An Ontology based Method for Identifying Highly Interacted Human Protein Complexes and its Pathways
Discovering highly interacting proteins and its pathways is a challenge for computational biologists. Identifying interactions among proteins and its pathways has been found to be useful for drug development. For example, Arabidopsis thaliana has 549 annotated metabolic pathways and a few thousand biological processes as defined with gene ontology terms, but the regulators for most of these pathways have not yet been revealed (1–3). With the advent of the whole-genome approach and the explosion of biological data in public repositories, demands have heightened for computational algorithms that can be used to predict pathway regulators using high-throughput gene expression datasets.
In our study, we collected data of 40,788 protein - protein interactions from HPRD and IntAct. This pooled off data was loaded into cytoscape (version 2.63) to visualize the human interactome network through the grid layout method. By using Connected Component (CC) algorithm, the largest and the highly connected human network were found. From 36,945 binary protein – protein interactions, 89 highly connected modules were selected, which are of high score value. The total numbers of proteins in each of the 89 modules were found out. The proteins from all the 89 clusters were classified into 2 categories - normal and diseased pathways. From all these 89 clusters, 1350 proteins were obtained and were unique, of which 374 proteins are in the normal pathways and 976 proteins in diseased pathways. But 976 proteins were found in both normal and diseased pathways. Further computational studies can help to understand the changes that occur in the proteins to become in the diseased pathways.
Aittokallio, Tero, and Benno Schwikowski. "Graph-based methods for analysing networks in cell biology." Briefings in bioinformatics 7.3 (2006): 243-255.
Aldridge BB, Burke JM, Lauffenburger DA, Sorger PK (2006) Physicochemical modelling of cell signalling pathways. Nat Cell Biol, 8:1195–1203.
Bader GD, Cary MP, Sander C, (2006) Pathguide a pathway resource list. Nucleic Acids Res, 34:D504–506.
Bonetta L: Protein-protein interactions: Interactome under construction. Nature 2010, 68:851–854.
Bowen NJ, Walker LD, Matyunina LV, et al (2009). Gene expression profiling supports the hypothesis that human ovarian surface epithelia are multipotent and capable of serving as ovarian cancer initiating cells. BMC Med Genomics, 2, 71.
Brown, K, R. Otasek, D., Ali, M., Mc Guffin, M, J., Xie, W, Devani, B, Toch, I, L. and Jurisica, I. (2009). NAViGaTOR: Network Analysis, Visualization and Graphing Toronto. Bioinformatics. 25:3327–3329.
Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O, Anwar N, Schultz N,Bader GD, Sander C (2011), Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res, 39:D685–690.
Dannenfelser, R, Lachmann, A., Szenk, M., and Ma'ayan, A. (2011). FNV: Light-weight Flash-based network and pathway viewer. Bioinformatics. 27:1181–1182.
Dutkowski, J. and Ideker, T. (2011). Protein networks as logic functions in development and cancer. PLoS Comput Biol.; 7:e1002180. doi: 10.1371/journal.pcbi.1002180.
Franceschini, Andrea, et al. "STRING v9. 1: protein-protein interaction networks, with increased coverage and integration." Nucleic acids research 41.D1 (2012): D808-D815.
Francesconi M, Remondini D, Neretti N, et al (2008). Reconstructing networks of pathways via significance analysis of their intersections. BMC Bioinformatics, 9 Suppl 4, S9.
Gambette, P. and Huson, D., H. (2008). Improved layout of phylogenetic networks. IEEE/ACM Trans Comput Biol Bioinform.; 5:472–479.
Gavin A.C., Bosche, M., Krause, R., Grandi, P., Marzioch, M., Bauer, A., Schultz, J., Rick, J, M., Michon, A, M., Cruciat, C, M. et al. (2002). Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature. 415, 141–147.
Gisler, Serge M., et al. "Monitoring protein-protein interactions between the mammalian integral membrane transporters and PDZ-interacting partners using a modified split-ubiquitin membrane yeast two-hybrid system." Molecular & cellular proteomics 7.7 (2008): 1362-1377..
He, S., Mei, J., Shi, G., Wang, Z. and Li, W. (2010). LucidDraw: efficiently visualizing complex biochemical networks within MATLAB. BMC Bioinformatics. 11:31.
Hosoyama, N. Nasimul, N. and Iba, H. (2003). Layout search of a gene regulatory network for 3-D visualization. Genome Inform. 14:103–113.
Ideker, T. and Sharan, R. (2008). Protein networks in disease. Genome Res.; 18:644–52.
Johannes Tuikkala, Heidi Vähämaa, Pekka Salmela, Olli S Nevalainen. and Tero Aittokallio. (2012). A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization. BMC.Bioinformatics. 5: 2.
Kerrien S, Alam-Faruque Y, Aranda B, Bancarz I, Bridge A, Derow C, Dimmer E, Feuermann M, Friedrichsen A, Huntley R, Kohler C, Khadake J, Leroy C, Liban A, Lieftink C, Montecchi-Palazzi L, Orchard S, Risse J, Robbe K, Roechert B, Thorneycroft D, Zhang Y, Apweiler R, Hermjakob H. (2007). IntAct open source resource for molecular interaction data. Nucleic Acids Res, 35:D561-265.
Keshava Prasad TS, Goel R, Kandasamy K, et al (2009). Human Protein Reference Database--2009 update. Nucleic Acids Res, 37, D767-72.
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