Skip to main content
SpringerLink
Log in

Toward Large-Scale Computational Prediction of Protein Complexes

  • Protocol
  • First Online:
Book cover Computational Cell Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1819))

Abstract

Cellular functions are often performed by multiprotein structures called protein complexes. These complexes are dynamic structures that evolve during the cell cycle or in response to external and internal stimuli, and are tightly regulated by protein expression in different tissues resulting in quantitative and qualitative variation of protein complexes. Advances in high-throughput techniques, such as mass-spectrometry and yeast two-hybrid provided a large amount of data on protein–protein interactions. This sparked the development of computational methods able to predict protein complex formation under a variety of biological and clinical conditions. However, the challenges that need to be addressed for successful computational protein complex prediction are highly complex.

The post-genomic era saw an emerging number of algorithms and software, which are able to predict protein complexes from protein–protein interaction networks and a variety of other sources. Despite the high capacity of these methods to qualitatively predict protein complexes, they could provide only limited or no quantitative information of the predicted complexes. Recently, a new large-scale simulation of protein complexes was able to achieve this task by simulating protein complex formation on the proteome scale.

In this chapter, we review representative methods that can predict multiple protein complexes at different scales and discuss how these can be combined with emerging sources of data in order to improve protein complex characterization.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gavin AC, Aloy P, Grandi P, Krause R, Boesche M, Marzioch M, Rau C, Jensen LJ, Bastuck S, Dumpelfeld B, Edelmann A, Heurtier MA, Hoffman V, Hoefert C, Klein K, Hudak M, Michon AM, Schelder M, Schirle M, Remor M, Rudi T, Hooper S, Bauer A, Bouwmeester T, Casari G, Drewes G, Neubauer G, Rick JM, Kuster B, Bork P, Russell RB, Superti-Furga G (2006) Proteome survey reveals modularity of the yeast cell machinery. Nature 440(7084):631–636. https:// doi.org/10.1038/nature04532

    Article  PubMed  Google Scholar 

  2. Havugimana Pierre C, Hart GT, Nepusz T, Yang H, Turinsky Andrei L, Li Z, Wang Peggy I, Boutz Daniel R, Fong V, Phanse S, Babu M, Craig Stephanie A, Hu P, Wan C, Vlasblom J, Dar V-u-N, Bezginov A, Clark Gregory W, Wu Gabriel C, Wodak Shoshana J, Tillier Elisabeth RM, Paccanaro A, Marcotte Edward M, Emili A (2012) A census of human soluble protein complexes. Cell 150(5):1068–1081. https:// doi.org/10.1016/j.cell.2012.08.011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Hein Marco Y, Hubner Nina C, Poser I, Cox J, Nagaraj N, Toyoda Y, Gak Igor A, Weisswange I, Mansfeld J, Buchholz F, Hyman Anthony A, Mann M (2015) A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell 163(3):712–723. https:// doi.org/10.1016/j.cell.2015.09.053

    Article  CAS  PubMed  Google Scholar 

  4. de Lichtenberg U, Jensen LJ, Brunak S, Bork P (2005) Dynamic complex formation during the yeast cell cycle. Science 307(5710):724–727. https:// doi.org/10.1126/science.1105103

    Article  CAS  PubMed  Google Scholar 

  5. Bader GD, Hogue CW (2002) Analyzing yeast protein–protein interaction data obtained from different sources. Nat Biotechnol 20(10):991–997

    Article  CAS  Google Scholar 

  6. Srihari S, Yong CH, Patil A, Wong L (2015) Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes. FEBS Lett. https:// doi.org/10.1016/j.febslet.2015.04.026

    Google Scholar 

  7. Sanbonmatsu KY (2012) Computational studies of molecular machines: the ribosome. Curr Opin Struct Biol 22(2):168–174. https:// doi.org/10.1016/j.sbi.2012.01.008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Perilla JR, Goh BC, Cassidy CK, Liu B, Bernardi RC, Rudack T, Yu H, Wu Z, Schulten K (2015) Molecular dynamics simulations of large macromolecular complexes. Curr Opin Struct Biol 31:64–74. https:// doi.org/10.1016/j.sbi.2015.03.007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Suderman R, Deeds EJ (2013) Machines vs. ensembles: effective MAPK signaling through heterogeneous sets of protein complexes. PLoS Comput Biol 9(10):e1003278. https:// doi.org/10.1371/journal.pcbi.1003278

