Abstract
Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network’s structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yanagida M (2002) Functional proteomics; current achievements. J Chromatogr B 771(1–2):89–106
Berggård T, Linse S, James P (2007) Methods for the detection and analysis of protein–protein interactions. Proteomics 7(16):2833–2842
Nooren IM, Thornton JM (2003) Diversity of protein–protein interactions. EMBO J 22(14):3486–3492
Jones S, Thornton JM (1996) Principles of protein-protein interactions. Proc Natl Acad Sci 93(1):13–20
Phizicky EM, Fields S (1995) Protein-protein interactions: methods for detection and analysis. Microbiol Rev 59(1):94–123
Prelich G, Tan CK, Kostura M, Mathews MB, So AG, Downey KM, Stillman B (1987) Functional identity of proliferating cell nuclear antigen and a DNA polymerase-δ auxiliary protein. Nature 326(6112):517–520
Srere PA (1987) Complexes of sequential metabolic enzymes. Annu Rev Biochem 56(1):89–124
Vincent JP, Lazdunski M (1972) Trypsin-pancreatic trypsin inhibitor association. Dynamics of the interaction and role of disulfide bridges. Biochemistry 11(16):2967–2977
Weber J, Lee RS, Wilke-Mounts S, Grell E, Senior AE (1993) Combined application of site-directed mutagenesis, 2-azido-ATP labeling, and lin-benzo-ATP binding to study the noncatalytic sites of Escherichia coli F1-ATPase. J Biol Chem 268(9):6241–6247
Hill RL, Brew K (1975) Lactose synthetase. Adv Enzvmol Relat Areas Mol Biol 43:411–490
Podobnik M, Kraševec N, Zavec AB, Naneh O, Flašker A, Caserman S, Hodnik V, Anderluh G (2016) How to study protein-protein interactions. Acta Chim Slov 63(3):424–439
Rao VS, Srinivas K, Sujini GN, Kumar GN (2014) Protein-protein interaction detection: methods and analysis. Int J Proteomics 2014:147648
Xing S, Wallmeroth N, Berendzen KW, Grefen C (2016) Techniques for the analysis of protein-protein interactions in vivo. Plant Physiol 171(2):727–758
Lehne B, Schlitt T (2009) Protein-protein interaction databases: keeping up with growing interactomes. Hum Genomics 3:1–7
Boeri Erba E, Petosa C (2015) The emerging role of native mass spectrometry in characterizing the structure and dynamics of macromolecular complexes. Protein Sci 24(8):1176–1192
Rigaut G, Shevchenko A, Rutz B, Wilm M, Mann M, Séraphin B (1999) A generic protein purification method for protein complex characterization and proteome exploration. Nat Biotechnol 17(10):1030–1032
Kobe B, Guncar G, Buchholz R, Huber T, Maco B, Cowieson N, Martin JL, Marfori M, Forwood JK (2008) Crystallography and protein–protein interactions: biological interfaces and crystal contacts. Biochem Soc Trans 36(6):1438–1441
Zuiderweg ER (2002) Mapping protein− protein interactions in solution by NMR spectroscopy. Biochemistry 41(1):1–7
Vinogradova O, Qin J (2012) NMR as a unique tool in assessment and complex determination of weak protein–protein interactions. In: NMR of proteins and small biomolecules. Springer, Berlin, pp 35–45
Grigoriev A (2001) A relationship between gene expression and protein interactions on the proteome scale: analysis of the bacteriophage T7 and the yeast Saccharomyces cerevisiae. Nucleic Acids Res 29(17):3513–3519
Remy I, Campbell-Valois FX, Michnick SW (2007) Detection of protein–protein interactions using a simple survival protein-fragment complementation assay based on the enzyme dihydrofolate reductase. Nat Protoc 2(9):2120–2125
Ooi SL, Pan X, Peyser BD, Ye P, Meluh PB, Yuan DS, Irizarry RA, Bader JS, Spencer FA, Boeke JD (2006) Global synthetic-lethality analysis and yeast functional profiling. Trends Genet 22(1):56–63
