Function Analysis of Human Protein Interactions Based on a Novel Minimal Loop Algorithm

Author(s): Mingyang Jiang, Zhili Pei*, Xiaojing Fan, Jingqing Jiang, Qinghu Wang, Zhifeng Zhang

Journal Name: Current Bioinformatics

Volume 14 , Issue 2 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Background: Various properties of Protein-Protein Interaction (PPI) network have been widely exploited to discover the topological organizing principle and the crucial function motifs involving specific biological pathway or disease process. The current motifs of PPI network are either detected by the topology-based coarse grain algorithms, i.e. community discovering, or depended on the limited-accessible protein annotation data derived precise algorithms. However, the identified network motifs are hardly compatible with the well-defined biological functions according to those two types of methods.

Method: In this paper, we proposed a minimal protein loop finding method to explore the elementary structural motifs of human PPI network. Initially, an improved article exchange model was designed to search all the independent shortest protein loops of PPI network. Furthermore, Gene Ontology (GO) based function clustering analysis was implemented to identify the biological functions of the shortest protein loops. Additionally, the disease process associated shortest protein loops were considered as the potential drug targets.

Result: Our proposed method presents the lowest computational complexity and the highest functional consistency, compared to the three other methods. The functional enrichment and clustering analysis for the identified minimal protein loops revealed the high correlation between the protein loops and the corresponding biological functions, particularly, statistical analysis presenting the protein loops with the length less than 4 is closely connected with some disease process, suggesting the potential drug target.

Conclusion: Our minimal protein loop method provides a novel manner to precisely define the functional motif of PPI network, which extends the current knowledge about the cooperating mechanisms and topological properties of protein modules composed of the short loops.

Keywords: Human protein-protein interaction network, minimal protein loop, function enrichment analysis, network clustering, disease process, article exchange model.