    Article  PubMed  PubMed Central  Google Scholar 

  10. Deeds EJ, Krivine J, Feret J, Danos V, Fontana W (2012) Combinatorial complexity and compositional drift in protein interaction networks. PLoS One 7(3):e32032. https:// doi.org/10.1371/journal.pone.0032032

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Beyer A, Wilhelm T (2004) Dynamic simulation of protein complex formation on a genomic scale. Bioinformatics 21(8):1610–1616. https:// doi.org/10.1093/bioinformatics/bti223

    Article  CAS  PubMed  Google Scholar 

  12. Osmanović D, Rabin Y (2016) Effect of non-specific interactions on formation and stability of specific complexes. J Chem Phys 144(20):205104. https:// doi.org/10.1063/1.4952981

    Article  CAS  PubMed  Google Scholar 

  13. Rizzetto S, Priami C, Csikasz-Nagy A (2015) Qualitative and quantitative protein complex prediction through proteome-wide simulations. PLoS Comput Biol 11(10):e1004424. https:// doi.org/10.1371/journal.pcbi.1004424

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Clancy T, Hovig E (2014) From proteomes to complexomes in the era of systems biology. Proteomics 14(1):24–41. https:// doi.org/10.1002/pmic.201300230

    Article  CAS  PubMed  Google Scholar 

  15. Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4:2

    Article  Google Scholar 

  16. Nepusz T, Yu H, Paccanaro A (2012) Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods 9(5):471–472

    Article  CAS  Google Scholar 

  17. 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

    Article  CAS  PubMed  Google Scholar 

  18. Richardson JS (1981) The anatomy and taxonomy of protein structure. Adv Protein Chem 34:167–339. https:// doi.org/10.1016/s0065-3233(08)60520-3

    Article  CAS  PubMed  Google Scholar 

  19. Ozawa Y, Saito R, Fujimori S, Kashima H, Ishizaka M, Yanagawa H, Miyamoto-Sato E, Tomita M (2010) Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions. BMC Bioinformatics 11:350. https:// doi.org/10.1186/1471-2105-11-350

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ma W, McAnulla C, Wang L (2012) Protein complex prediction based on maximum matching with domain-domain interaction. Biochim Biophys Acta 1824(12):1418–1424. https:// doi.org/10.1016/j.bbapap.2012.06.009

    Article  CAS  PubMed  Google Scholar 

  21. Xu B, Lin H, Chen Y, Yang Z, Liu H (2013) Protein complex identification by integrating protein-protein interaction evidence from multiple sources. PLoS One 8(12):e83841. https:// doi.org/10.1371/journal.pone.0083841

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wang J, Peng X, Li M, Pan Y (2013) Construction and application of dynamic protein interaction network based on time course gene expression data. Proteomics 13(2):301–312. https:// doi.org/10.1002/pmic.201200277

    Article  CAS  PubMed  Google Scholar 

  23. Zhang Y, Lin H, Yang Z, Wang J (2016) Construction of dynamic probabilistic protein interaction networks for protein complex identification. BMC Bioinformatics 17(1). https:// doi.org/10.1186/s12859-016-1054-1

    Google Scholar 

  24. Zhang Y, Lin H, Yang Z, Wang J, Xu B (2013) Integrating multiple biomedical resources for protein complex prediction. IEEE, Shanghai, pp 456–459. https:// doi.org/10.1109/bibm.2013.6732535

    Book  Google Scholar 

  25. Li X, Wang J, Zhao B, Wu F-X, Pan Y (2016) Identification of protein complexes from multi-relationship protein interaction networks. Hum Genomics 10(S2). https:// doi.org/10.1186/s40246-016-0069-z

    Google Scholar 

  26. SV Dongen (2000) Graph clustering by flow simulation. Ph.D. thesis, University of Utrecht

    Google Scholar 

  27. Bernaschi M, Castiglione F, Ferranti A, Gavrila C, Tinti M, Cesareni G (2007) ProtNet: a tool for stochastic simulations of protein interaction networks dynamics. BMC Bioinformatics 8(Suppl 1):S4. https:// doi.org/10.1186/1471-2105-8-s1-s4

    Article  PubMed  PubMed Central  Google Scholar 

  28. Galeota E, Gravila C, Castiglione F, Bernaschi M, Cesareni G (2015) The hierarchical organization of natural protein interaction networks confers self-organization properties on pseudocells. BMC Syst Biol 9(Suppl 3):S3. https:// doi.org/10.1186/1752-0509-9-s3-s3

