e-ISSN 2231-8526
ISSN 0128-7680
Soheir Noori, Nabeel Al-A’araji and Eman Al-Shamery
Pertanika Journal of Science & Technology, Volume 29, Issue 2, April 2021
DOI: https://doi.org/10.47836/pjst.29.2.35
Keywords: Common neighbours; density; protein complex; protein–protein interaction network; topological structure
Published on: 30 April 2021
Defining protein complexes by analysing the protein–protein interaction (PPI) networks is a crucial task in understanding the principles of a biological cell. In the last few decades, researchers have proposed numerous methods to explore the topological structure of a PPI network to detect dense protein complexes. In this paper, the overlapping protein complexes with different densities are predicted within an acceptable execution time using seed expanding model and topological structure of the PPI network (SETS). SETS depend on the relation between the seed and its neighbours. The algorithm was compared with six algorithms on six datasets: five for yeast and one for human. The results showed that SETS outperformed other algorithms in terms of F-measure, coverage rate and the number of complexes that have high similarity with real complexes.
Adamcsek, B., Palla, G., Farkas, I. J., Derényi, I., & Vicsek, T. (2006). CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics, 22(8), 1021-1023. https://doi.org/10.1093/bioinformatics/btl039
Aloy, P., Böttcher, B., Ceulemans, H., Leutwein, C., Mellwig, C., Fischer, S., Gavin, A.-C., Bork, P., Superti-Furga, G., & Serrano, L. (2004). Structure-based assembly of protein complexes in yeast. Science, 303(5666), 2026-2029. https://doi.org/10.1126/science.1092645
Altaf-Ul-Amin, M., Shinbo, Y., Mihara, K., Kurokawa, K., & Kanaya, S. (2006). Development and implementation of an algorithm for detection of protein complexes in large interaction networks. BMC Bioinformatics, 7, Article 207. https://doi.org/10.1186/1471-2105-7-207
Bader, G. D., & Hogue, C. W. (2003). An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 4, Article 2. https://doi.org/10.1186/1471-2105-4-2
Brohée, S., & van Helden, J. (2006). Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics, 7, Article 488. https://doi.org/10.1186/1471-2105-7-488
Feng, J., Jiang, R., & Jiang, T. (2010). A max-flow-based approach to the identification of protein complexes using protein interaction and microarray data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(3), 621-634. https://doi.org/10.1109/TCBB.2010.78
Friedel, C. C., Krumsiek, J., & Zimmer, R. (2008). Bootstrapping the interactome: unsupervised identification of protein complexes in yeast. In M. Vingron & L. Wong (Eds.), Lecture notes in computer science: Research in computational molecular biology (Vol. 4955, pp. 3-16). Springer. https://doi.org/10.1007/978-3-540-78839-3_2.
Goldberg, D. S., & Roth, F. P. (2003). Assessing experimentally derived interactions in a small world. Proceedings of the National Academy of Sciences, USA, 100(8), 4372-4376. https://doi.org/10.1073/pnas.0735871100
Hartwell, L. H., Hopfield, J. J., Leibler, S., & Murray, A. W. (1999). From molecular to modular cell biology. Nature, 402(6761), C47-C52. https://doi.org/10.1038/35011540
Jiang, P., & Singh, M. (2010). SPICi: A fast clustering algorithm for large biological networks. Bioinformatics, 26(8), 1105-1111. https://doi.org/10.1093/bioinformatics/btq078
Krogan, N. J., Cagney, G., Yu, H., Zhong, G., Guo, X., Ignatchenko, A., Li, J., Pu, S., Datta, N., & Tikuisis, A. P. (2006). Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature, 440(7084), 637-643. https://doi.org/10.1038/nature04670
Li, M., Chen, J. E., Wang, J. X., Hu, B., & Chen, G. (2008). Modifying the DPClus algorithm for identifying protein complexes based on new topological structures. BMC Bioinformatics, 9, Article 398. https://doi.org/10.1186/1471-2105-9-398
Li, M., Chen, W., Wang, J., Wu, F. X., & Pan, Y. (2014). Identifying dynamic protein complexes based on gene expression profiles and PPI networks. BioMed Research International, 2014, Article 375262. https://doi.org/10.1155/2014/375262
Li, X. L., Foo, C. S., Tan, S. H., & Ng, S. K. (2005). Interaction graph mining for protein complexes using local clique merging. Genome Informatics, 16(2), 260-269. https://doi.org/10.11234/gi1990.16.2_260
Liu, G., Wong, L., & Chua, H. N. (2009). Complex discovery from weighted PPI networks. Bioinformatics, 25(15), 1891-1897. https://doi.org/10.1093/bioinformatics/btp311
Liu, G., Yong, C. H., Wong, L., & Chua, H. N. (2010, December 18-21 ). Decomposing PPI networks for complex discovery [Paper presentation]. 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Hong Kong, China. https://doi.org/10.1109/BIBM.2010.5706577.
