A composite score for predicting errors in protein structure models

Protein Science

Volumn 15, Issue 7, 2006, Pages 1653-1666

  Eramian, David ^a,b   Shen, Min Yi ^b   Devos, Damien ^b   Melo, Francisco ^c   Sali, Andrej ^b   Marti Renom, Marc A ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c PONTIFICIA UNIVERSIDAD CATÓLICA DE CHILE   (Chile)

Author keywords

Comparative modeling; Fold assignment; Model assessment; Protein structure prediction; Statistical potentials; Support vector machine

Indexed keywords

AMINO ACID SEQUENCE; ANALYTICAL ERROR; ARTICLE; CORRELATION COEFFICIENT; MATHEMATICAL MODEL; PRIORITY JOURNAL; PROTEIN STRUCTURE; SCORING SYSTEM;

MODELS, MOLECULAR; MODELS, THEORETICAL; PROTEINS;

EID: 33745726716     PISSN: 09618368     EISSN: 1469896X     Source Type: Journal    
DOI: 10.1110/ps.062095806     Document Type: Article

References (69)

1. Adamczak, R., Porollo, A., and Meller, J. 2004. Accurate prediction of solvent accessibility using neural networks-based regression. Proteins 56: 753-767.
2. Andreeva, A., Howorth, D., Brenner, S.E., Hubbard, T.J., Chothia, C., and Murzin, A.G. 2004. SCOP database in 2004: Refinements integrate structure and sequence family data. Nucleic Acids Res. 32: D226-D229.
3. Apweiler, R., Bairoch, A., Wu, C.H., Barker, W.C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., Lopez, R., Magrane, M., et al. 2004. UniProt: The Universal Protein knowledge base. Nucleic Acids Res. 32: D115-D119.
4. Bairoch, A., Apweiler, R., Wu, C.H., Barker, W.C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., Lopez, R., Magrane, M., et al. 2005. The Universal Protein Resource (UniProt). Nucleic Acids Res. 33: D154-D159.
5. Baker, D. and Sali, A. 2001. Protein structure prediction and structural genomics. Science 294: 93-96.
6. Bock, J.R. and Gough, D.A. 2001. Predicting protein-protein interactions from primary structure. Bioinformatics 17: 455-460.
7. Bradley, P., Misura, K.M., and Baker, D. 2005. Toward high-resolution de novo structure prediction for small proteins. Science 309: 1868-1871.
8. Brooks, R.B., Bruccoleri, R.E., Olafson, B.D., States, D.J., Swaminathan, S., and Karplus, M. 1983. CHARMM: A program for macromolecular energy minimization and dynamics calculations. J. Comput. Chem. 4: 187-217.
9. Burbidge, R., Trotter, M., Buxton, B., and Holden, S. 2001. Drug design by machine learning: Support vector machines for pharmaceutical data analysis. Comput. Chem. 26: 5-14.
10. Cai, C.Z., Wang, W.L., Sun, L.Z., and Chen, Y.Z. 2003a. Protein function classification via support vector machine approach. Math. Biosci. 185: 111-122.
11. Cai, Y.D., Liu, X.J., Li, Y.X., Xu, X.B., and Chou, K.C. 2003b. Prediction of β-turns with learning machines. Peptides 24: 665-669.
12. Cristobal, S., Zemla, A., Fischer, D., Rychlewski, L., and Elofsson, A. 2001. A study of quality measures for protein threading models. BMC Bioinformatics 2: 5.
13. Ding, C.H. and Dubchak, I. 2001. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17: 349-358.
14. Domingues, F.S., Koppensteiner, W.A., Jaritz, M., Prlic, A., Weichenberger, C., Wiederstein, M., Floeckner, H., Lackner, P., and Sippl, M.J. 1999. Sustained performance of knowledge-based potentials in fold recognition. Proteins 3 (Suppl. 3): 112-120.
15. Doniger, S., Hofmann, T., and Yeh, J. 2002. Predicting CNS permeability of drug molecules: Comparison of neural network and support vector machine algorithms. J. Comput. Biol. 9: 849-864.
16. Eswar, N., John, B., Mirkovic, N., Fiser, A., Ilyin, V.A., Pieper, U., Stuart, A.C., Marti-Renom, M.A., Madhusudhan, M.S., Yerkovich, B., et al. 2003. Tools for comparative protein structure modeling and analysis. Nucleic Acids Res. 31: 3375-3380.
17. Eyrich, V.A., Marti-Renom, M.A., Przybylski, D., Madhusudhan, M.S., Fiser, A., Pazos, F., Valencia, A., Sali, A., and Rost, B. 2001. EVA: Continuous automatic evaluation of protein structure prediction servers. Bioinformatics 17: 1242-1243.
