The tetratricopeptide repeats (TPR)-like superfamily of proteins in Leishmania spp., as revealed by multi-relational data mining

Pattern Recognition Letters

Volumn 31, Issue 14, 2010, Pages 2178-2189

  Diniz, Michely C ^a   Pacheco, Ana Carolina L ^a   Girão, Karen T ^a   Araujo, Fabiana F ^a   Walter, Cezar A ^a   Oliveira, Diana M ^a  

^a Campus Do Itaperi   (Brazil)

Author keywords

Hidden Markov models; Leishmania proteins; Multi relational data mining; Tetratricopeptide repeat motif; Viterbi algorithm

Indexed keywords

ASSOCIATION RULE DISCOVERY; GENOME DATABASE; LEISHMANIA; LEISHMANIA PROTEINS; MOTIF DISCOVERY; MULTIRELATIONAL DATA MINING; PATHOGENIC PROTOZOA; PROBABILISTIC RELATIONAL MODELS; PROTEIN SEQUENCE ANALYSIS; SEQUENCE ANALYSIS; TARGET SEQUENCES; TETRATRICOPEPTIDE REPEAT MOTIF; TETRATRICOPEPTIDE REPEATS; VITERBI;

ASSOCIATIVE PROCESSING; DATA MINING; HEURISTIC METHODS; PATTERN MATCHING; PROBABILITY DISTRIBUTIONS; PROTEINS; PROTOZOA; SPEECH RECOGNITION; VITERBI ALGORITHM;

HIDDEN MARKOV MODELS;

EID: 77957926291     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2010.04.008     Document Type: Article

References (80)