Fields S, Song OK (1989) A novel genetic system to detect protein–protein interactions. Nature 340(6230):245–246
Overbeek R, Fonstein M, D’Souza M, Pusch GD, Maltsev N (1999) The use of gene clusters to infer functional coupling. Proc Natl Acad Sci U S A 96:2896–2901
Juan D, Pazos F, Valencia A (2008) High-confidence prediction of global interactomes based on genome-wide coevolutionary networks. Proc Natl Acad Sci 105(3):934–939
Goh CS, Cohen FE (2002) Co-evolutionary analysis reveals insights into protein–protein interactions. J Mol Biol 324(1):177–192
Pazes F, Valencia A (2001) Similarity of phylogenetic trees as indicator of protein-protein interaction [J]. Protein Eng 14(9):609–614
Yamada M, Kabir MS, Tsunedomi R (2004) Divergent promoter organization may be a preferred structure for gene control in Escherichia coli. Microbial Physiol 6(3–4):206–210
Lee SA, Chan CH, Tsai CH, Lai JM, Wang FS, Kao CY, Huang CY (2008) Ortholog-based protein-protein interaction prediction and its application to inter-species interactions. BMC Bioinformatics 9:1–9
Memišević V, Wallqvist A, Reifman J (2013) Reconstituting protein interaction networks using parameter-dependent domain-domain interactions. BMC Bioinformatics 14(1):1–5
Pazos F, Valencia A (2002) In silico two-hybrid system for the selection of physically interacting protein pairs. Proteins 47(2):219–227
Marcotte EM, Pellegrini M, Ng HL, Rice DW, Yeates TO, Eisenberg D (1999) Detecting protein function and protein-protein interactions from genome sequences. Science 285(5428):751–753
Tsoka S, Ouzounis CA (2000) Prediction of protein interactions: metabolic enzymes are frequently involved in gene fusion. Nat Genet 26(2):141–142
Enright AJ, Iliopoulos I, Kyrpides NC, Ouzounis CA (1999) Protein interaction maps for complete genomes based on gene fusion events. Nature 402(6757):86–90
Bock JR, Gough DA (2001) Predicting protein–protein interactions from primary structure. Bioinformatics 17(5):455–460
Sprinzak E, Margalit H (2001) Correlated sequence-signatures as markers of protein-protein interaction. J Mol Biol 311(4):681–692
Li BQ, Feng KY, Chen L, Huang T, Cai YD (2012) Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS. PLoS One 7(8):1–10
Zhou HX, Shan Y (2001) Prediction of protein interaction sites from sequence profile and residue neighbor list. Proteins 44(3):336–343
Barman RK, Saha S, Das S (2014) Prediction of interactions between viral and host proteins using supervised machine learning methods. PLoS One 9(11):e112034
Sarkar D, Jana T, Saha S (2018) LMDIPred: a web-server for prediction of linear peptide sequences binding to SH3, WW and PDZ domains. PLoS One 13(7):e0200430
Du T, Liao L, Wu CH, Sun B (2016) Prediction of residue-residue contact matrix for protein-protein interaction with fisher score features and deep learning. Methods 110:97–105
You ZH, Lei YK, Zhu L, Xia J, Wang B (2013) Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC Bioinformatics 14:1–11
Xu B, Guan J (2014) From function to interaction: a new paradigm for accurately predicting protein complexes based on protein-to-protein interaction networks. IEEE/ACM Trans Comput Biol Bioinform 11(4):616–627
Xenarios I, Fernandez E, Salwinski L, Duan XJ, Thompson MJ, Marcotte EM, Eisenberg D (2001) DIP: the database of interacting proteins: 2001 update. Nucleic Acids Res 29(1):239–241