    Article  PubMed  PubMed Central  Google Scholar 

  29. Xie Z-R, Chen J, Wu Y (2016) Multiscale model for the assembly kinetics of protein complexes. J Phys Chem B 120(4):621–632. https:// doi.org/10.1021/acs.jpcb.5b08962

    Article  CAS  PubMed  Google Scholar 

  30. Mewes H-W, Frishman D, Mayer KF, Münsterkötter M, Noubibou O, Pagel P, Rattei T, Oesterheld M, Ruepp A, Stümpflen V (2006) MIPS: analysis and annotation of proteins from whole genomes in 2005. Nucleic Acids Res 34(suppl 1):D169–D172

    Article  CAS  Google Scholar 

  31. Pu S, Wong J, Turner B, Cho E, Wodak SJ (2009) Up-to-date catalogues of yeast protein complexes. Nucleic Acids Res 37(3):825–831. https:// doi.org/10.1093/nar/gkn1005

    Article  CAS  PubMed  Google Scholar 

  32. Shen X, Yi L, Jiang X, Zhao Y, Hu X, He T, Yang J (2016) Neighbor affinity based algorithm for discovering temporal protein complex from dynamic PPI network. Methods 110:90–96. https:// doi.org/10.1016/j.ymeth.2016.06.010

    Article  CAS  PubMed  Google Scholar 

  33. Gillespie DT (2001) Approximate accelerated stochastic simulation of chemically reacting systems. J Chem Phys 115(4):1716. https:// doi.org/10.1063/1.1378322

    Article  CAS  Google Scholar 

  34. Letunic I, Doerks T, Bork P (2014) SMART: recent updates, new developments and status in 2015. Nucleic Acids Res 43(D1):D257–D260. https:// doi.org/10.1093/nar/gku949

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Mavrich TN, Ioshikhes IP, Venters BJ, Jiang C, Tomsho LP, Qi J, Schuster SC, Albert I, Pugh BF (2008) A barrier nucleosome model for statistical positioning of nucleosomes throughout the yeast genome. Genome Res 18(7):1073–1083. https:// doi.org/10.1101/gr.078261.108

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Schaefer MH, Fontaine JF, Vinayagam A, Porras P, Wanker EE, Andrade-Navarro MA (2012) HIPPIE: integrating protein interaction networks with experiment based quality scores. PLoS One 7(2):e31826. https:// doi.org/10.1371/journal.pone.0031826

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Yellaboina S, Tasneem A, Zaykin DV, Raghavachari B, Jothi R (2010) DOMINE: a comprehensive collection of known and predicted domain-domain interactions. Nucleic Acids Res 39(Database):D730–D735. https:// doi.org/10.1093/nar/gkq1229

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kim Y, Min B, Yi GS (2012) IDDI: integrated domain-domain interaction and protein interaction analysis system. Proteome Sci 10(Suppl 1):S9. https:// doi.org/10.1186/1477-5956-10-S1-S9

    Article  PubMed  PubMed Central  Google Scholar 

  39. Garzón JI, Deng L, Murray D, Shapira S, Petrey D, Honig B (2016) A computational interactome and functional annotation for the human proteome. eLife 5:pii: e18715. https:// doi.org/10.7554/eLife.18715

    Article  CAS  Google Scholar 

  40. Ahnert SE, Marsh JA, Hernandez H, Robinson CV, Teichmann SA (2015) Principles of assembly reveal a periodic table of protein complexes. Science 350(6266):aaa2245. https:// doi.org/10.1126/science.aaa2245

    Article  CAS  PubMed  Google Scholar 

  41. Acuner Ozbabacan SE, Engin HB, Gursoy A, Keskin O (2011) Transient protein-protein interactions. Protein Eng Des Sel 24(9):635–648. https:// doi.org/10.1093/protein/gzr025

    Article  CAS  PubMed  Google Scholar 

  42. Ghaemmaghami S, Huh W-K, Bower K, Howson RW, Belle A, Dephoure N, O'Shea EK, Weissman JS (2003) Global analysis of protein expression in yeast. Nature 425(6959):737–741

    Article  CAS  Google Scholar 

  43. Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson A, Kampf C, Sjostedt E, Asplund A, Olsson I, Edlund K, Lundberg E, Navani S, Szigyarto CAK, Odeberg J, Djureinovic D, Takanen JO, Hober S, Alm T, Edqvist PH, Berling H, Tegel H, Mulder J, Rockberg J, Nilsson P, Schwenk JM, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M, von Heijne G, Nielsen J, Ponten F (2015) Tissue-based map of the human proteome. Science 347(6220):1260419–1260419. https:// doi.org/10.1126/science.1260419

