Multi-relational data mining for tetratricopeptide repeats (TPR)-like superfamily members in Leishmania spp.: Acting-by-connecting proteins

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5265 LNBI, Issue , 2008, Pages 359-372

  Girão, Karen T ^a   Oliveira, Fátima C E ^a   Farias, Kaio M ^a   Maia, Italo M C ^a   Silva, Samara C ^a   Gadelha, Carla R F ^a   Carneiro, Laura D G ^a   Pacheco, Ana C L ^a   Kamimura, Michel T ^a   Diniz, Michely C ^a   Silva, Maria C ^a   Oliveira, Diana M ^a  

^a Faculdade de Veterinaria   (Brazil)

Author keywords

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

Indexed keywords

BIOINFORMATICS; HIDDEN MARKOV MODELS; PATTERN RECOGNITION; PROTEINS; PROTOZOA; RELATIONAL DATABASE SYSTEMS; SCAFFOLDS (BIOLOGY); STOCHASTIC SYSTEMS; VITERBI ALGORITHM;

ASSOCIATION RULE DISCOVERY; HIDDEN MARKOV MODELS (HMMS); LEISHMANIA PROTEINS; MULTIRELATIONAL DATA MINING; PROBABILISTIC RELATIONAL MODELS; PROTEIN-PROTEIN INTERACTIONS; TETRATRICOPEPTIDE REPEAT MOTIF; TETRATRICOPEPTIDE REPEATS;

DATA MINING;

EID: 57049152311     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-88436-1_31     Document Type: Conference Paper

References (52)