Hermjakob H, Montecchi-Palazzi L, Lewington C, Mudali S, Kerrien S, Orchard S, Vingron M, Roechert B, Roepstorff P, Valencia A, Margalit H (2004) IntAct: an open source molecular interaction database. Nucleic Acids Res 32(suppl 1):D452–D455
Shafreen B, Venugopal A (2009) Human protein reference database2009 update. Nucleic Acids Res 37:D767–D772
Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34(suppl 1):D535–D539
Chatr-aryamontri A, Kerrien S, Khadake J et al (2008) MINT and IntAct contribute to the second BioCreative challenge: serving the text-mining community with high quality molecular interaction data. Genome Biol 9(Suppl 2):S5
Turner B, Razick S, Turinsky AL, Vlasblom J, Crowdy EK, Cho E, Morrison K, Donaldson IM, Wodak SJ (2010) iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence. Database (Oxford) 2010:1–5
Giurgiu M, Reinhard J, Brauner B, Dunger-Kaltenbach I, Fobo G, Frishman G, Montrone C, Ruepp A (2019) CORUM: the comprehensive resource of mammalian protein complexes—2019. Nucleic Acids Res 47(D1):D559–D563
Bader GD, Donaldson I, Wolting C, Ouellette BF, Pawson T, Hogue CW (2001) BIND—the biomolecular interaction network database. Nucleic Acids Res 29(1):242–245
Güldener U, Münsterkötter M, Oesterheld M, Pagel P, Ruepp A, Mewes HW, Stümpflen V (2006) MPact: the MIPS protein interaction resource on yeast. Nucleic Acids Res 34(suppl 1):D436–D441
Pagel P, Kovac S, Oesterheld M, Brauner B, Dunger-Kaltenbach I, Frishman G, Montrone C, Mark P, Stümpflen V, Mewes HW, Ruepp A (2005) The MIPS mammalian protein–protein interaction database. Bioinformatics 21(6):832–834
Brown KR, Jurisica I (2005) Online predicted human interaction database. Bioinformatics 21(9):2076–2082
Alanis-Lobato G, Andrade-Navarro MA, Schaefer MH (2016) HIPPIE v2. 0: enhancing meaningfulness and reliability of protein–protein interaction networks. Nucleic Acids Res. https://doi.org/10.1093/nar/gkw985
Calderone A, Castagnoli L, Cesareni G (2013) Mentha: a resource for browsing integrated protein-interaction networks. Nat Methods 10(8):690–691
Patil A, Nakai K, Nakamura H (2011) HitPredict: a database of quality assessed protein–protein interactions in nine species. Nucleic Acids Res 39(suppl 1):D744–D749
Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P, Jensen LJ, von Mering C (2021) The STRING database in 2021: customizable protein-protein networks, and functional 475 characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 49:D605–D612
Kotlyar M, Pastrello C, Sheahan N, Jurisica I (2016) Integrated interactions database: tissue-specific view of the human and model organism interactomes. Nucleic Acids Res 44(D1):D536–D541
Rhee SY, Beavis W, Berardini TZ, Chen G, Dixon D, Doyle A, Garcia-Hernandez M, Huala E, Lander G, Montoya M, Miller N (2003) The arabidopsis information resource (TAIR): a model organism database providing a centralized, curated gateway to Arabidopsis biology, research materials and community. Nucleic Acids Res 31(1):224–228
Pavlopoulos GA, O’Donoghue SI, Satagopam VP, Soldatos TG, Pafilis E, Schneider R (2008) Arena3D: visualization of biological networks in 3D. BMC Syst Biol 2:1–7
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504
Hooper SD, Bork P (2005) Medusa: a simple tool for interaction graph analysis. Bioinformatics 21(24):4432–4433
Brown KR, Otasek D, Ali M, McGuffin MJ, Xie W, Devani B, Toch IL, Jurisica I (2009) NAViGaTOR: network analysis, visualization and graphing Toronto. Bioinformatics 25(24):3327–3329
Breitkreutz BJ, Stark C, Tyers M (2003) Osprey: a network visualization system. Genome Biol 4:1–4
