Clustering-based approach for predicting motif pairs from protein interaction data

Journal of Bioinformatics and Computational Biology

Volumn 7, Issue 4, 2009, Pages 701-716

  Leung, Henry Chi Ming ^a   Siu, Man Hung ^a   Yiu, Siu Ming ^a   Chin, Francis Yuk Lun ^a   Sung, Ken Wing Kin ^b  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Motif pair; Protein domain; Protein protein interaction network

Indexed keywords

CHAPERONIN; FUNGAL PROTEIN;

AMINO ACID SEQUENCE; ARTICLE; CLUSTER ANALYSIS; COMPUTER PROGRAM; CONTROLLED STUDY; INTERMETHOD COMPARISON; MATHEMATICAL ANALYSIS; MAXIMUM LIKELIHOOD METHOD; NONHUMAN; PREDICTION; PROBABILITY; PROTEIN INTERACTION; PROTEIN MOTIF; PROTEIN PROTEIN INTERACTION; SCORING SYSTEM; SEQUENCE ANALYSIS;

ALGORITHMS; AMINO ACID MOTIFS; AMINO ACID SEQUENCE; BINDING SITES; CLUSTER ANALYSIS; MOLECULAR SEQUENCE DATA; PATTERN RECOGNITION, AUTOMATED; PROTEIN BINDING; PROTEIN INTERACTION MAPPING; PROTEIN STRUCTURE, TERTIARY; PROTEINS; SEQUENCE ANALYSIS, PROTEIN;

EID: 68349110569     PISSN: 02197200     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219720009004266     Document Type: Article

References (28)

1. Weidemann A, Konig G, Bunke D, Fischer P, Salbaum M, Masters C, Beyreuther K, Identification, biogenesis, and localization of precursors of Alzheimer's disease A4 amyloid protein, Cell 57:115-126, 1989.
2. Hu H et al., A map of WW domain family interactions, Proteomics 4(3):643-655, 2004.
3. Goehler H et al., Can we infer peptide recognition specifying mediated by SH3 domains? FEBS Lett 513(1):38-44, 2002.
4. Hans J, Brandt W, Vogt T, Site-directed mutagenesis and protein 3D-homology modelling suggest a catalytic mechanism for UDP-glucose-dependent betanidin 5-O-glucosyltransferase from Dorotheanthus bellidiformis, Plant J 39(3):319-333, 2004.
5. Karkkainen S, Hiipakka M, Wang JH, Kleino I, Vaha-Jaakkola M, Renkema GH, Liss M, Wagner R, Saksela K, Identification of preferred protein interactions by phagedisplay of the human Src homology-3 proteome, EMBO Rep 7(2):186-191, 2006.
6. Aragues R, Sali A, Bonet J, Marti-Renom MA, Oliva B, Characterization of protein hubs by inferring interacting motifs from protein interactions, PLoS Comput Biol 3(9):e178, 2007.
7. Leung H, Siu MH, Yiu SM, Chin F, Sung WK, Finding linear motif pairs from protein interaction networks: A probabilistic approach, Proc 6th Ann Int Conf. Computational Systems Bioinformatics CSB 2007, pp. 111-120, 2007.
8. Tan SH, Hugo W, Sung WK, Ng SK, A correlated motif approach for finding short linear motifs from protein interaction networks, BMC Bioinformatics 7:502, 2006.
9. Wang H, Segal E, Ben-Hur A, Li QR, Vidal M, Koller D, InSite: A computational method for identifying protein-protein interaction binding sites on a proteome-wide scale, Genome Biology 8(9):R192, 2007.
10. Bailey T, Elkan C, Fitting a mixture model by expectation maximization to discover motifs in biopolymers, ISMB, pp. 28-36, 1994.
11. Leung H, Chin F, Finding motifs from all sequences with and without binding sites, Bioinformatics 22(18):2217-2223, 2006.
12. Leung H, Chin F, Discovering motifs with transcription factor domain knowledge, PSB, pp. 472-483, 2007.
13. Pavesi G, Mereghetti P, Zambelli F, Stefani M, Mauri G, Pesole G, MoD Tools: Regulatory motif discovery in nucleotide sequences from co-regulated or homologous genes, Nucleic Acids Res 34 W566-W570, 2006.
14. Buhler J, Tompa M, Finding motifs using random projections, J Comput Biol 9(2):225-242, 2002.
15. Reiss DJ, Schwikowski B, Predicting protein-peptide interactions via a network-based motif sampler, Bioinformatics 20(supp 1):i274-i282, 2004.
16. Li H, Li J, Wong L, Discovering motif pairs at interaction sites from protein sequences on a proteome-wide scale, Bioinformatics 22 8):989-996, 2006.
17. Pei P, Zhang A, Towards detecting protein complexes from protein interaction data, LNCS 3992:734-741, 2006.
18. Barabasi, Albert-Laszlo, Scale-free networks, Scientific American 288:60-69, 2003.
19. Karp R, Reducibility among combinatorial problems, Complexity of Computer Computations, pp. 85-103, 1972.
20. Tong AHY et al., A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules, Science 295(5553):321-324, 2002.
21. Bader GD et al., BIND-The Biomolecular Interaction Network Database, Nucleic Acids Res 29:242-245, 2001.
22. Mewes HW, Heumann K, Kaps A, Mayer K, Pfeiffer F, Stocker S, Frishman D, MIPS: A database for genomes and protein sequences, Nucleic Acid Res 27:44-48, 1999.
23. Xenarios I, Rice DW, Salwinski L, Baron MK, Marcotte EM, Eisenberg D, DIP: The database of interacting proteins, Nucleic Acids Res 28 289-291, 2000.
24. Finn RD, Mistry J, Schuster-Bockler B, Griffiths-Jones S, Hollich V, Lassmann T, Moxon S, Marshall M, Khanna A, Durbin R, Eddy SR, Sonnhammer ELL, Bateman A, Pfam: Clans, web tools and services, Nucleic Acids Res Database Issue 34:D247-D251, 2006.
25. Russo AA, Jeffrey PD, Pavletich NP, Structural basis of cyclin-dependant kinase activation by phosphorylation, Nat Struct Biol 3 696-700, 1996.
26. Hunter T, Plowman GD, The protein kinases of budding yeast: Six score and more, Trends Biochem Sci 22:18-22, 1997.
27. Neer EJ, Schmidt CJ, Nambudripad R, Smith TF, The ancient regulatory-protein family of WD-repeat proteins, Nature 371 297-300, 1994.
28. Koonin EV et al., A common set of conserved motifs in a vast variety of putative nucleic acid-dependent ATPases including MCM proteins involved in the initiation of eukaryotic DNA replication, Nucleic Acids Res 21:2541-2547, 1993.