1. S.F. Altschul, T.L. Madden, A.A. Schäffer, J. Zhang, Z. Zhang, W. Miller, and D.J. Lipman Gapped BLAST and PSI-BLAST: A new generation of protein database search programs Nucleic Acids Res. 25 1997 3389 3402
2. C. Aurrecoechea, M. Heiges, H. Wang, Z. Wang, S. Fischer, P. Rhodes, J. Miller, E. Kraemer, C.J. Stoeckert Jr., D.S. Roos, and J.C. Kissinger ApiDB: Integrated resources for the apicomplexan bioinformatics resource center Nucleic Acids Res. 35 2007 D427 D430
3. G.L. Blatch, and M. Lässle The tetratricopeptide repeat: A structural motif mediating protein-protein interactions BioEssays 21 1999 932 939
4. A.K. Bjorklund, D. Ekman, and A. Elofsson Expansion of protein domain repeats PLoS Comput. Biol. 2 2006 e114
5. C. Burge, and S. Karlin Prediction of complete gene structures in human genomic DNA J. Mol. Biol. 268 1997 78 94
6. E.A. Champion, B.H. Lane, M.E. Jackrel, L. Regan, and S.J. Baserga A direct interaction between the Utp6 half-a-tetratricopeptide repeat domain and a specific peptide in Utp21 is essential for efficient pre-rRNA processing Mol. Cell. Biol. 28 2008 6547 6556
7. Craven, M., Page, D., Shavlik, J., Bockhorst, J., Glasner, J., 2000. A probabilistic learning approach to whole-genome operon prediction. In: Proc. Eighth Internat. Conf. on Intelligent Systems for Molecular Biology, AAAI Press, La Jolla, CA, pp. 116-127.
8. L.D. D'Andrea, and L. Regan TPR proteins: The versatile helix Trends Biochem. Sci. 28 2003 655 662
9. A.K. Das, P.W. Cohen, and D. Barford The structure of the tetratricopeptide repeats of protein phosphatase 5: Implications for TPR-mediated protein-protein interactions EMBO J. 17 1998 1192 1199
10. Dehaspe, L., De Raedt, L., 1997. Mining association rules in multiple relations. In: Proc. Sevneth Internat. Workshop on Inductive Logic Programming, vol. 1297, Springer-Verlag, LNAI, Heidelberg.
11. R. Durbin, S.R. Eddy, A. Krogh, and G.J. Mitchison Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids first ed. 1998 Cambridge University Press Cambridge, UK
12. Dyrka, W., 2007. Probabilistic Context-Free Grammar for Pattern Detection in Protein Sequences. In: MSc Thesis. Kingston University, London.
13. W. Dyrka, and J.-C. Nebel A stochastic context free grammar based framework for analysis of protein sequences BMC Bioinf. 10 2009 323
14. S.R. Eddy Profile hidden Markov models Bioinformatics 14 1998 755 763
15. R.C. Edgar MUSCLE: Multiple sequence alignment with high accuracy and high throughput Nucleic Acids Res. 32 2004 1792 1797
16. R.D. Finn, J. Tate, J. Mistry, P.C. Coggill, J.S. Sammut, H.R. Hotz, G. Ceric, K. Forslund, S.R. Eddy, E.L. Sonnhammer, and A. Bateman The Pfam protein families database Nucleic Acids Res. 36 Database Issue 2008 D281 D288
17. Forney, G.D. Jr., 1973. The Viterbi algorithm. In: Proc. IEEE, vol. 61. p. 268.
18. Friedman, N., Getoor, L., Koller, D., Pfeffer, A., 1999. Learning probabilistic relational models. In: Proc. Internat. Joint Conf. on Artificial Intelligence, Morgan Kaufman, Stockholm, Sweden, pp. 1300-1307.
19. T. Friedrich, B. Pils, T. Dandekar, J. Schultz, and T. Müller Modelling interaction sites in protein domains with interaction profile hidden Markov models Bioinformatics 22 2006 2851 2857
20. Garey, M.R., Johnson, D.S., 1979. Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman, p. 202. ISBN:0-7167-1045-5. A1.4: GT 48.
21. Getoor, L., 2001. Multi-relational data mining using probabilistic relational models: Research summary. In: Knobbe, A.J., van der Wallen, D.M.G. (Eds.), Proc. First Workshop in Multi-relational Data Mining, KDD, 2001.
22. L. Getoor, N. Friedman, D. Koller, and A. Pfeffer Learning probabilistic relational models S. Dzeroski, N. Lavrac, Relational Data Mining 2001 Kluwer 307 335
23. L. Getoor, and J. Grant PRL: A probabilistic relational language Mach. Learn. J. 62 2006 7 31
24. Getoor, L., Taskar, B., Koller, D., 2001. Using probabilistic models for selectivity estimation. In: Proc. ACM SIGMOD Internat. Conf. on Management of Data, ACM Press, 2001, pp. 461-472.
25. Girão, K.T., Oliveira, F.C.E., Farias, K.M., Maia, I.M.C., Silva, S.C., Gadelha, C.R.F., Carneiro, L.D.G., Pacheco, A.C.L., Kamimura, M.T., Diniz, M.C., Silva, M.C., Oliveira, D.M., 2008. Multi-relational Data Mining for Tetratricopeptide Repeats (TPR)-Like Superfamily Members in Leishmania spp.: Acting-by-Connecting Proteins. Lecture Notes in Computer Science, vol. 5265, Pattern Recognition in Bioinformatics, 2008, pp. 359-372. doi:10.1007/978-3-540- 88436-1.