Hu Z, Mellor J, Wu J, DeLisi C (2004) VisANT: an online visualization and analysis tool for biological interaction data. BMC Bioinformatics 5(1):1–8
Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. In: Proceedings of the international AAAI conference on web and social media, vol. 3, issue 1, pp 361–362
Bayelas A (1950) Communication patterns in task-oriented group. J Acoust Soc Am 22:725–730
Shimbel A (1953) Structural parameters of communication networks. Bull Math Biophys 15:501–507
Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18(1):39–43
Freeman LC (2002) Centrality in social networks: conceptual clarification. In: Social network: critical concepts in sociology, vol 1. Routledge, London, pp 238–263
Bonacich P (1972) Technique for analyzing overlapping memberships. Sociol Methodol 4:176–185
Barrat A, Barthelemy M, Pastor-Satorras R, Vespignani A (2004) The architecture of complex weighted networks. Proc Natl Acad Sci 101(11):3747–3752
Opsahl T, Agneessens F, Skvoretz J (2010) Node centrality in weighted networks: generalizing degree and shortest paths. Soc Netw 32(3):245–251
Newman ME, Strogatz SH, Watts DJ (2001) Random graphs with arbitrary degree distributions and their applications. Phys Rev E 64(2):026118
Zelen SM (1989) Rethinking centrality: methods and examples. Soc Netw 11:1–37
Joyce KE, Laurienti PJ, Burdette JH, Hayasaka S (2010) A new measure of centrality for brain networks. PLoS One 5(8):e12200
Liu Y, Tang M, Zhou T, Do Y (2016) Identify influential spreaders in complex networks, the role of neighborhood. Phys A Stat Mech Appl 452:289–298
Wang J, Hou X, Li K, Ding Y (2017) A novel weight neighborhood centrality algorithm for identifying influential spreaders in complex networks. Phys A Stat Mech Appl 475:88–105
Bonacich P (1987) Centrality and power: a family of measures. Am J Sociol 92:1170–1182
Scardoni G, Petterlini M, Laudanna C (2009) Analyzing biological network parameters with CentiScaPe. Bioinformatics 25(21):2857–2859
Ashtiani M, Mirzaie M, Jafari M (2019) CINNA: an R/CRAN package to decipher central informative nodes in network analysis. Bioinformatics 35(8):1436–1437
Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJ Complex Syst 1695(5):1–9
Hagberg A, Swart P, Chult S (2008) Exploring network structure, dynamics, and function using NetworkX. Los Alamos National Lab (LANL), Los Alamos
Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8(4):1–7
Gu Z, Wang J (2013) CePa: an R package for finding significant pathways weighted by multiple network centralities. Bioinformatics 29(5):658–660
Junker BH, Koschützki D, Schreiber F (2006) Exploration of biological network centralities with CentiBiN. BMC Bioinformatics 7(1):1–7
Gräßler J, Koschützki D, Schreiber F (2012) CentiLib: comprehensive analysis and exploration of network centralities. Bioinformatics 28(8):1178–1179
Potapov AP, Voss N, Sasse N, Wingender E (2005) Topology of mammalian transcription networks. Genome Inform 16(2):270–278
Yu H, Kim PM, Sprecher E, Trifonov V, Gerstein M (2007) The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol 3(4):e59
Li C, Li Q, Van Mieghem P, Stanley HE, Wang H (2015) Correlation between centrality metrics and their application to the opinion model. Eur Phys J B 88:1–3
Han JD, Bertin N, Hao T, Goldberg DS, Berriz GF, Zhang LV, Dupuy D, Walhout AJ, Cusick ME, Roth FP, Vidal M (2004) Erratum: evidence for dynamically organized modularity in the yeast protein–protein interaction network. Nature 430(6997):380
Newman ME (2005) Power laws, Pareto distributions and Zipf’s law. Contemp Phys 46(5):323–351