[1]
Vidal M. A unifying view of 21st century systems biology. FEBS Lett 2009; 583: 3891-4.
[2]
Tan K, Shlomi T, Feizi H, Ideker T, Sharan R. Transcriptional regulation of protein complexes within and across species. Proc Natl Acad Sci USA 2007; 104: 1283-8.
[3]
Liang Z, Xu M, Teng M, Niu L. Comparison of protein interaction networks reveals species conservation and divergence. BMC Bioinformatics 2006; 7: 457.
[4]
Li G, Cao H, Xu Y. Structural and functional analyses of microbial metabolic networks reveal novel insights into genome-scale metabolic fluxes. Brief Bioinform 2018.
[http://dx.doi.org/10.1093/bib/bby022]
[5]
Dill K. Biochemistry (Mathews, Christopher K.; van Holde, K.E.). J Chem Educ 1991; 68: A21.
[6]
Ideker T, Sharan R. Protein networks in disease. Genome Res 2008; 18: 644-52.
[7]
Vidal M, Cusick ME, Barabási AL. Interactome networks and human disease. Cell 144: 986-98.
[8]
Cheng F, Liu C, Jiang J, et al. Prediction of drug-target interactions and drug repositioning via network-based inference. PLOS Comput Biol 2012; 8: e1002503.
[9]
Alaimo S, Pulvirenti A, Giugno R, Ferro A. Drug–target interaction prediction through domain-tuned network-based inference. Bioinformatics 2013; 29: 2004-8.
[10]
Ge H, Walhout AJ, Vidal M. Integrating ‘omic’ information: a bridge between genomics and systems biology. Trends Genet 2003; 19: 551-60.
[11]
Yu H, Braun P, Yıldırım MA, et al. High-quality binary protein interaction map of the yeast interactome network. Science 2008; 322: 104-10.
[12]
He X, Zhang J. Why do hubs tend to be essential in protein networks? PLoS Genet 2006; 2: e88.
[13]
Dreze M, Monachello D, Lurin C, et al. Chapter 12 - High-Quality Binary Interactome Mapping. In Methods in Enzymology. Volume 470: Academic Press; 2010; pp: 281-315.
[14]
Charbonnier S, Gallego O, Gavin AC. The social network of a cell: Recent advances in interactome mapping. In Biotechnology Annual Review. Volume 14. Edited by El-Gewely MR: Elsevier; 2008; pp: 1-28.
[15]
Snider J, Kotlyar M, Saraon P, et al. Fundamentals of protein interaction network mapping. Mol Syst Biol 2015; 11: 848.
[16]
Rain JC, Selig L, De Reuse H, et al. The protein–protein interaction map of Helicobacter pylori. Nature 2001; 409: 211.
[17]
Ito T, Chiba T, Ozawa R, et al. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci USA 2001; 98: 4569-74.
[18]
Uetz P, Giot L, Cagney G, et al. A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature 2000; 403: 623.
[19]
Li S, Armstrong CM, Bertin N, et al. A Map of the Interactome Network of the Metazoan C. elegans. Science 2004; 303: 540-3.
[20]
Giot L, Bader JS, Brouwer C, et al. A protein interaction map of Drosophila melanogaster. Science 2003; 302: 1727-36.
[21]
Li T, Wernersson R, Hansen RB, et al. A scored human protein–protein interaction network to catalyze genomic interpretation. Nat Methods 2016; 14: 61.
[22]
Goehler H, Lalowski M, Stelzl U, et al. A protein interaction network links GIT1, an enhancer of huntingtin aggregation, to Huntington’s disease. Mol Cell 2004; 15: 853-65.
[23]
Colland F, Jacq X, Trouplin V, et al. Functional proteomics mapping of a human signaling pathway. Genome Res 2004; 14: 1324-32.
[24]
Keshava Prasad TS, Goel R, Kandasamy K, et al. Human protein reference database-2009 update. Nucleic Acids Res 2009; 37: D767-72.
[25]
Kandasamy K, Mohan SS, Raju R, et al. NetPath: a public resource of curated signal transduction pathways. Genome Biol 2010; 11: R3.
[26]
Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 2015; 43: D447-52.
[27]
Chatr-aryamontri A, Oughtred R, Boucher L, et al. The BioGRID interaction database: 2017 update. Nucleic Acids Res 2017; 45: D369-79.
[28]
Chung SS, Pandini A, Annibale A, et al. Bridging topological and functional information in protein interaction networks by short loops profiling. Sci Rep 2015; 5: 8540.
[29]
Newman M. Networks: An Introduction. Oxford University Press, Inc. 2010.
[30]
Lancichinetti A, Fortunato S. Community detection algorithms: A comparative analysis. P Phys Rev E Stat Nonlin Soft Matter Phys 2009; 80: 056117.
[31]
Wiuf C. Algebraic Statistics and Methods in Systems Biology.In Handbook of Statistical Systems Biology. John Wiley & Sons, Ltd 2011; pp. 114-32.
[32]
Royer L, Reimann M, Andreopoulos B, Schroeder M. Unraveling protein networks with power graph analysis. PLOS Comput Biol 2008; 4: e1000108.
[33]
Alon U. Network motifs: theory and experimental approaches. Nat Rev Genet 2007; 8: 450.
[34]
Jiancang Z, Dapeng L, Yunfeng W, Quan Z, Xiangrong L. An empirical study of features fusion techniques for protein-protein interaction prediction. Curr Bioinform 2016; 11: 4-12.
[35]
Leyi W, Quan Z, Minghong L, Huijuan L, Yuming Z. A novel machine learning method for cytokine-receptor interaction prediction. Comb Chem High Throughput Screen 2016; 19: 144-52.
[36]
Sánchez I, Mahlke C, Yuan J. Pivotal role of oligomerization in expanded polyglutamine neurodegenerative disorders. Nature 2003; 421: 373.
[37]
Jiao X, Sherman BT, Huang DW, et al. DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics 2012; 28: 1805-6.
[38]
Yu LWC, Guo L, Liang Y, Yang X. Article exchange model and its algorithm. J Jilin University 2010; 48: 653-7.
[39]
Zhang XF, Ou-Yang L, Zhu Y, Wu MY, Dai DQ. Determining minimum set of driver nodes in protein-protein interaction networks. BMC Bioinformatics 2015; 16: 146.
[40]
Serrour B, Arenas A, Gómez S. Detecting communities of triangles in complex networks using spectral optimization. Comput Commun 2011; 34: 629-34.
[41]
Newman MEJ. Detecting community structure in networks. Eur Phys J B 2004; 38: 321-30.
[42]
Mistry M, Pavlidis P. Gene ontology term overlap as a measure of gene functional similarity. BMC Bioinformatics 2008; 9: 327.
[43]
Newman ME. Fast algorithm for detecting community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys 2004; 69: 066133.
[44]
Bhati M, Lee C, Nancarrow AL, et al. Implementing the LIM code: the structural basis for cell type‐specific assembly of LIM‐homeodomain complexes. EMBO J 2008; 27: 2018-29.
[45]
De Souza EB. Corticotropin-releasing factor receptors: Physiology, pharmacology, biochemistry and role in central nervous system and immune disorders. Psychoneuroendocrinology 1995; 20: 789-819.
[46]
Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2008; 4: 44.
[47]
Cohen J. A Coefficient of Agreement for Nominal Scales. Educ Psychol Meas 1960; 20: 37-46.
[48]
Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 2005; 33: D514-7.
[49]
Bolden JE, Peart MJ, Johnstone RW. Anticancer activities of histone deacetylase inhibitors. Nat Rev Drug Discov 2006; 5: 769.
[50]
Francoz S, Froment P, Bogaerts S, et al. Mdm4 and Mdm2 cooperate to inhibit p53 activity in proliferating and quiescent cells in vivo. Proc Natl Acad Sci USA 2006; 103: 3232-7.
[51]
Jiang PH, Motoo Y, Garcia S, et al. Down-expression of tumor protein p53-induced nuclear protein 1 in human gastric cancer. World J Gastroenterol 2006; 12: 691-6.
[52]
Sankalecha TH, Gupta SJ, Gaikwad NR, Shirole NU, Kothari HG. Yield of p53 expression in esophageal squamous cell cancer and its relationship with survival. Saudi J Gastroenterol 2017; 23: 281-6.
[53]
Ashburner M, Ball CA, Blake JA, et al. Gene Ontology: tool for the unification of biology. Nat Genet 2000; 25: 25.
[54]
Beutler B, Hoebe K, Du X, Ulevitch RJ. How we detect microbes and respond to them: the Toll-like receptors and their transducers. J Leukoc Biol 2003; 74: 479-85.
[55]
Wiencek JR, Na M, Hirbawi J, Kalafatis M. Amino Acid Region 1000–1008 of Factor V Is a Dynamic Regulator for the Emergence of Procoagulant Activity. J Biol Chem 2013; 288: 37026-38.
[56]
Esmon CT, Vigano-D’Angelo S, D’Angelo A, Comp PC. Anticoagulation Proteins C and S. In The New Dimensions of Warfarin Prophylaxis. Edited by Wessler S, Becker CG, Nemerson Y. Boston, MA: Springer US; 1987; pp: 47-54..


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 14
ISSUE: 2
Year: 2019
Page: [164 - 173]
Pages: 10
DOI: 10.2174/1574893613666180906103946
Price: $65

Article Metrics

PDF: 35
HTML: 3
PRC: 1