×

A local average connectivity-based method for identifying essential proteins from the network level. (English) Zbl 1366.92046

Summary: Identifying essential proteins is very important for understanding the minimal requirements of cellular survival and development. Fast growth in the amount of available protein-protein interactions has produced unprecedented opportunities for detecting protein essentiality from the network level. Essential proteins have been found to be more abundant among those highly connected proteins. However, there exist a number of highly connected proteins which are not essential. By analyzing these proteins, we find that few of their neighbors interact with each other. Thus, we propose a new local method, named LAC, to determine a protein’s essentiality by evaluating the relationship between a protein and its neighbors. The performance of LAC is validated based on the yeast protein interaction networks obtained from two different databases: DIP and BioGRID. The experimental results of the two networks show that the number of essential proteins predicted by LAC clearly exceeds that explored by degree centrality (DC). More over, LAC is also compared with other seven measures of protein centrality (neighborhood component (DMNC), betweenness centrality (BC), closeness centrality (CC), bottle neck (BN), information centrality (IC), eigenvector centrality (EC), and subgraph centrality (SC)) in identifying essential proteins. The comparison results based on the validations of sensitivity, specificity, F-measure, positive predictive value, negative predictive value, and accuracy consistently show that LAC outweighs these seven previous methods.

MSC:

92C40 Biochemistry, molecular biology
92C37 Cell biology
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Acencio, M. L.; Lemke, N., Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information, BMC Bioinformatics, 10, 290 (2009)
[2] Winzeler, E. A.; Shoemaker, D. D.; Astromoff, A.; Liang, H.; Anderson, K., Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis, Science, 285, 901-906 (1999)
[3] Kamath, R. S.; Fraser, A. G.; Dong, Y.; Poulin, G.; Durbin, R., Systematic functional analysis of the Caenorhabditis elegans genome using RNAi, Nature, 421, 231-237 (2003)
[4] Furney, S. J.; Alba, M. M.; Lopez-Bigas, N., Differences in the evolutionary history of disease genes affected by dominant or recessive mutations, BMC Genomics, 7, July (165) (2006)
[5] Kondrashov, F. A.; Ogurtsov, A. Y.; Kondrashov, A. S., Bioinformatical assay of human gene morbidity, Nucleic Acids Research, 32, March (5), 1731-1737 (2004)
[6] Steinmetz, L. M.; Scharfe, C.; Deutschbauer, A. M.; Mokranjac, D.; Herman, Z. S., Systematic screen for human disease genes in yeast, Nature Gene, August (31), 400-404 (2002)
[7] Becker, S. A.; Palsson, B. O., Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation, BMC Microbiology, 5, 8 (2005)
[8] Lamichhane, G.; Zignol, M.; Blades, N. J.; Geiman, D. E.; Dougherty, A.; Grosset, J.; Broman, K. W.; Bishai, W. R., A postgenomic method for predicting essential genes at subsaturation levels of mutagenesis: application to Mycobacterium tuberculosis, PNAS, 100, 12, 7213-7218 (2003)
[9] Giaever, G.; Chu, A. M.; Ni, L., Functional profiling of the Saccharomyces cerevisiae genome, Nature, 418, 6896, 387-391 (2002)
[10] Cullen, L. M.; Arndt, G. M., Genome-wide screening for gene function using RNAi in mammalian cells, Immunology and Cell Biology, 83, 3, 217-223 (2005)
[11] Roemer, T.; Jiang, B.; Davison, J., Large-scale essential gene identification in Candida albicans and applications to antifungal drug discovery, Molecular Microbiology, 50, 167-181 (2003)
[12] Uetz, P., A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae, Nature, 403, 623-627 (2000)
[13] Gavin, A. C., Proteome survey reveals modularity of the yeast cell machinery, Nature, 440, 7084, 631-636 (2006)
[14] Ho, Y., Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry, Nature, 415, 6868, 180-183 (2002)
[15] Krogan, N. J., Global landscape of protein complexes in the yeast Saccharomyces cerevisiae, Nature, 440, 7084, 637-643 (2006)
[16] Jeong, H.; Mason, S. P.; Barab̈¢si, A. L.; Oltvai, Z. N., Lethality and centrality in protein networks, Nature, 411, 6833, 41-42 (2001)
[17] Yu, H.; Greenbaum, D.; Xin Lu, H.; Zhu, X.; Gerstein, M., Genomic analysis of essentiality within protein networks, Trends in Genetics, 20, 6, 227-231 (2004)
[18] Hahn, M. W.; Kern, A. D., Comparative genomics of centrality and essentiality in three eukaryotic protein interaction networks, Molecular Biology and Evolution, 22, December (4), 803-806 (2004)