Sinha A, Nagarajaram HA (2013) Effect of alternative splicing on the degree centrality of nodes in protein–protein interaction networks of Homo sapiens. J Proteome Res 12(4):1980–1988
Jeong H, Mason SP, Barabási AL, Oltvai ZN (2001) Lethality and centrality in protein networks. Nature 411(6833):41–42
Vallabhajosyula RR, Chakravarti D, Lutfeali S, Ray A, Raval A (2009) Identifying hubs in protein interaction networks. PLoS One 4(4):e5344
Hahn MW, Kern AD (2005) Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol Biol Evol 22(4):803–806
Patil A, Kinoshita K, Nakamura H (2010) Domain distribution and intrinsic disorder in hubs in the human protein–protein interaction network. Protein Sci 19(8):1461–1468
Hu G, Wu Z, Uversky VN, Kurgan L (2017) Functional analysis of human hub proteins and their interactors involved in the intrinsic disorder-enriched interactions. Int J Mol Sci 18(12):2761
Wright PE, Dyson HJ (1999) Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm. J Mol Biol 293(2):321–331
Dunker AK, Obradovic Z (2001) The protein trinity—linking function and disorder. Nat Biotechnol 19(9):805–806
Haynes C, Oldfield CJ, Ji F, Klitgord N, Cusick ME, Radivojac P, Uversky VN, Vidal M, Iakoucheva LM (2006) Intrinsic disorder is a common feature of hub proteins from four eukaryotic interactomes. PLoS Comput Biol 2(8):e100
Iakoucheva LM, Brown CJ, Lawson JD, Obradović Z, Dunker AK (2002) Intrinsic disorder in cell-signaling and cancer-associated proteins. J Mol Biol 23(3):573–584
Wang J, Cao Z, Zhao L, Li S (2011) Novel strategies for drug discovery based on intrinsically disordered proteins (IDPs). Int J Mol Sci 12(5):3205–3219
Ekman D, Light S, Björklund ÅK, Elofsson A (2006) What properties characterize the hub proteins of the protein-protein interaction network of Saccharomyces cerevisiae? Genome Biol 7:1–3
Kim PM, Lu LJ, Xia Y, Gerstein MB (2006) Relating three-dimensional structures to protein networks provides evolutionary insights. Science 314(5807):1938–1941
Humphris EL, Kortemme T (2007) Design of multi-specificity in protein interfaces. PLoS Comput Biol 3(8):e164
Patil A, Nakamura H (2006) Disordered domains and high surface charge confer hubs with the ability to interact with multiple proteins in interaction networks. FEBS Lett 580(8):2041–2045
Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ (2008) Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet 40(12):1413–1441
Tsai CJ, Ma B, Nussinov R (2009) Protein–protein interaction networks: how can a hub protein bind so many different partners? Trends Biochem Sci 34(12):594–600
Patil A, Kinoshita K, Nakamura H (2010) Hub promiscuity in protein-protein interaction networks. Int J Mol Sci 11(4):1930–1943
Khoury MP, Bourdon JC (2011) p53 isoforms: an intracellular microprocessor? Genes Cancer 2(4):453–465
Fraser HB, Hirsh AE, Steinmetz LM, Scharfe C, Feldman MW (2002) Evolutionary rate in the protein interaction network. Science 296(5568):750–752
Krylov DM, Wolf YI, Rogozin IB, Koonin EV (2003) Gene loss, protein sequence divergence, gene dispensability, expression level, and interactivity are correlated in eukaryotic evolution. Genome Res 13(10):2229–2235
Hahn MW, Conant GC, Wagner A (2004) Molecular evolution in large genetic networks: does connectivity equal constraint? J Mol Evol 58:203–211
Evangelisti AM, Wagner A (2004) Molecular evolution in the yeast transcriptional regulation network. J Exp Zool B Mol Dev Evol 302(4):392–411