1. Ideker, T., Bafna, V., Lemberger, T.: Integrating scientific cultures. Mol. Syst. Biol. 3, 105-112 (2007), doi: 10.1038/msb4100145
2. Getoor, L.: Multi-relational data mining using probabilistic relational models: research summary. In: Knobbe, A.J., van der Wallen, D.M.G. (eds.) Proceedings 1st Workshop in Multi-relational Data Mining, KDD (2001)
3. Dehaspe, L., De Raedt, L.: Mining association rules in multiple relations. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297. Springer, Heidelberg (1997)
4. Eddy, S.R.: Profile hidden Markov models. Bioinformatics 14, 755-763 (1998)
5. Winters-Hilt, S.: Hidden Markov Model Variants and their Application. BMC Bioinformatics 7, 14 (2006)
6. Forney Jr., G.D.: The Viterbi algorithm. Proc. IEEE 61, 268 (1973)
7. Blatch, G.L., Lässle, M.: The tetratricopeptide repeat: a structural motif mediating proteinprotein interactions. Bio. Essays 21, 932-939 (1999)
8. D'Andrea, L.D., Regan, L.: TPR proteins: the versatile helix. Trends Biochem. Sci. 28, 655-662 (2003)
9. Das, A.K., Cohen, P.W., Barford, D.: The structure of the tetratricopeptide repeats of protein phosphatase 5: implications for TPR-mediated protein-protein interactions. EMBO. J. 17, 1192-1199 (1998)
10. Small, I.D., Peeters, N.: The PPR motif - a TPR-related motif prevalent in plant organellar proteins. Trends Biochem. Sci. 25, 46-47 (2000)
11. Kotera, E., Tasaka, M., Shikanai, T.: A pentatricopeptide repeat protein is essential for RNA editing in chloroplasts. Nature 433, 326-330 (2005)
12. Preker, P.J., Keller, W.: The HAT helix, a repetitive motif implicated in RNA processing. Trends Biochem. Sci. 23, 15-16 (1998)
13. Scheufler, C., Blinker, A., Bourenkov, G., et al.: Structure of TPR domain-peptide complexes: critical elements in the assembly of the Hsp70-Hsp90 multichaperone machine. Cell 101, 199-210 (2000)
14. Koga, H., Terasawa, H., Nunoi, H., et al: Tetratricopeptide Repeat (TPR) Motifs of p67phox Participate in Interaction with the Small GTPase Rac and Activation of the Phagocyte NADPH Oxidase. Biol. Chem. 274, 25051-25060 (1999)
15. Lurin, C., Andrés, C., Aubourg, S., et al.: Genome-Wide Analysis of Arabidopsis Pentatricopeptide Repeat Proteins Reveals Their Essential Role in Organelle Biogenesis. The Plant Cell 16, 2089-2103 (2004)
16. Rivals, E., Bruyère, C., Toffano-Nioche, C., Lecharny, A.: Formation of the Arabidopsis Pentatricopeptide Repeat Family. Plant Physiol. 141, 825-839 (2006)
17. Main, E.R.G., Lowe, A.R., Mochrie, S.G.J., Jackson, S.E., Regan, L.: A recurring theme in protein engineering: the design, stability and folding of repeat proteins. Curr. Opin. Struct. Biol. 15, 464-471 (2005)
18. Karpenahalli, M.R., Lupas, A.N., Söding, J.: TPRpred: a tool for prediction of TPR-, PPR-and SEL1-like repeats from protein sequences. BMC Bioinformatics 8, 2 (2007), doi:10.1186/1471-2105-8-2
19. Groves, M.R., Barford, D.: Topological characteristics of helical repeat proteins. Curr. Opin. Struct. Biol. 9, 383-389 (1999)
20. Kobe, B., Kajava, A.V.: When protein folding is simplified to protein coiling: the continuum of solenoid protein structures. Trends Biochem. Sci. 25, 509-515 (2000)
21. Sonnhammer, E.L., Eddy, S.R., Durbin, R.: Pfam: a comprehensive database of protein domain families based on seed alignments. Proteins 28, 405-420 (1997)
22. Schultz, J., Milpetz, F., Bork, P., Ponting, C.P.: SMART, a simple modular architecture research tool: identification of signaling domains. Proc. Natl. Acad. Sci. USA 95, 5857-5864 (1998)
23. Madera, M., Vogel, C., Kummerfeld, S.K., Chothia, C., Gough, J.: The superfamily database in. 2004: additions and improvements. Nucleic Acids Res. 32, 235-239 (2004)
24. Hertz-Fowler, C., Peacock, C.S., Wood, C., et al.: GeneDB: a resource for prokaryotic and eukaryotic organisms. Nucleic Acids Res. 32, D339-D343 (2004) The Pathogen Sequencing Unit - Wellcome Trust Sanger Institute - GeneDB - (2004), http://www.genedb.org
25. Altschul, S.F., Madden, T.L., Schärfer, A.A., et al.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389-3402 (1997)
26. Majoras, W.H., Pertea, M., Salzberg, S.L.: TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics 20, 2878-2879 (2004)
27. Marchler-Bauer, A., Anderson, J.B., Derbyshire, M.K., et al.: CDD: a conserved domain database for interactive domain family analysis. Nucleic Acids Res. 35, D237-240 (2007)
28. Edgar, R.C.: MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792-1797 (2004)
29. Pacheco, A.C.L., Araujo, F.F., Kamimura, M.T., et al: Following the Viterbi Path to Deduce Flagellar Actin-Interacting Proteins of Leishmania spp.: Report on Cofilins and Twinfilins. In: Pham, T. (ed.) AIP Proceedings of Computer Models for Life Sciences, CMLS 2007, vol. 952, pp. 315-324. American Institute of Physics, Australia (2007)
30. Murzin, A.G., Brenner, S.E., Hubbard, T., Chothia, C.: SCOP: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol. 247, 536-540 (1995)
31. Li, W., Jaroszewski, L., Godzik, A.: Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics 17, 282-283 (2001)
32. Li, W., Jaroszewski, L., Godzik, A.: Tolerating some redundancy significantly speeds up clustering of large protein databases. Bioinformatics 18, 77-82 (2002)
33. Karplus, K., Barrett, C., Hughey, R.: Hidden Markov models for detecting remote protein homologies. Bioinformatics 14, 846-856 (1998)
34. Friedrich, T., Pils, B., Dandekar, T., Schultz, J., Müller, T.: Modelling interaction sites in protein domains with interaction profile hidden Markov models. Bioinformatics 22, 2851-2857 (2006), doi:10.1093/bioinformatics/ btl486
35. Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1300-1307. Morgan Kaufman, Stockholm (1999)
36. Getoor, L., Friedman, N., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Dzeroski, S., Lavrac, N. (eds.) Relational Data Mining, pp. 307-335. Kluwer, Dordrecht (2001)
37. Getoor, L., Taskar, B., Koller, D.: Using probabilistic models for selectivity estimation. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 461-472. ACM Press, New York (2001)
38. Craven, M., Page, D., Shavlik, J., Bockhorst, J., Glasner, J.: A probabilistic learning approach to whole-genome operon prediction. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pp. 116-127. AAAI Press, La Jolla (2000)
39. Segal, E., Taskar, B., Gasch, A., Friedman, N., Koller, D.: Rich probabilistic models for gene expression. Bioinformatics 1, 1-10 (2001)
40. Bjorklund, A.K., et al.: Expansion of protein domain repeats. PLoS Comput. Biol. 2, 114 (2006)
41. Servant, F., Bru, C., Carrère, S., et al.: ProDom: automated clustering of homologous domains. Brief. Bioinform. 3, 246-251 (2002)
42. Rivals, E., Bruyere, E., Toffano-Nioche, C., Lecharny, A.: Formation of the Arabidopsis pentatricopeptide repeat family. Plant Physiol. 141, 825-839 (2006)
43. Mingler, M.K., Hingst, A.M., Clement, S.L., et al.: Identification of pentatricopeptide repeat proteins in Trypanosoma brucei. Mol. Biochem. Parasitol. 150, 37-45 (2006)
44. Pusnik, M., Small, I., Read, L.K., Fabbro, T., Schneider, A.: Pentatricopeptide Repeat Proteins in Trypanosoma brucei Function in Mitochondrial Ribosom.es. Mol. Cell. Biol. 27, 6876-6888 (2007)
45. NCBI (National Center for Biotechnology Information / Entrez / Cn3D (All Databases), http://www.nebi.nlm.nih.gov/sites/gcjuery
46. Swiss-Prot/trEMBL, http://www.expasy.org/sprot
47. AMIGO after GeneDB access, http://www.genedb.org/amigo/perl
48. SMART, http://smart.embl.de
49. Superfamily, http://supfam.es.bris.ac.uk
50. TPRpred, http://toolkit.tuebingen.mpg.de/tprpred
51. Arabidopsis Genome Initiative (AGI, 2000), http://www.arabidopsis.org/ portals
52. Pfam, http://pfam.wustl.edu/hmmsearch.shtm