26. J. Gough, K. Karplus, R. Hughey, and C. Chothia Assignment of Homology to Genome Sequences using a Library of Hidden Markov Models that Represent all Proteins of Known Structure J. Mol. Biol. 313 2001 903 919
27. M.R. Groves, and D. Barford Topological characteristics of helical repeat proteins Curr. Opin. Struct. Biol. 9 1999 383 389
28. C. Hertz-Fowler, C.S. Peacock, C. Wood, M. Aslett, A. Kerhornou, P. Mooney, A. Tivey, M. Berriman, N. Hall, K. Rutherford, J. Parkhill, A.C. Ivens, M.A. Rajandream, and B. Barrel GeneDB: A resource for prokaryotic and eukaryotic organisms Nucleic Acids Res. 32 2004 D339 D343
29. T. Ideker, V. Bafna, and T. Lemberger Integrating scientific cultures Mol. Syst. Biol. 3 2007 105 112
30. M.R. Karpenahalli, A.N. Lupas, and J. Söding TPRpred: A tool for prediction of TPR-, PPR- and SEL1-like repeats from protein sequences BMC Bioinf. 8 2007 2
31. K. Karplus, C. Barrett, and R. Hughey Hidden Markov models for detecting remote protein homologies Bioinformatics 14 1998 846 856
32. B. Kobe, and A.V. Kajava When protein folding is simplified to protein coiling: The continuum of solenoid protein structures Trends Biochem. Sci. 25 2000 509 515
33. H. Koga, H. Terasawa, H. Nunoi, K. Takeshige, F. Inagaki, and H. Sumimoto Tetratricopeptide repeat (TPR) motifs of p67phox participate in interaction with the small GTPase Rac and activation of the phagocyte NADPH oxidase Biol. Chem. 274 1999 25051 25060
34. E. Kotera, M. Tasaka, and T. Shikanai A pentatricopeptide repeat protein is essential for RNA editing in chloroplasts Nature 433 2005 326 330
35. C. Lawrence, S. Altschul, M. Boguski, J. Liu, A. Neuwald, and J. Wootton Detecting subtle sequence signals: A Gibbs sampling strategy for multiple alignment Science 262 1993 208 214
36. I. Letunic, T. Doerks, and P. Bork SMART 6: Recent updates and new developments Nucleic Acids Res. 37 Database issue 2009 D229 D232
37. W. Li, L. Jaroszewski, and A. Godzik Clustering of highly homologous sequences to reduce the size of large protein databases Bioinformatics 17 2001 282 283
38. W. Li, L. Jaroszewski, and A. Godzik Tolerating some redundancy significantly speeds up clustering of large protein databases Bioinformatics 18 2002 77 82
39. C. Lurin, C. Andrés, and S. Aubourg Genome-wide analysis of arabidopsis pentatricopeptide repeat proteins reveals their essential role in organelle biogenesis The Plant Cell 16 2004 2089 2103
40. M. Madera Profile Comparer (PRC): A program for scoring and aligning profile hidden Markov models Bioinformatics 24 2008 2630 2631
41. M. Madera, C. Vogel, S.K. Kummerfeld, C. Chothia, and J. Gough The superfamily database in 2004: Additions and improvements Nucleic Acids Res. 32 2004 D235 D239
42. E.R.G. Main, A.R. Lowe, S.G.J. Mochrie, S.E. Jackson, and L. Regan A recurring theme in protein engineering: The design, stability and folding of repeat proteins Curr. Opin. Struct. Biol. 15 2005 464 471
43. W.H. Majoros, M. Pertea, and S.L. Salzberg TigrScan and GlimmerHMM: Two open source ab initio eukaryotic gene-finders Bioinformatics 20 2004 2878 2879
44. A. Marchler-Bauer, J.B. Anderson, M.K. Derbyshire, M.K. Derbyshire, C. DeWeese-Scott, N.R. Gonzales, and M. Gwadz CDD: A conserved domain database for interactive domain family analysis Nucleic Acids Res. 35 2007 D237 D240
45. M.K. Mingler, A.M. Hingst, S.L. Clement, L.E. Yu, L. Reifur, and D.J. Koslowsky Identification of pentatricopeptide repeat proteins in Trypanosoma brucei Mol. Biochem. Parasitol. 150 2006 37 45
46. A.G. Murzin, S.E. Brenner, T. Hubbard, and C. Chothia SCOP: A structural classification of proteins database for the investigation of sequences and structures J. Mol. Biol. 247 1995 536 540
47. N. O'Toole, M. Hattori, C. Andres, K. Iida, C. Lurin, C. Schmitz-Linneweber, M. Sugita, and I. Small On the expansion of the pentatricopeptide repeat gene family in plants Mol. Biol. Evol. 25 2008 1120 1128
48. Pacheco, A.C.L., Araujo, F.F., Kamimura, M.T., Medeiros, S.R., Viana, D.A., Oliveira, F.C.E., Araújo-Filho, R., Costa, M.P., Oliveira, D.M., 2007. Following the Viterbi Path to deduce flagellar actin-interacting proteins of Leishmania spp.: Report on cofilins and twinfilins. In: Pham, T. (Ed.), Computer Models for Life Sciences, CMLS'07, AIP Proc., vol. 952. American Institute of Physics, Australia, 2007, pp. 315-324.