Manna B, Bhattacharya T, Kahali B, Ghosh TC (2009) Evolutionary constraints on hub and non-hub proteins in human protein interaction network: insight from protein connectivity and intrinsic disorder. Gene 434(1–2):50–55
Pang E, Hao Y, Sun Y, Lin K (2016) Differential variation patterns between hubs and bottlenecks in human protein-protein interaction networks. BMC Evol Biol 16:1–9
Batada NN, Hurst LD, Tyers M (2006) Evolutionary and physiological importance of hub proteins. PLoS Comput Biol 2(7):e88
Higurashi M, Ishida T, Kinoshita K (2008) Identification of transient hub proteins and the possible structural basis for their multiple interactions. Protein Sci 217(1):72–78
Kim PM, Sboner A, Xia Y, Gerstein M (2008) The role of disorder in interaction networks: a structural analysis. Mol Syst Biol 4(1):179
Alberghina L, Mavelli G, Drovandi G, Palumbo P, Pessina S, Tripodi F, Coccetti P, Vanoni M (2012) Cell growth and cell cycle in Saccharomyces cerevisiae: basic regulatory design and protein–protein interaction network. Biotechnol Adv 30(1):52–72
Itzhaki Z (2011) Domain-domain interactions underlying herpesvirus-human protein-protein interaction networks. PLoS One 26(7):e21724
Calderwood MA, Venkatesan K, Xing L, Chase MR, Vazquez A, Holthaus AM, Ewence AE, Li N, Hirozane-Kishikawa T, Hill DE, Vidal M (2007) Epstein–Barr virus and virus human protein interaction maps. Proc Natl Acad Sci 104(18):7606–7611
Halehalli RR, Nagarajaram HA (2015) Molecular principles of human virus protein–protein interactions. Bioinformatics 31(7):1025–1033
Ahmed H, Howton TC, Sun Y, Weinberger N, Belkhadir Y, Mukhtar MS (2018) Network biology discovers pathogen contact points in host protein-protein interactomes. Nat Commun 9(1):2312
Zhao Z, Xia J, Tastan O, Singh I, Kshirsagar M, Carbonell J, Klein-Seetharaman J (2011) Virus interactions with human signal transduction pathways. Int J Comput Biol Drug Des 4(1):83–105
Rachita HR, Nagarajaram HA (2014) Viral proteins that bridge unconnected proteins and components in the human PPI network. Mol BioSyst 10(9):2448–2458
Dupressoir A, Vernochet C, Bawa O, Harper F, Pierron G, Opolon P, Heidmann T (2009) Syncytin-a knockout mice demonstrate the critical role in placentation of a fusogenic, endogenous retrovirus-derived, envelope gene. Proc Natl Acad Sci 106(29):12127–12132
Jonsson PF, Bates PA (2006) Global topological features of cancer proteins in the human interactome. Bioinformatics 22(18):2291–2297
Wuchty S, Siwo G, Ferdig MT (2010) Viral organization of human proteins. PLoS One 5(8):e11796
Vidal M, Cusick ME, Barabási AL (2011) Interactome networks and human disease. Cell 2144(6):986–998
Jaeger S, Aloy P (2012) From protein interaction networks to novel therapeutic strategies. IUBMB Life 64(6):529–537
Karbalaei R, Allahyari M, Rezaei-Tavirani M, Asadzadeh-Aghdaei H, Zali MR (2018) Protein-protein interaction analysis of Alzheimers disease and NAFLD based on systems biology methods unhide common ancestor pathways. Gastroenterol Hepatol Bed Bench 11(1):27
Azodi MZ, Peyvandi H, Rostami-Nejad M, Safaei A, Rostami K, Vafaee R, Heidari M, Hosseini M, Zali MR (2016) Protein-protein interaction network of celiac disease. Gastroenterol Hepatol Bed Bench 9(4):268
Feng Y, Wang Q, Wang T (2017) Drug target protein-protein interaction networks: a systematic perspective. Biomed Res Int 2017:1289259
Galan-Vasquez E, Perez-Rueda E (2021) A landscape for drug-target interactions based on network analysis. PLoS One 16(3):e0247018
Bertin N, Simonis N, Dupuy D, Cusick ME, Han JD, Fraser HB, Roth FP, Vidal M (2007) Confirmation of organized modularity in the yeast interactome. PLoS Biol 5(6):e153