Ma, C. Y., Chen, Y. P. P., Berger, B., & Liao, C. S. (2017). Identification of protein complexes by integrating multiple alignment of protein interaction networks. Bioinformatics, 33(11), 1681-1688. https://doi.org/10.1093/bioinformatics/btx043
Maraziotis, I. A., Dimitrakopoulou, K., & Bezerianos, A. (2007). Growing functional modules from a seed protein via integration of protein interaction and gene expression data. BMC Bioinformatics, 8, Article 408. https://doi.org/10.1186/1471-2105-8-408
Mewes, H. W., Amid, C., Arnold, R., Frishman, D., Güldener, U., Mannhaupt, G., Münsterkötter, M., Pagel, P., Strack, N., & Stümpflen, V. (2004). MIPS: Analysis and annotation of proteins from whole genomes. Nucleic Acids Research, 32(suppl_1), D41-D44. https://doi.org/10.1093/nar/gkh092
Nepusz, T., Yu, H., & Paccanaro, A. (2012). Detecting overlapping protein complexes in protein-protein interaction networks. Nature Methods, 9(5), 471-472. https://doi.org/10.1038/nmeth.1938
Palla, G., Derényi, I., Farkas, I., & Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043), 814-818. https://doi.org/10.1038/nature03607
Peng, X., Wang, J., Peng, W., Wu, F. X., & Pan, Y. (2017). Protein–protein interactions: Detection, reliability assessment and applications. Briefings in Bioinformatics, 18(5), 798-819. https://doi.org/10.1093/bib/bbw066
Pizzuti, C., & Rombo, S. E. (2014). Algorithms and tools for protein–protein interaction networks clustering, with a special focus on population-based stochastic methods. Bioinformatics, 30(10), 1343-1352. https://doi.org/10.1093/bioinformatics/btu034
Pu, S., Wong, J., Turner, B., Cho, E., & Wodak, S. J. (2009). Up-to-date catalogues of yeast protein complexes. Nucleic Acids Research, 37(3), 825-831. https://doi.org/10.1093/nar/gkn1005
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004). Defining and identifying communities in networks. Proceedings of the National Academy of Sciences, USA, 101(9), 2658-2663. https://doi.org/10.1073/pnas.0400054101
Rives, A. W., & Galitski, T. (2003). Modular organization of cellular networks. Proceedings of the National Academy of Sciences, USA, 100(3), 1128-1133. https://doi.org/10.1073/pnas.0237338100
Schlicker, A., Domingues, F. S., Rahnenführer, J., & Lengauer, T. (2006). A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinformatics, 7, Article 302. https://doi.org/10.1186/1471-2105-7-302
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498-2504. https://doi.org/10.1101/gr.1239303
Tadaka, S., & Kinoshita, K. (2016). NCMine: Core-peripheral based functional module detection using near-clique mining. Bioinformatics, 32(22), 3454-3460. https://doi.org/10.1093/bioinformatics/btw488
Van Dongen, S. M. (2000). Graph clustering by flow simulation [Doctoral dissertation, Utrecht University]. Utrecht University Publication. https://dspace.library.uu.nl/bitstream/handle/1874/848/full.pdf?sequence=1&isAllowed=y.
Wang, J., Liu, B., Li, M., & Pan, Y. (2010). Identifying protein complexes from interaction networks based on clique percolation and distance restriction. BMC Genomics, 11, Article S10. https://doi.org/10.1186/1471-2164-11-S2-S10
Wang, R., Liu, G., Wang, C., Su, L., & Sun, L. (2018). Predicting overlapping protein complexes based on core-attachment and a local modularity structure. BMC Bioinformatics, 19, Article 305. https://doi.org/10.1186/s12859-018-2309-9
Wang, Y., You, Z., Li, X., Chen, X., Jiang, T., & Zhang, J. (2017). PCVMZM: Using the probabilistic classification vector machines model combined with a zernike moments descriptor to predict protein–protein interactions from protein sequences. International Journal of Molecular Sciences, 18(5), Article 1029. https://doi.org/10.3390/ijms18051029
Xenarios, I., Salwinski, L., Duan, X. J., Higney, P., Kim, S. M., & Eisenberg, D. (2002). DIP, the database of interacting proteins: A research tool for studying cellular networks of protein interactions. Nucleic Acids Research, 30(1), 303-305. https://doi.org/10.1093/nar/30.1.303
Zaki, N., Efimov, D., & Berengueres, J. (2013). Protein complex detection using interaction reliability assessment and weighted clustering coefficient. BMC Bioinformatics, 14, Article 163. https://doi.org/10.1186/1471-2105-14-163
Zhao, J., & Lei, X. (2019). Detecting overlapping protein complexes in weighted PPI network based on overlay network chain in quotient space. BMC Bioinformatics, 20, Article 682. https://doi.org/10.1186/s12859-019-3256-9
ISSN 0128-7680
e-ISSN 2231-8526