49. A.C.L. Pacheco, F.F. Araujo, M.T. Kamimura, S.C. Silva, M.C. Diniz, F.C.E. Oliveira, R. Araujo Filho, M.P. Costa, and D.M. Oliveira Hidden Markov models and the Viterbi algorithm applied to integrated bioinformatics analyses of putative flagellar actin-interacting proteins in Leishmania spp Internat. J. Comput. Aided Eng. Technol. (IJCAET) 1 2009 420 436
50. D. Page, and M. Craven Biological applications of multi-relational data mining SIGKDD Explorations 5 2003 69 79
51. J.S. Papadopoulos, and R. Agarwala COBALT: Constraint-based alignment tool for multiple protein sequences Bioinformatics 23 2007 1073 1079
52. P.J. Preker, and W. Keller The HAT helix, a repetitive motif implicated in RNA processing Trends Biochem. Sci. 23 1998 15 16
53. M. Pusnik, I. Small, L.K. Read, T. Fabbro, and A. Schneider Pentatricopeptide repeat proteins in Trypanosoma brucei function in mitochondrial ribosomes Mol. Cell. Biol. 27 2007 6876 6888
54. E. Rivas, and S.R. Eddy Noncoding RNA gene detection using comparative sequence analysis BMC Bioinf. 2 1 2001 8
55. E. Rivals, C. Bruyre, C. Toffano-Nioche, and A. Lecharny Formation of the arabidopsis pentatricopeptide repeat family Plant Physiol. 141 2006 825 839
56. Y. Sakakibara, M. Brown, R. Hughey, I.S. Mian, K. Sjolander, R. Underwood, and D. Haussler Stochastic context-free grammars for tRNA Nucleic Acids Res. 22 1994 5112 5120
57. C. Scheufler, A. Brinker, G. Bourenkov, S. Pegoraro, L. Moroder, H. Bartunik, F.U. Hartl, and I. Moarefi Structure of TPR domain-peptide complexes: Critical elements in the assembly of the Hsp70-Hsp90 multichaperone machine Cell 101 2000 199 210
58. S. Schmidler, J. Liu, and D. Brutlag Bayesian segmentation of protein secondary structure J. Comput. Biol. 7 2000 233 248
59. J. Schultz, F. Milpetz, P. Bork, and C.P. Ponting SMART, a simple modular architecture research tool: Identification of signaling domains Proc. Natl. Acad. Sci. USA 95 1998 5857 5864
60. E. Segal, B. Taskar, A. Gasch, N. Friedman, and D. Koller Rich probabilistic models for gene expression Bioinformatics 1 2001 1 10
61. F. Servant, C. Bru, S. Carrre, E. Courcelle, J. Gouzy, D. Peyruc, and D. Kahn ProDom: Automated clustering of homologous domains Brief. Bioinf. 3 2002 246 251
62. C.J. Sigrist, L. Cerutti, E. de Castro, P.S. Langendijk-Genevaux, V. Bulliard, A. Bairoch, and N. Hulo PROSITE, a protein domain database for functional characterization and annotation Nucleic Acids Res. 38 Database issue 2010 D161 D166
63. I.D. Small, and N. Peeters The PPR motif - a TPR-related motif prevalent in plant organellar proteins Trends Biochem. Sci. 25 2000 46 47
64. The Gene Ontology Consortium, 2000. Gene ontology: Tool for the unification of biology. Nature Genet. 25, 25-29.
65. The UniProt Consortium. The universal protein resource (UniProt) in 2010. Nucleic Acids Res. (2010) D142-D148.
66. Xu, R., Supekar, K., Huang, Y., Das, A., Garber, A., 2006. Combining text classification and Hidden Markov modeling techniques for structuring randomized clinical trial abstracts. In: AMIA Annu. Symp. Proc. 2006, pp. 824-828.
67. Wilson, D., Pethica, R., Zhou, Y., Talbot, C., Vogel, C., Madera, M., Chothia, C., Gough, J., 2009. SUPERFAMILY - comparative genomics, datamining and sophisticated visualisation. Nucleic Acids Res. 37 (Database issue) D380-D386.
68. S. Winters-Hilt Hidden Markov model variants and their application BMC Bioinf. 7 2006 S14
69. NCBI (National Center for Biotechnology Information/Entrez/Cn3D (All Databases) http://www.ncbi.nlm.nih.gov/sites/gquery.
70. The Pathogen Sequencing Unit - Wellcome Trust Sanger Institute - GeneDB - www.genedb.org.
71. TriTrypDB version 2.0 - http://TriTrypDB.org.
72. The UniProt Consortium - www.uniprot.org.
73. Swiss-Prot/trEMBL www.expasy.org/sprot.
74. AMIGO after GeneDB access. www.genedb.org/amigo/perl.
75. SMART http://smart.embl.de.
76. SUPERFAMILY, http://supfam.cs.bris.ac.uk.
77. TPRpred http://toolkit.tuebingen.mpg.de/tprpred.
78. Arabidopsis Genome Initiative (AGI, 2000) http://www.arabidopsis.org/ portals.
79. Pfam http://pfam.wustl.edu/hmmsearch.shtm.
80. Gene Ontology www.geneontology.org.