Acharya D, Dutta TK (2021) Elucidating the network features and evolutionary attributes of intra-and interspecific protein–protein interactions between human and pathogenic bacteria. Sci Rep 11(1):190
Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, Bull S, Pawson T, Morris Q, Wrana JL (2009) Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol 27(2):199–204
Batada NN, Reguly T, Breitkreutz A, Boucher L, Breitkreutz BJ, Hurst LD, Tyers M (2006) Stratus not altocumulus: a new view of the yeast protein interaction network. PLoS Biol 4(10):e317
Jin G, Zhang S, Zhang XS, Chen L (2007) Hubs with network motifs organize modularity dynamically in the protein-protein interaction network of yeast. PLoS One 2(11):e1207
Biswas K, Acharya D, Podder S, Ghosh TC (2018) Evolutionary rate heterogeneity between multi-and single-interface hubs across human housekeeping and tissue-specific protein interaction network: insights from proteins’ and its partners’ properties. Genomics 110(5):283–290
Kiran M, Nagarajaram HA (2013) Global versus local hubs in human protein–protein interaction network. J Proteome Res 12(12):5436–5446
Kiran M, Nagarajaram HA (2016) Interaction and localization diversities of global and local hubs in human protein–protein interaction networks. Mol BioSyst 12(9):2875–2882
Dyer MD, Murali TM, Sobral BW (2008) The landscape of human proteins interacting with viruses and other pathogens. PLoS Pathog 4(2):e32
McDermott JE, Taylor RC, Yoon H, Heffron F (2009) Bottlenecks and hubs in inferred networks are important for virulence in Salmonella typhimurium. J Comput Biol 16(2):169–180
Mitchell HD, Eisfeld AJ, Stratton KG, Heller NC, Bramer LM, Wen J, McDermott JE, Gralinski LE, Sims AC, Le MQ, Baric RS (2019) The role of EGFR in influenza pathogenicity: multiple network-based approaches to identify a key regulator of non-lethal infections. Front Cell Dev Biol 7:200
Goñi J, Esteban FJ, de Mendizábal NV, Sepulcre J, Ardanza-Trevijano S, Agirrezabal I, Villoslada P (2008) A computational analysis of protein-protein interaction networks in neurodegenerative diseases. BMC Syst Biol 2(1):1–10
Nuoroozi G, Mirmotalebisohi SA, Sameni M, Arianmehr Y, Zali H (2021) Deregulation of microRNAs in oral squamous cell carcinoma, a bioinformatics analysis. Gene Rep 24:101241
Hwang WC, Zhang AA, Ramanathan M (2008) Identification of information flow-modulating drug targets: a novel bridging paradigm for drug discovery. Clin Pharmacol Therap 84(5):563–572
Nithya C, Kiran M, Nagarajaram HA (2023) Dissection of hubs and bottlenecks in a protein-protein interaction network. Comput Biol Chem 102:107802
Acknowledgments
N.C., a registered PhD student at the University of Hyderabad, gratefully acknowledges the Boarding cum Lodging (BBL) fellowship from the University of Hyderabad. H.A.N. gratefully acknowledges the core grant support from the University of Hyderabad. All the authors gratefully acknowledge the SAHAJ-BUILDER support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Nithya, C., Kiran, M., Nagarajaram, H.A. (2024). Hubs and Bottlenecks in Protein-Protein Interaction Networks. In: Mandal, S. (eds) Reverse Engineering of Regulatory Networks. Methods in Molecular Biology, vol 2719. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3461-5_13
Download citation
DOI: https://doi.org/10.1007/978-1-0716-3461-5_13
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-3460-8
Online ISBN: 978-1-0716-3461-5
eBook Packages: Springer Protocols