Abstract
We describe an approach to clustering the yeast protein-protein interaction network in order to identify functional modules, groups of proteins forming multi-protein complexes accomplishing various functions in the cell. We have developed a clustering method that accounts for the small-world nature of the network. The algorithm makes use of the concept of k-cores in a graph, and employs recursive spectral clustering to compute the functional modules. The computed clusters are annotated using their protein memberships into known multi-protein complexes in the yeast. We also dissect the protein interaction network into a global subnetwork of hub proteins (connected to several clusters), and a local network consisting of cluster proteins.
Research supported by NSF grant CCR0306334, by subcontract B542604 from the Lawrence Livermore National Laboratory, and by a grant from the Office of Research at Old Dominion University.
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Gavine, A.-C., Bosche, M., Krause, R., et al.: Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141–147 (2002)
Achsel, T., et al.: The Sm domain is an ancient RNA-binding motif with oligo(U) specificity. Procs. Natl. Acad. Sci. 98, 3685–3689 (2001)
Angenstein, F., et al.: A receptor for activated C kinase is part of messenger ribonucleoprotein complexes associated with polyA-mRNA in neurons. J. Neurosci. 22, 8827–8837 (2002)
Bader, G.D., Hogue, C.W.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4(2), 27 (2003)
Bi, X., Goss, D.: Wheat germ poly (A)-binding protein increases ATPase and the RNA helicase activity of translation initiation factors eIF4A, eIF4B and eIF-iso-4F. J. Biol. Chem. 275, 17740–17746 (2000)
Bornholdt, S., Schuster, H.G. (eds.): Handbook of Graphs and Networks. Wiley VCH, Chichester (2003)
Chung, F., Lu, L.: The average distances in random graphs with given expected degrees. Procs. Natl. Acad. Sci. 99(25), 15879–15882 (2002)
Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Procs. ACM Internatl. Conf. Knowledge Discovery in Data Mining (KDD) (2001)
Ding, C., He, X., Meraz, R.F., Holbrook, S.R.: A unified representation of multi-protein complex data for modeling interaction networks. Proteins: Structure, Function, and Genetics (2004) (to appear)
Ding, C., He, X., Zha, H., Gu, M., Simon, H.: A MinMaxCut spectral method for data clustering and graph partitioning. In: Procs. IEEE Internatl. Conf. Data Mining (ICDM), pp. 107–114 (2001)
Fromont-Racine, M., et al.: Genome-wide protein interaction screens reveal functional networks involving Sm-like proteins. Yeast 17, 95–110 (2000)
Galisson, F., Legrain, P.: The biochemical defects of PRP4-1 and PRP6-1 yeast splicing mutants reveal that the PRP6 protein is required for the accumulation of the [U4/U6.U5] tri-snRNP. Nucl. Acids Res. 21, 1555–1562 (1993)
Han, J.D.J., Dupuy, D., Bertin, N., et al.: Effect of sampling on topology predictions of protein-protein interaction networks. Nature Biotechnology 23, 839–844 (2005)
Hartwell, L.H., Hopfeld, J.J., Leibler, S., Murray, A.W.: From molecular to modular cell biology. Nature 402, C47–C52 (1999)
Ho, Y., Gruhler, A., Heilbut, A., et al.: Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415, 180–193 (2002)
Ito, T., Chiba, T., Ozawa, R., et al.: A comprehensive two hybrid analysis to explore the yeast protein interactome. Procs. Natl. Acad. Sci. 98, 4569–4574 (2001)
Jarvis, A.A., Patrick, E.A.: Clustering based on a similarity measure based on shared nearest neighbors. IEEE Trans. Computers C-22, 1025–1034 (1973)
Noble, C., et al.: Rna14-Rna15 assembly mediates the RNA-binding capability of Saccharomyces cerevisiae cleavage factor IA. Nucl. Acids Res. 32, 3364–3375 (2004)
Ramadan, E., Tarafdar, A., Pothen, A.: A hypergraph model for the yeast protein complex network. In: Procs. Workshop High Performance Computational Biology (HICOMB), p. 8. IEEE / ACM, Los Alamitos (2004) (CDROM)
Rymond, B.C.: Convergent transcripts of the yeast PRP38-SMD1 locus encode two essential splicing factors, including the D1 core polypeptide of small nuclear ribonucleoprotein particles. Procs. Natl. Acad. Sci. 90, 848–852 (1993)
Spirin, V., Mirny, L.A.: Protein complexes and functional modules in molecular networks. Procs. Natl. Acad. Sci. 100, 12123–12128 (2003)
Tan, P., Steinbach, M., Kumar, V.: Introduction to Datamining. Addison Wesley, Reading (2005)
Urushiyama, S., et al.: The prp1+ gene required for pre-mRNA splicing in Schizosaccharomyces pombe encodes a protein that contains TPR motifs and is similar to Prp6p of budding yeast. Genetics 147, 101–115 (1997)
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Ramadan, E., Osgood, C., Pothen, A. (2005). The Architecture of a Proteomic Network in the Yeast. In: R. Berthold, M., Glen, R.C., Diederichs, K., Kohlbacher, O., Fischer, I. (eds) Computational Life Sciences. CompLife 2005. Lecture Notes in Computer Science(), vol 3695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11560500_24
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DOI: https://doi.org/10.1007/11560500_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29104-6
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