Exploring the extremes of sequence/structure space with ensemble fold recognition in the program Phyre

Proteins: Structure, Function and Genetics

Volumn 70, Issue 3, 2008, Pages 611-625

  Bennett Lovsey, Riccardo M ^a   Herbert, Alex D ^a   Sternberg, Michael J E ^a   Kelley, Lawrence A ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Ensemble; Fold recognition; Meta server; Phyre; Profile profile alignment; Protein structure prediction; Remote homology modelling

Indexed keywords

ACCURACY; ALGORITHM; AMINO ACID SEQUENCE; ENERGY TRANSFER; GENE SEQUENCE; INTERNET; LABORATORY DIAGNOSIS; MOLECULAR RECOGNITION; PREDICTION; PRIORITY JOURNAL; PROTEIN FOLDING; PROTEIN STRUCTURE; QUALITY CONTROL; REVIEW; SEQUENCE HOMOLOGY; STRUCTURAL GENOMICS; TECHNIQUE; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ANIMALS; DATABASES, PROTEIN; HUMANS; PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEINS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE;

EID: 38549141229     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.21688     Document Type: Review

References (47)

1. Sippl MJ. 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 1990;213:859-883.
2. Jones DT, Taylor WR, Thornton JM. A new approach to protein fold recognition. Nature 1992;358:86-89.
3. Bryant SH, Lawrence CE. An empirical energy function for threading protein sequence through the folding motif. Proteins 1993;16:92-112.
4. Bowie JU, Luthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science 1991;253:164-170.
5. Godzik A, Skolnick J. Sequence-structure matching in globular proteins: application to supersecondary and tertiary structure determination. Proc Natl Acad Sci USA 1992;89:12098-12102.
6. Fischer D, Eisenberg D. Protein fold recognition using sequence-derived predictions. Protein Sci 1996;5:947-955.
7. Kelley LA, MacCallum RM, Sternberg MJ. Enhanced genome annotation using structural profiles in the program 3D-PSSM. J Mol Biol 2000;299:499-520.
8. Tang CL, Xie L, Koh IY, Posy S, Alexov E, Honig B. On the role of structural information in remote homology detection and sequence alignment: new methods using hybrid sequence profiles. J Mol Biol 2003;334:1043-1062.
9. Karplus K, Barrett C, Hughey R. Hidden Markov models for detecting remote protein homologies. Bioinformatics 1998;14:846-856.
10. Eddy SR. Profile hidden Markov models. Bioinformatics 1998;14: 755-763.
11. von Ohsen N, Sommer I, Zimmer R. Profile-profile alignment: a powerful tool for protein structure prediction. Pac Symp Biocomput 2003:252-263.
12. Ohlson T, Wallner B, Elofsson A. Profile-profile methods provide improved fold-recognition: a study of different profile-profile alignment methods. Proteins 2004;57:188-197.
13. Panchenko AR. Finding weak similarities between proteins by sequence profile comparison. Nucleic Acids Res 2003;31:683-689.
14. Jaroszewski L, Rychlewski L, Li Z, Li W, Godzik A. FFAS03: a server for profile-profile sequence alignments. Nucleic Acids Res 2005;33:W284-W288.
15. Lundstrom J, Rychlewski L, Bujnicki J, Elofsson A. Pcons: a neural-network-based consensus predictor that improves fold recognition. Protein Sci 2001;10:2354-2362.
16. Ginalski K, Elofsson A, Fischer D, Rychlewski L. 3D-Jury: a simple approach to improve protein structure predictions. Bioinformatics 2003;19:1015-1018.
17. Fischer D. 3D-SHOTGUN: a novel, cooperative, fold-recognition meta-predictor. Proteins 2003;51:434-441.
18. Fischer D. Hybrid fold recognition: combining sequence derived properties with evolutionary information. Pac Symp Biocomput 2000:119-130.
19. Rychlewski L, Zhang B, Godzik A. Fold and function predictions for Mycoplasma genitalium proteins. Fold Des 1998;3:229-238.
20. Ginalski K, von Grotthuss M, Grishin NV, Rychlewski L. Detecting distant homology with Meta-BASIC. Nucleic Acids Res 2004;32:W576-W581.
21. Ginalski K, Pas J, Wyrwicz LS, von Grotthuss M, Bujnicki JM, Rychlewski L. ORFeus: Detection of distant homology using sequence profiles and predicted secondary structure. Nucleic Acids Res 2003;31:3804-3807.
22. Moult J. A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. Curr Opin Struct Biol 2005;15:285-289.
23. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The protein data bank. Nucleic Acids Res 2000;28:235-242.
24. Yan Y, Moult J. Protein family clustering for structural genomics. J Mol Biol 2005;353:744-759.
25. Jain AK, Duin RPW, Mao JC. Statistical pattern recognition: A review. IEEE Trans Pattern Anal 2000;22:4-37.
26. Kuncheva LI, Whitaker CJ. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 2003;51:181-207.
27. Zhou ZH, Jiang Y, Yang YB, Chen SF. Lung cancer cell identification based on artificial neural network ensembles. Artif Intell Med 2002;24:25-36.
28. Murzin AG, Brenner SE, Hubbard T, Chothia C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 1995;247:536-540.
29. Xiang Z, Soto CS, Honig B. Evaluating conformational free energies: the colony energy and its application to the problem of loop prediction. Proc Natl Acad Sci USA 2002;99:7432-7437.
30. Vapnik V, Chapelle O. Bounds on error expectation for support vector machines. Neural Comput 2000;12:2013-2036.
31. Brenner SE, Koehl P, Levitt M. The ASTRAL compendium for protein structure and sequence analysis. Nucleic Acids Res 2000;28:254-256.
32. Yona G, Levitt M. Within the twilight zone: a sensitive profile-profile comparison tool based on information theory. J Mol Biol 2002;315:1257-1275.
33. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997;25:3389-3402.
34. McGuffin LJ, Bryson K, Jones DT. The PSIPRED protein structure prediction server. Bioinformatics 2000;16:404-405.
35. Przybylski D, Rost B. Improving fold recognition without folds. J Mol Biol 2004;341:255-269.
36. Siew N, Elofsson A, Rychlewski L, Fischer D. MaxSub: an automated measure for the assessment of protein structure prediction quality. Bioinformatics 2000;16:776-785.
37. Zhang Y, Skolnick J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res 2005;33:2302-2309.
38. Zhang Y, Skolnick J. Scoring function for automated assessment of protein structure template quality. Proteins 2004;57:702-710.
39. Joachims T. Learning to classify text using support vector machines. Boston: Kluwer Academic Publishers; 2002. xvi, 205 pp.
40. Ortiz AR, Strauss CE, Olmea O. MAMMOTH (matching molecular models obtained from theory): an automated method for model comparison. Protein Sci 2002;11:2606-2621.
41. Nelder JA, Mead R. A simplex method for function minimization. Comput J 1965;7:308-313.
42. Lagarias JC, Reeds JA, Wright MH, Wright PE. Convergence properties of the Nelder-Mead Simplex method in low dimensions. SIAM J Optim 1998;9:112-147.
43. Goonesekere NC, Lee B. Frequency of gaps observed in a structurally aligned protein pair database suggests a simple gap penalty function. Nucleic Acids Res 2004;32:2838-2843.
44. Breiman L. Arcing classifiers. Ann Stat 1998;26:801-824.
45. Breiman L. Random forests. Mach Learn 2001;45:5-32.
46. McNemar Q. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 1947;12:153-157.
47. Yates F. Contingency tables involving small numbers and the chi-squared test. J R Stat Soc 1934;Suppl 1:217-235.