18. Felsenstein, J. 1985. Confidence limits on phylogenies: An approach using the bootstrap. Evolution Int. J. Org. Evolution 39: 783-791.
19. Fischer, D., Elofsson, A., Rice, D., and Eisenberg, D. 1996. Assessing the performance of fold recognition methods by means of a comprehensive benchmark. Pac. Symp. Biocomput. 397: 300-318.
20. Gatchell, D.W., Dennis, S., and Vajda, S. 2000. Discrimination of near-native protein structures from misfolded models by empirical free energy functions. Proteins 41: 518-534.
21. Ginalski, K., Grishin, N.V., Godzik, A., and Rychlewski, L. 2005. Practical lessons from protein structure prediction. Nucleic Acids Res. 33: 1874-1891.
22. Holm, L. and Sander, C. 1992. Evaluation of protein models by atomic solvation preference. J. Mol. Biol. 225: 93-105.
23. Joachims, T. 1988. Making large-scale SVM learning practical." LS VIII-Report, No. 24. In . Computer Science Department, Universität Dortmund, Dortmund, Germany.
24. John, B. and Sali, A. 2003. Comparative protein structure modeling by iterative alignment, model building and model assessment. Nucleic Acids Res. 31: 3982-3992.
25. Jones, D.T. 1999a. GenTHREADER: An efficient and reliable protein fold recognition method for genomic sequences. J. Mol. Biol. 287: 797-815.
26. _. 1999b. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292: 195-202.
27. Kabsch, W. and Sander, C. 1983. Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22: 2577-2637.
28. Karchin, R., Kelly, L., and Sali, A. 2005. Improving functional annotation of non-synonomous SNPs with information theory. Pac. Symp. Biocomput. 2005: 397-408.
29. Kuhlman, B., Dantas, G., Ireton, G.C., Varani, G., Stoddard, B.L., and Baker, D. 2003. Design of a novel globular protein fold with atomic-level accuracy. Science 302: 1364-1368.
30. Lazaridis, T. and Karplus, M. 1997. New view of protein folding reconciled with the old through multiple unfolding simulations. Science 278: 1928-1931.
31. _. 1999a. Discrimination of the native from misfolded protein models with an energy function including implicit solvation. J. Mol. Biol. 288: 477-487.
32. _. 1999b. Effective energy function for proteins in solution. Proteins 35: 133-152.
33. _. 2000. Effective energy functions for protein structure prediction. Curr. Opin. Struct. Biol. 10: 139-145.
34. Marti-Renom, M.A., Madhusudhan, M.S., Fiser, A., Rost, B., and Sali, A. 2002. Reliability of assessment of protein structure prediction methods. Structure (Camb.) 10: 435-440.
35. Marti-Renom, M.A., Fiser, A., Madhusudhan, M.S., John, B., Stuart, A.C., Eswar, N., Pieper, U., Shen, M.-Y., and Sali, A. 2003. Modeling protein structure from its sequence. In Current protocols in bioinformatics (eds. A.D. Baxevanis et al.), pp. 5.1.1-5.1.32. John Wiley & Sons, New York.
36. McGuffin, L.J. and Jones, D.T. 2003. Benchmarking secondary structure prediction for fold recognition. Proteins 52: 166-175.
37. Melo, F. and Feytmans, E. 1997. Novel knowledge-based mean force potential at atomic level. J. Mol. Biol. 267: 207-222.
38. Melo, F. and Feytmans, E. 1998. Assessing protein structures with a non-local atomic interaction energy. J. Mol. Biol. 277: 1141-1152.
39. Melo, F., Devos, D., Depiereux, E., and Feytmans, E. 1997. ANOLEA: A www server to assess protein structures. Proc. Int. Conf. Intell. Syst. Mol. Biol. 5: 187-190.
40. Melo, F., Sanchez, R., and Sali, A. 2002. Statistical potentials for fold assessment. Protein Sci. 11: 430-448.
41. Miyazawa, S. and Jernigan, R.L. 1996. Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J. Mol. Biol. 256: 623-644.
42. Moult, J., Fidelis, K., Zemla, A., and Hubbard, T. 2003. Critical assessment of methods of protein structure prediction (CASP)-round V. Proteins 53 (Suppl. 6): 334-339.
43. Park, B. and Levitt, M. 1996. Energy functions that discriminate X-ray and near-native folds from well-constructed decoys. J. Mol. Biol. 258: 367-392.
44. Park, B.H., Huang, E.S., and Levitt, M. 1997. Factors affecting the ability of energy functions to discriminate correct from incorrect folds. J. Mol. Biol. 266: 831-846.
