Hidden Markov models for prediction of protein features

Methods in Molecular Biology

Volumn 413, Issue , 2007, Pages 173-198

  Bystroff, Christopher ^a   Krogh, Anders ^a  

^a NONE

Author keywords

Baum Welch; Folding; Local; Motif; Profile; Topology; Transmembrane; Viterbi

Indexed keywords

EID: 37149024415     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1385/1-59745-574-1:173     Document Type: Article

References (52)

1. Eddy, S. Profile hidden Markov models. Bioinformatics, 14:755-763.
2. Madera, M. et al. (2004). The SUPERFAMILY database in 2004: additions and improvements. Nucleic Acids Res, 32(90001):235-239.
3. Rabiner, L. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-286.
4. Krogh, A., Larsson, B., von Heijne, G., and Sonnhammer, E. (2001). Predicting transmembrane protein topology with a, hidden Markov model: application to complete genomes. J Mol Biol, 305(3):567-580.
5. Needleman, S. and Wunsch, C. (1970). A general, method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol, 48(3):443-453.
6. Durbin, R., Eddy, S., Krogh, A., and Mitchison, G. (1998). Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press.
7. Kyte, J. and Doolittle, R. (1982). A simple method for displaying the hydropathic character of a protein. J Mol Biol, 157(1): 105-132.
8. Argos, P., Rao, J., and Hargrave, P. (1982). Structural prediction of membranebound proteins. Eur J Biochem, 128:565-575.
9. von Heijne, G. (1990). The signal peptide. J Membr Biol, 115(3):195-201.
10. von Heijne, G. (1992). Membrane protein structure prediction. Hydrophobicity analysis and the positive-inside rule. J Mol Biol, 225(2):487-494.
11. Jones, D., Taylor, W., and Thornton, J. (1994). A model recognition approach to the prediction of all-helical membrane protein structure and topology. Biochemistry, 33(10):3038-3049.
12. Rost, B., Casadlo, R., and Fariselli, P. (1996). Refining neural network predictions for helical transmembrane proteins by dynamic programming. Proc Int Conf Intell Syst Mol Biol, 4:192-200.
13. Yuan, Z., Mattick, J., and Teasdale, R. (2004). SVMtm: support vector machines to predict transmembrane segments. J Comput Chem, 25(5):632-636.
14. Sonnhammer, E., von Heijne, G., Krogh, A., et al. (1998). A hidden Markov model for predicting transmembrane helices in protein sequences. Proc Int. Conf Intell Syst Mol Biol, 6:175-182.
15. Tusnady, G. and Simon, I. (1998). Principles governing amino acid composition of integral membrane proteins: application to topology prediction. J Mol Biol, 283(2):489-506.
16. Chow, Y. and Schwartz, R. (1989). The N-Best algorithm: an efficient procedure for finding top N sentence hypotheses. Proceedings of the DARPA Speech and Natural Language Workshop, 199-202.
17. Kahsay, R., Gao, G., Liao, L., and Journals, O. (2005). An improved hidden Markov model for transmembrane protein detection and topology prediction and its applications to complete genomes. Bioinformatics, 21(9):1853-1858.
18. Viklund, H. and Elofsson, A. (2004). Best α-helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information. Protein Sci, 13:1908-1917.
19. Kall, L., Krogh, A., and Sonnhammer, E. (2005). An HMM posterior decoder for sequence feature prediction that includes homology in formation. Bioinformatics, 21(1):i251-i.257.
20. Bendtsen, J., Nielsen, H., von Heijne, G., and Brunak, S. (2004). Improved prediction of signal peptides: SignalP 3.0. J Mol Biol, 340(4):783-795.
21. Nielsen, II. and Krogh, A. (1998). Prediction of signal peptides and signal anchors by a hidden Markov model. Proc Int Conf Intell Syst Mol Biol, 6:122-130.
22. Juncker, A., Willenbrock, H., von Heijne, G., Brunak, S., Nielsen, H., and Krogh, A. (2003). Prediction of lipoprotein signal peptides in Gram-negative bacteria. Protein Sci, 12:1652-1662.
23. Klee, E. and Ellis, L. (2005). Evaluating eukaryotic secreted protein prediction. BMC Bioinformatics, 6(1):256.
24. Martelli, P.L., Fariselli P., and Casadio, R. (2003). An ENSEMBLE machine learning approach for the prediction of all-alpha membrane proteins. Bioinformatics, 19(Suppl 1):I205-I211.
25. Fariselli, P., Finelli, M., Marchignoli, D., Martelli, P.L., Rossi, I., and Casadio, R. (2003). MaxSubSeq: an algorithm for segment-length optimization. The case study of the transmembrane spanning segments. Bioinformatics, 19:500-505.
26. Delorenzi, M. and Speed, T. (2002). An HMM: model for coiled-coil domains and a comparison with PSSM-based predictions. Bioinformatics, 18(4):617-625.