    Article  CAS  PubMed  Google Scholar 

  44. Eisenberg E, Levanon EY (2013) Human housekeeping genes, revisited. Trends Genet 29(10):569–574. https:// doi.org/10.1016/j.tig.2013.05.010

    Article  CAS  PubMed  Google Scholar 

  45. Mayne J, Ning Z, Zhang X, Starr AE, Chen R, Deeke S, Chiang C-K, Xu B, Wen M, Cheng K, Seebun D, Star A, Moore JI, Figeys D (2016) Bottom-up proteomics (2013–2015): keeping up in the era of systems biology. Anal Chem 88(1):95–121. https:// doi.org/10.1021/acs.analchem.5b04230

    Article  CAS  PubMed  Google Scholar 

  46. Zieske LR (2006) A perspective on the use of iTRAQTM reagent technology for protein complex and profiling studies. J Exp Bot 57(7):1501–1508. https:// doi.org/10.1093/jxb/erj168

    Article  CAS  PubMed  Google Scholar 

  47. Schwanhäusser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W, Selbach M (2011) Global quantification of mammalian gene expression control. Nature 473(7347):337–342. https:// doi.org/10.1038/nature10098

    Article  CAS  PubMed  Google Scholar 

  48. Kim MS, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Chaerkady R, Madugundu AK, Kelkar DS, Isserlin R, Jain S, Thomas JK, Muthusamy B, Leal-Rojas P, Kumar P, Sahasrabuddhe NA, Balakrishnan L, Advani J, George B, Renuse S, Selvan LD, Patil AH, Nanjappa V, Radhakrishnan A, Prasad S, Subbannayya T, Raju R, Kumar M, Sreenivasamurthy SK, Marimuthu A, Sathe GJ, Chavan S, Datta KK, Subbannayya Y, Sahu A, Yelamanchi SD, Jayaram S, Rajagopalan P, Sharma J, Murthy KR, Syed N, Goel R, Khan AA, Ahmad S, Dey G, Mudgal K, Chatterjee A, Huang TC, Zhong J, Wu X, Shaw PG, Freed D, Zahari MS, Mukherjee KK, Shankar S, Mahadevan A, Lam H, Mitchell CJ, Shankar SK, Satishchandra P, Schroeder JT, Sirdeshmukh R, Maitra A, Leach SD, Drake CG, Halushka MK, Prasad TS, Hruban RH, Kerr CL, Bader GD, Iacobuzio-Donahue CA, Gowda H, Pandey A (2014) A draft map of the human proteome. Nature 509(7502):575–581. https:// doi.org/10.1038/nature13302

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Schmidt A, Kochanowski K, Vedelaar S, Ahrne E, Volkmer B, Callipo L, Knoops K, Bauer M, Aebersold R, Heinemann M (2016) The quantitative and condition-dependent Escherichia coli proteome. Nat Biotechnol 34(1):104–110. https:// doi.org/10.1038/nbt.3418

    Article  CAS  PubMed  Google Scholar 

  50. Lawrence Robert T, Perez Elizabeth M, Hernández D, Miller Chris P, Haas Kelsey M, Irie Hanna Y, Lee S-I, Blau CA, Villén J (2015) The proteomic landscape of triple-negative breast cancer. Cell Rep 11(4):630–644. https:// doi.org/10.1016/j.celrep.2015.03.050

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Hukelmann JL, Anderson KE, Sinclair LV, Grzes KM, Murillo AB, Hawkins PT, Stephens LR, Lamond AI, Cantrell DA (2016) The cytotoxic T cell proteome and its shaping by the kinase mTOR. Nat Immunol 17(1):104–112. https:// doi.org/10.1038/ni.3314

    Article  CAS  PubMed  Google Scholar 

  52. Kolker E, Higdon R, Haynes W, Welch D, Broomall W, Lancet D, Stanberry L, Kolker N (2012) MOPED: model organism protein expression database. Nucleic Acids Res 40(Database issue):D1093–D1099. https:// doi.org/10.1093/nar/gkr1177

    Article  CAS  PubMed  Google Scholar 

  53. Wang M, Weiss M, Simonovic M, Haertinger G, Schrimpf SP, Hengartner MO, von Mering C (2012) PaxDb, a database of protein abundance averages across all three domains of life. Mol Cell Proteomics 11(8):492–500. https:// doi.org/10.1074/mcp.O111.014704