45. Pazos, F., Helmer-Citterich, M., Ausiello, G., and Valencia, A. 1997. Correlated mutations contain information about protein-protein interaction. J. Mol. Biol. 271: 511-523.
46. Pearl, F., Todd, A., Sillitoe, I., Dibley, M., Redfern, O., Lewis, T., Bennett, C., Marsden, R., Grant, A., Lee, D., et al. 2005. The CATH Domain Structure Database and related resources Gene3D and DHS provide comprehensive domain family information for genome analysis. Nucleic Acids Res. 33: D247-D251.
47. Pieper, U., Eswar, N., Braberg, H., Madhusudhan, M.S., Davis, F.P., Stuart, A.C., Mirkovic, N., Rossi, A., Marti-Renom, M.A., Fiser, A., et al. 2004. MODBASE, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids Res. 32: D217-D222.
48. Qiu, D., Shenkin, P.S., Hollinger, F.P., and Still, W.C. 1997. The GB/SA continuum model for solvation. A fast analytical method for the calculation of approximate Born radii. J. Phys. Chem. A 101: 3005-3014.
49. Rychlewski, L. and Fischer, D. 2005. LiveBench-8: The large-scale, continuous assessment of automated protein structure prediction. Protein Sci. 14: 240-245.
50. Sali, A. and Blundell, T.L. 1993. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234: 779-815.
51. Samudrala, R. and Levitt, M. 2000. Decoys 'R' Us: A database of incorrect conformations to improve protein structure prediction. Protein Sci. 9: 1399-1401.
52. Seok, C., Rosen, J.B., Chodera, J.D., and Dill, K.A. 2003. MOPED: Method for optimizing physical energy parameters using decoys. J. Comput. Chem. 24: 89-97.
53. Shindyalov, I.N. and Bourne, P.E. 1998. Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Eng. 11: 739-747.
54. Shortle, D., Simons, K.T., and Baker, D. 1998. Clustering of low-energy conformations near the native structures of small proteins. Proc. Natl. Acad. Sci. 95: 11158-11162.
55. Siew, N., Elofsson, A., Rychlewski, L., and Fischer, D. 2000. MaxSub: An automated measure for the assessment of protein structure prediction quality. Bioinformatics 16: 776-785.
56. Sippl, M.J. 1993a. Boltzmann's principle, knowledge-based mean fields and protein folding. An approach to the computational determination of protein structures. J. Comput. Aided Mol. Des. 7: 473-501.
57. _. 1993b. Recognition of errors in three-dimensional structures of proteins. Proteins 17: 355-362.
58. _. 1995. Knowledge-based potentials for proteins. Curr. Opin. Struct. Biol. 5: 229-235.
59. Still, W.C., Tempczyk, A., Hawley, R.C., and Hendrickson, T. 1990. Semi-analytical treatment of solvation for molecular mechanics and dynamics. J. Am. Chem. Soc. 112: 6127-6129.
60. Topf, M., Baker, M.L., John, B., Chiu, W., and Sali, A. 2005. Structural characterization of components of protein assemblies by comparative modeling and electron cryo-microscopy. J. Struct. Biol. 149: 191-203.
61. Tosatto, S.C.E. 2005. The victor/FRST function for model quality estimation. J. Comput. Biol. 12: 1316-1327.
62. Tsai, J., Bonneau, R., Morozov, A.V., Kuhlman, B., Rohl, C.A., and Baker, D. 2003. An improved protein decoy set for testing energy functions for protein structure prediction. Proteins 53: 76-87.
63. Vapnik, V. 1995. The nature of statistical learning theory. Springer, Berlin, Germany.
64. Vorobjev, Y.N. and Hermans, J. 2001. Free energies of protein decoys provide insight into determinants of protein stability. Protein Sci. 10: 2498-2506.
65. Wallner, B. and Elofsson, A. 2003. Can correct protein models be identified? Protein Sci. 12: 1073-1086.
66. Ward, J.J., McGuffin, L.J., Buxton, B.F., and Jones, D.T. 2003. Secondary structure prediction with support vector machines. Bioinformatics 19: 1650-1655.
67. Zhang, C., Liu, S., Zhou, H., and Zhou, Y. 2004. The dependence of all-atom statistical potentials on structural training database. Biophys. J. 86: 3349-3358.
68. Zhou, H. and Zhou, Y. 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.
69. Zhu, J., Zhu, Q., Shi, Y., and Liu, H. 2003. How well can we predict native contacts in proteins based on decoy structures and their energies? Proteins 52: 598-608.