[19] Wuchty, S., Interaction and domain networks of yeast, Proteomics, 2, 1715-1723 (2002)
[20] He, X. L.; Zhang, J. Z., Why do hubs tend to be essential in protein networks?, PLoS Genetics, 2, 6, 0826-0834 (2006)
[21] Zotenko, E.; Mestre, J.; O’Leary, D. P.; Przytycka, T. M., Why do hubs in the yeast protein interaction network tend to be essential: reexamining the connection between the network topology and essentiality, PLoS Computational Biology, 4, 8, 1-16 (2008)
[22] Batada, N. N.; Hurst, L. D.; Tyers, M., Evolutionary and physiological importance of hub proteins, PLoS Computational Biology, 2, 7, e88 (2006)
[23] Vallabhajosyula, R.; Chakravarti, D.; Lutfeali, S.; Ray, A.; Raval, A., Identifying hubs in protein interaction networks, Plos One, 4, 4, 1-10 (2009)
[24] Ernesto E., 2005 Virtual identification of essential proteins within the protein interaction network of yeast. http://arxiv.org/abs/q-bio.MN/0505007; Ernesto E., 2005 Virtual identification of essential proteins within the protein interaction network of yeast. http://arxiv.org/abs/q-bio.MN/0505007
[25] Narayanan S. The betweenness centrality of biological networks. Master of Science in Computer Science. Virginia Polytechnic Institute and State University. September 16, 2005.; Narayanan S. The betweenness centrality of biological networks. Master of Science in Computer Science. Virginia Polytechnic Institute and State University. September 16, 2005.
[26] Joy, M., High-betweenness proteins in the yeast protein interaction network, Journal of Biomedicine and Biotechnology, 2, 96-103 (2005)
[27] Wuchty, S.; Stadler, P. F., Centers of complex networks, Journal of Theoretical Biology, 223, 45-53 (2003) · Zbl 1464.92114
[28] Przulj, N.; Wigle, D. A.; Jurisica, I., Functional topology in a network of protein interactions, Bioinformatics, 20, 340-348 (2004)
[29] Yu, H.; Kim, P. M.; Sprecher, E.; Trifonov, V.; Gerstein, M., The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics, PLoS Computional Biology, 3, e59 (2007)
[30] Bonacich, P. F., Power and centrality: a family of measures, American Journal of Sociology, 92, 5, 1170-1182 (1987)
[31] Stevenson, K.; Zelen, M., Rethinking centrality: methods and examples, Social Networks, 11, 1-37 (1989)
[32] Estrada, E.; Rodrguez-Velázquez, J. A., Subgraph centrality in complex networks, Physical Review E, 71, 5 (2005)
[33] Lin, C.-Y.; Chin, C.-H.; Wu, H.-H., Hubba: hub objects analyzer—a framework of interactome hubs identification for network biology, Nucleic Acids Research, 1-6 (2008)
[34] Jacob, R.; Koschtzki, D.; Lehmann, K. A.; Peeters, L.; Tenfelde-Pedehl, D., Algorithms for centrality indices, (Brandes, U.; Erlebach, T., Network Analysis: Methodological Foundations, Volume 3418 of LNCS Tutorial (2005), Springer), 62-82
[35] Mason, O.; Verwoerd, M., Graph theory and networks in biology, IET Systems Biology, 1, 2, 89-119 (2006)
[36] Jeong, H.; Oltvai, Z.; Barabsi, A. L., Prediction of protein essentiality based on genomic data, Complexus, 1, 12, 19-28 (2003)
[37] Dezso, Z.; Oltvai, Z. N.; Barabasi, A. L., Bioinformatics analysis of experimentally determined protein complexes in the yeast Saccharomyces cerevisiae, Genome Research, 13, 11, 2450-2454 (2003)
[38] Hart, G. T.; Lee, I.; Marcotte, M. E., A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality, BMC Bioinformatics, 8, 236 (2007)
[39] Mewes, H. W.; Amid, C.; Arnold, R., MIPS: analysis and annotation of proteins from whole genomes, Nucleic Acids Research, 32, D41-D44 (2004)
[40] SGD. Single Mutant Phenotype(s) for inviable http://www.yeastgenome.org/cgi-bin/phenotype/sbles&phenotype=inviable; SGD. Single Mutant Phenotype(s) for inviable http://www.yeastgenome.org/cgi-bin/phenotype/sbles&phenotype=inviable
[41] Ren, Z.; Yan, L., DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes, Nucleic Acids Research, 37, D455-D458 (2009)
[42] SGDP. Saccharomyces Genome Deletion Project http://www-sequence.stanford.edu/group/yeast_deletion_project/; SGDP. Saccharomyces Genome Deletion Project http://www-sequence.stanford.edu/group/yeast_deletion_project/
[43] Holman, A. G.; Davis, P.; Foster, J. M., Computational prediction of essential genes in an unculturable endosymbiotic bacterium, Wolbachia of Brugia malayi, BMC Microbiology, 9, 243 (2009)
[44] Yu, Y.; Hirsch, J. P., An essential gene pair in Saccharomyces cerevisiae with a potential role in mating, DNA and Cell Biology, 14, 5, 411-418 (1995)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.