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Binder JX, Pletscher-Frankild S, Tsafou K, Stolte C, O'Donoghue SI, Schneider R, Jensen LJ (2014) COMPARTMENTS: unification and visualization of protein subcellular localization evidence. Database 2014:bau012. https:// doi.org/10.1093/database/bau012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Emanuelsson O, Nielsen H, Brunak S, von Heijne G (2000) Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J Mol Biol 300(4):1005–1016. https:// doi.org/10.1006/jmbi.2000.3903

    Article  CAS  Google Scholar 

  56. Satija R, Farrell JA, Gennert D, Schier AF, Regev A (2015) Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33(5):495–502. https:// doi.org/10.1038/nbt.3192

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Newell EW, Cheng Y (2016) Mass cytometry: blessed with the curse of dimensionality. Nat Immunol 17(8):890–895. https:// doi.org/10.1038/ni.3485

    Article  CAS  PubMed  Google Scholar 

  58. Costanzo M, VanderSluis B, Koch EN, Baryshnikova A, Pons C, Tan G, Wang W, Usaj M, Hanchard J, Lee SD, Pelechano V, Styles EB, Billmann M, van Leeuwen J, van Dyk N, Lin ZY, Kuzmin E, Nelson J, Piotrowski JS, Srikumar T, Bahr S, Chen Y, Deshpande R, Kurat CF, Li SC, Li Z, Usaj MM, Okada H, Pascoe N, San Luis BJ, Sharifpoor S, Shuteriqi E, Simpkins SW, Snider J, Suresh HG, Tan Y, Zhu H, Malod-Dognin N, Janjic V, Przulj N, Troyanskaya OG, Stagljar I, Xia T, Ohya Y, Gingras AC, Raught B, Boutros M, Steinmetz LM, Moore CL, Rosebrock AP, Caudy AA, Myers CL, Andrews B, Boone C (2016) A global genetic interaction network maps a wiring diagram of cellular function. Science 353(6306). https:// doi.org/10.1126/science.aaf1420

    Article  Google Scholar 

  59. Chiu YL, Cao H, Rana TM (2007) Quantitative analysis of RNA-mediated protein-protein interactions in living cells by FRET. Chem Biol Drug Des 69(4):233–239. https:// doi.org/10.1111/j.1747-0285.2007.00501.x

    Article  CAS  PubMed  Google Scholar 

  60. Nilsson T, Lundin CR, Nordlund G, Adelroth P, von Ballmoos C, Brzezinski P (2016) Lipid-mediated protein-protein interactions modulate respiration-driven ATP synthesis. Sci Rep 6:24113. https:// doi.org/10.1038/srep24113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Giudice G, Sánchez-Cabo F, Torroja C, Lara-Pezzi E (2016) ATtRACT—a database of RNA-binding proteins and associated motifs. Database 2016:baw035. https:// doi.org/10.1093/database/baw035

    Article  Google Scholar 

  62. Zanegina O, Kirsanov D, Baulin E, Karyagina A, Alexeevski A, Spirin S (2016) An updated version of NPIDB includes new classifications of DNA–protein complexes and their families. Nucleic Acids Res 44(D1):D144–D153. https:// doi.org/10.1093/nar/gkv1339

    Article  CAS  PubMed  Google Scholar 

  63. Yachie N, Saito R, Sugiyama N, Tomita M, Ishihama Y (2011) Integrative features of the yeast phosphoproteome and protein-protein interaction map. PLoS Comput Biol 7(1):e1001064. https:// doi.org/10.1371/journal.pcbi.1001064

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Duan G, Walther D (2015) The roles of post-translational modifications in the context of protein interaction networks. PLoS Comput Biol 11(2):e1004049. https:// doi.org/10.1371/journal.pcbi.1004049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Huang KY, Su MG, Kao HJ, Hsieh YC, Jhong JH, Cheng KH, Huang HD, Lee TY (2016) dbPTM 2016: 10-year anniversary of a resource for post-translational modification of proteins. Nucleic Acids Res 44(D1):D435–D446. https:// doi.org/10.1093/nar/gkv1240

    Article  CAS  PubMed  Google Scholar 

  66. Ori A, Iskar M, Buczak K, Kastritis P, Parca L, Andrés-Pons A, Singer S, Bork P, Beck M (2016) Spatiotemporal variation of mammalian protein complex stoichiometries. Genome Biol 17(1). https:// doi.org/10.1186/s13059-016-0912-5

    Google Scholar 

  67. Gosens I, den Hollander AI, Cremers FP, Roepman R (2008) Composition and function of the crumbs protein complex in the mammalian retina. Exp Eye Res 86(5):713–726. https:// doi.org/10.1016/j.exer.2008.02.005