27. Kabsch, W. and Sander, C. (1983). How good are predictions of protein secondary structure? Biopolymers, 22:2577-2637.
28. Heinig, M. and Frishman, D. (2004). STRIDE: a web server for secondary structure assignment from known atomic coordinates of proteins. Nucleic Acids Res, 32:500-502.
29. Rost, B. and Sander, C. (1993). Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol, 232(2):584-599.
30. Jones, D. (1999). Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol, 292(2):195-202.
31. Ward, J., McGuffin, L., Buxton, B., and Jones, D. (2003). Secondary structure prediction with support vector machines. Bioinformatics, 19(13):1650-1655.
32. Asai, K., Hayamizu, S., and Handa, K. (1993). Prediction of protein secondary structure by the hidden Markov model. Bioinformatics, 9:141-146.
33. Zemla, A., Venclovas, C., Moult, J., and Fidelis, K. (2001). Processing and evaluation of predictions in CASP 4. Proteins, 45(Suppl 5):13-21.
34. Stultz, C. (1993). Structural analysis based on state-space modeling. Protein Sci, 2(3):305-314.
35. Bienkowska, J., He, H., and Smith, T. (2001). Automatic pattern embedding in protein structure models. Intelligent Systems, IEEE [see also IEEE Expert], 16(6):21-25.
36. Rooman, M.J., Kocher, J.P., and Wodak, S.J. (1991). Prediction of protein backbone conformation based on seven structure assignments. Influence of local interactions. J Mol Biol, 221(3):961-979.
37. de Brevem, A.G., Valadie, H., Hazout, S., and Etchebest, C. (2002). Extension of a local backbone description using a structural alphabet: a new approach to the sequence-structure relationship. Protein Sci, 11:2871-2886.
38. Bysttoff, C. and Baker, D. (1998). Prediction of local structure in proteins using a library of sequence-structure motifs. J Mol Biol, 281(3):565-577.
39. Unger, R., Hard, D., Wherland, S., and Sussman, J. (1989). A 3D building blocks approach to analyzing and predicting structure of proteins. Proteins, 5:355-373.
40. Camproux, A., Tuffery, P., Chevrolat, J., Boisvieux, J., and Hazout, S. (1999). Hidden Markov model approach for identifying the modulai- framework of the protein backbone. Protein Eng, 12(12):1063-1073.
41. Kent, J. T. and Hamelryck, T. (2005). Using the Fisher-Bingham distribution in stochastic models for protein structure. In Barber, S., Baxter, P. D., V.Mardia, K., and Walls, R. E., editors, Proceedings of the 24th LASR Workshop, 57-60. Leeds University Press.
42. Hamelryck, T., Kent, JT, Krogh, A. (2006) Sampling realistic protein conformations using local structural bias. PLoS J Comput Biol, 2(9):e131.
43. Bystroff, C., Thorsson, V., and Baker, D. (2000). HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins. J Mol Biol, 301(1):173-190.
44. Bystroff, C. and Shao, Y. (2002). Fully automated ab initio protein structure prediction using I-SITES, HMMSTR and ROSETTA. Bioinformatics, 18(1):54-61.
45. Shao, Y. and Bystroff, C. (2003). Predicting interresidue contacts using templates and pathways. Proteins, 53(Supple 6):497-502.
46. Zahn, R., Liu, A., Luhrs, T., Riek, R., von Schroetter, C., Garcia, F., Billeter, M., Calzolai, L., Wider, G., and Wuthrich, K. (2000). NMR solution structure of the human prion protein. Proc Natl Acad Sci USA, 97(1):145-150.
47. Knaus, K., Morillas, M., Swietnicki, W., Malone, M., Surewicz, W., and Yee, V. (2001). Crystal structure of the human prion protein reveals a mechanism for oligomerization. Nat Struct Biol, 8:770-774.
48. Kovacs, G., Trabattoni, G., Hainfellner, J., Ironside, J., Knight, R., and Budka, H. (2002). Mutations of the prion protein gene. J Neurol, 249(11):1567-1582.
49. Bateman, A., Coin, L., Durbin, R., Finn, R.D., Hollich, V., Griffiths-Jones, S., Khanna, A., Marshall, M., Moxon, S., Sonnhammer, E.L., Studholme, D.J., Yeats, C., and Eddy, S.R. (2004). The Pfam protein families database. Nucleic Acids Res 32: D138-D141.
50. Karplus, K., Sjoelander, K., Barrett, C., Cline, M., Haussier, D., Hughey, R., Holm, L., and Sander, C. (1997). Predicting protein structure using hidden Markov models. Proteins, 29(Suppl 1):134-139.
51. Tsigelny, I., Sharikov, Y., and Ten Eyck, L. (2002). Hidden Markov Models-based system (HMMSPECTR) for detecting structural homologies on the basis of sequential information. Protein Eng, 15(5):347-352.
52. Krogh, A., Brown, M., Mian, I. S., Sjölander, K., and Haussler, D. (1994). Hidden Markov Models in computational biology: applications to protein modeling. J Mol Biol., 235:1501-1531.