Detecting Community Structure in Amino Acid Interaction Networks
In this paper we introduce the notion of protein interaction network. This is a graph whose vertices are the protein-s amino acids and whose edges are the interactions between them. Using a graph theory approach, we observe that according to their structural roles, the nodes interact differently. By leading a community structure detection, we confirm this specific behavior and describe thecommunities composition to finally propose a new approach to fold a protein interaction network.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057125Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1184
 R. Albert, H. Jeong, and A.-L. Barab'asi. The diameter of the world wide web. Nature, 401:130-131, 1999.
 A. R. Atilgan, P. Akan, and C. Baysal. Small-world communication of residues and significance for protein dynamics. Biophys J, 86(1 Pt 1):85-91, January 2004.
 R. Beatson and L. Greengard. A short course on fast multipole methods.
 H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E. Bourne. The protein data bank. Nucleic Acids Research, 28:235-242, 2000.
 C. Branden and J. Tooze. Introduction to protein structure. Garland Publishing, 1999.
 K. V. Brinda and S. Vishveshwara. A network representation of protein structures: implications for protein stability. Biophys J, 89(6):4159- 4170, December 2005.
 A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, and J. Wiener. Graph structure in the Web. Computer Networks, 33(1-6):309-320, 2000.
 Aaron Clauset, M. E. J. Newman, and Cristopher Moore. Finding community structure in very large networks. Physical Review E, 70:066111, 2004.
 N. V. Dokholyan, L. Li, F. Ding, and E. I. Shakhnovich. Topological determinants of protein folding. Proc Natl Acad Sci U S A, 99(13):8637- 8641, June 2002.
 Thomas M. J. Fruchterman and Edward M. Reingold. Graph drawing by force-directed placement. Software - Practice and Experience, 21(11):1129-1164, 1991.
 O. Gaci and S. Balev. Hubs identification in amino acids interaction networks. In Proceedings of the 7th ACS/IEEE International Conference on Computer Systems and Applications, 2009. 7 pages.
 O. Gaci and S. Balev. The small-world model for amino acid interaction networks. In Proceedings of the IEEE AINA 2009, workshop on Bioinformatics and Life Science Modeling and Computing, 2009. 6 pages.
 A. Ghosh, K. V. Brinda, and S. Vishveshwara. Dynamics of lysozyme structure network: probing the process of unfolding. Biophys J, 92(7):2523-2535, April 2007.
 L. Greengard and V. Rokhlin. A fast algorithm for particle simulations. J. Comput. Phys., 73(2):325-348, 1987.
 H. Jeong, B. Tombor, R. Albert, Z. N. Oltvai, and A.-L. Barab'asi. The large-scale organization of metabolic networks. Nature, 407(6804):651- 654, October 2000.
 U. K. Muppirala and Z. Li. A simple approach for protein structure discrimination based on the network pattern of conserved hydrophobic residues. Protein Eng Des Sel, 19(6):265-275, June 2006.
 M. E. J. Newman. Fast algorithm for detecting community structure in networks. Physical Review E, 69:066133, 2004.
 John P. Scott. Social Network Analysis: A Handbook. SAGE Publications, January 2000.
 S. Wasserman and K. Faust. Social network analysis : methods and applications , volume 8 of Structural analysis in the social sciences. Cambridge University Press, Cambridge, 1994.
 D. J. Watts and S. H. Strogatz. Collective dynamics of -small-world- networks. Nature., 393:440-442, 1998.
 Bo Yang and Da You Liu. Incremental algorithm for detecting community structure in dynamic networks. In proceedings of the 4th International Conference on Machine Learning and Cybernetics, pages 2284-2290, August 2005.