    Article  CAS  PubMed  Google Scholar 

  68. Hung MC, Link W (2011) Protein localization in disease and therapy. J Cell Sci 124(20):3381–3392. https:// doi.org/10.1242/jcs.089110

    Article  CAS  PubMed  Google Scholar 

  69. Rodina A, Wang T, Yan P, Gomes ED, Dunphy MPS, Pillarsetty N, Koren J, Gerecitano JF, Taldone T, Zong H, Caldas-Lopes E, Alpaugh M, Corben A, Riolo M, Beattie B, Pressl C, Peter RI, Xu C, Trondl R, Patel HJ, Shimizu F, Bolaender A, Yang C, Panchal P, Farooq MF, Kishinevsky S, Modi S, Lin O, Chu F, Patil S, Erdjument-Bromage H, Zanzonico P, Hudis C, Studer L, Roboz GJ, Cesarman E, Cerchietti L, Levine R, Melnick A, Larson SM, Lewis JS, Guzman ML, Chiosis G (2016) The epichaperome is an integrated chaperome network that facilitates tumour survival. Nature 538(7625):397–401. https:// doi.org/10.1038/nature19807

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Lage K, Karlberg EO, Størling ZM, Ólason PÍ, Pedersen AG, Rigina O, Hinsby AM, Tümer Z, Pociot F, Tommerup N, Moreau Y, Brunak S (2007) A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol 25(3):309–316. https:// doi.org/10.1038/nbt1295

    Article  CAS  PubMed  Google Scholar 

  71. Le DH (2015) A novel method for identifying disease associated protein complexes based on functional similarity protein complex networks. Algorithms Mol Biol 10:14. https:// doi.org/10.1186/s13015-015-0044-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Vinayagam A, Hu Y, Kulkarni M, Roesel C, Sopko R, Mohr SE, Perrimon N (2013) Protein complex-based analysis framework for high-throughput data sets. Sci Signal 6(264):rs5. https:// doi.org/10.1126/scisignal.2003629

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Wu M, Yu Q, Li X, Zheng J, Huang JF, Kwoh CK (2013) Benchmarking human protein complexes to investigate drug-related systems and evaluate predicted protein complexes. PLoS One 8(2):e53197. https:// doi.org/10.1371/journal.pone.0053197

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Nacher JC, Schwartz JM (2012) Modularity in protein complex and drug interactions reveals new polypharmacological properties. PLoS One 7(1):e30028. https:// doi.org/10.1371/journal.pone.0030028

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Hart JR, Zhang Y, Liao L, Ueno L, Du L, Jonkers M, Yates JR, Vogt PK (2015) The butterfly effect in cancer: a single base mutation can remodel the cell. Proc Natl Acad Sci 112(4):1131–1136. https:// doi.org/10.1073/pnas.1424012112

    Article  CAS  PubMed  Google Scholar 

  76. Collins SR, Kemmeren P, Zhao X-C, Greenblatt JF, Spencer F, Holstege FC, Weissman JS, Krogan NJ (2007) Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae. Mol Cell Proteomics 6(3):439–450

    Article  CAS  Google Scholar 

  77. Azevedo H, Moreira-Filho CA (2015) Topological robustness analysis of protein interaction networks reveals key targets for overcoming chemotherapy resistance in glioma. Sci Rep 5:16830. https:// doi.org/10.1038/srep16830

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Medical Sciences, Kensington, UNSW, Australia

    Simone Rizzetto

  2. Viral Immunology Systems Program, Kirby Institute for Infection and Immunity, Kensington, UNSW, Australia

    Simone Rizzetto

  3. Randall Division of Cell and Molecular Biophysics, Institute for Mathematical and Molecular Biomedicine, King’s College London, London, SE1 1UL, UK

    Attila Csikász-Nagy

  4. Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, H-1083, Budapest, Hungary

    Attila Csikász-Nagy

Authors
  1. Simone Rizzetto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Attila Csikász-Nagy
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

    Louise von Stechow

  2. Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

    Alberto Santos Delgado

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Rizzetto, S., Csikász-Nagy, A. (2018). Toward Large-Scale Computational Prediction of Protein Complexes. In: von Stechow, L., Santos Delgado, A. (eds) Computational Cell Biology. Methods in Molecular Biology, vol 1819. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8618-7_13

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-8618-7_13

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8617-0

  • Online ISBN: 978-1-4939-8618-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation