33(7):2302–2309CrossRefPubMedGoogle Scholar
  95. Zhou H, Zhou Y (2005) Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments. Proteins 58(2):321–328CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Center for Bioinformatics and Department of Molecular BioscienceUniversity of KansasLawrenceUSA

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