Abstract
Chameleon proteins are proteins which include sequences that can adopt α-helix–β-strand (HE-chameleon) or α-helix–coil (HC-chameleon) or β-strand–coil (CE-chameleon) structures to operate their crucial biological functions. In this study, using a network-based approach, we examined the chameleon proteins to give a better knowledge on these proteins. We focused on proteins with identical chameleon sequences with more than or equal to seven residues long in different PDB entries, which adopt HE-chameleon, HC-chameleon, and CE-chameleon structures in the same protein. One hundred and ninety-one human chameleon proteins were identified via our in-house program. Then, protein–protein interaction (PPI) networks, Gene ontology (GO) enrichment, disease network, and pathway enrichment analyses were performed for our derived data set. We discovered that there are chameleon sequences which reside in protein–protein interaction regions between two proteins critical for their dual function. Analysis of the PPI networks for chameleon proteins introduced five hub proteins, namely TP53, EGFR, HSP90AA1, PPARA, and HIF1A, which were presented in four PPI clusters. The outcomes demonstrate that the chameleon regions are in critical domains of these proteins and are important in the development and treatment of human cancers. The present report is the first network-based functional study of chameleon proteins using computational approaches and might provide a new perspective for understanding the mechanisms of diseases helping us in developing new medical therapies along with discovering new proteins with chameleon properties which are highly important in cancer.
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References
Ahmadi M, Jafari R, Marashi SA, Farazmand A (2015a) Evidence for the relationship between the regulatory effects of microRNAs and attack robustness of biological networks. Comput Biol Med 63:83–91
Ahmadi M, Jafari R, Marashi SA, Farazmand A (2015b) Indirect role of microRNAs and transcription factors in the regulation of important cancer genes: a network biology approach. Cell Mol Biol (Noisy-le-grand) 61:100–107
Almaas E, Oltvai ZN, Barabasi AL (2005) The activity reaction core and plasticity of metabolic networks. PLoS Comput Biol 1:e68
Altermann E, Klaenhammer TR (2005) PathwayVoyager: pathway mapping using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. BMC Genom 6:60
Assenov Y, Ramirez F, Schelhorn SE, Lengauer T, Albrecht M (2008) Computing topological parameters of biological networks. Bioinformatics 24:282–284
Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform 4:2
Bahramali G, Goliaei B, Minuchehr Z, Salari A (2016) Chameleon sequences in neurodegenerative diseases. Biochem Biophys Res Commun 472:209–216
Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic acids res 28:235–242
Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pages F, Trajanoski Z, Galon J (2009) ClueGO: a Cytoscape plug-into decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25:1091–1093
Browne F, Wang H, Zheng H (2016) Investigating the impact human protein–protein interaction networks have on disease-gene analysis. Int J Mach Learn Cybern, 1–10
Cabarkapa V (2015) Cystatin C—more than the marker of the glomerular filtration rate. Med Pregl 68:173–179
Califano R, Romanidou O, Mountzios G, Landi L, Cappuzzo F, Blackhall F (2016) Management of NSCLC disease progression after first-line EGFR tyrosine kinase inhibitors: what are the issues and potential therapies? Drugs 76:831–840
Chang RL, Andrews K, Kim D, Li Z, Godzik A, Palsson BO (2013) Structural systems biology evaluation of metabolic thermotolerance in Escherichia coli. Science 340:1220–1223
Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma’ayan A (2013) Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinform 14:128
Chene P (2001) The role of tetramerization in p53 function. Oncogene 20:2611–2617
Colombo M, Laayouni H, Invergo BM, Bertranpetit J, Montanucci L (2014) Metabolic flux is a determinant of the evolutionary rates of enzyme-encoding genes. Evolution 68:605–613
Cui Q, Yu Z, Purisima EO, Wang E (2006) Principles of microRNA regulation of a human cellular signaling network. Mol Syst Biol 2:46
Cui Q, Yu Z, Pan Y, Purisima EO, Wang E (2007) MicroRNAs preferentially target the genes with high transcriptional regulation complexity. Biochem Biophys Res Commun 352:733–738
Cumberworth A, Lamour G, Babu MM, Gsponer J (2013) Promiscuity as a functional trait: intrinsically disordered regions as central players of interactomes. Biochem J 454:361–369
Drescher M, Huber M, Subramaniam V (2012) Hunting the chameleon: structural conformations of the intrinsically disordered protein alpha-synuclein. ChemBioChem 13:761–768
Eckl JM, Richter K (2013) Functions of the Hsp90 chaperone system: lifting client proteins to new heights. Int J Biochem Mol Biol 4:157–165
Gajiwala KS, Feng J, Ferre R, Ryan K, Brodsky O, Weinrich S, Kath JC, Stewart A (2013) Insights into the aberrant activity of mutant EGFR kinase domain and drug recognition. Structure 21:209–219
Gazdar AF (2009) Activating and resistance mutations of EGFR in non-small-cell lung cancer: role in clinical response to EGFR tyrosine kinase inhibitors. Oncogene 28(Suppl 1):S24–S31
Gendoo DM, Harrison PM (2011) Discordant and chameleon sequences: their distribution and implications for amyloidogenicity. Protein Sci 20:567–579
Ghozlane A, Joseph AP, Bornot A, de Brevern AG (2009) Analysis of protein chameleon sequence characteristics. Bioinformation 3:367–369
Grainger RJ, Beggs JD (2005) Prp8 protein: at the heart of the spliceosome. RNA 11:533–557
Guo JT, Jaromczyk JW, Xu Y (2007) Analysis of chameleon sequences and their implications in biological processes. Proteins 67:548–558
Hanan EJ, Eigenbrot C, Bryan MC, Burdick DJ, Chan BK, Chen Y, Dotson J, Heald RA, Jackson PS, La H, Lainchbury MD, Malek S, Purkey HE, Schaefer G, Schmidt S, Seward EM, Sideris S, Tam C, Wang S, Yeap SK, Yen I, Yin J, Yu C, Zilberleyb I, Heffron TP (2014) Discovery of selective and noncovalent diaminopyrimidine-based inhibitors of epidermal growth factor receptor containing the T790M resistance mutation. J Med Chem 57:10176–10191
Hosseini Z, Marashi SA (2015) Hierarchical organization of fluxes in Escherichia coli metabolic network: using flux coupling analysis for understanding the physiological properties of metabolic genes. Gene 561:199–208
Jacoboni I, Martelli PL, Fariselli P, Compiani M, Casadio R (2000) Predictions of protein segments with the same aminoacid sequence and different secondary structure: a benchmark for predictive methods. Proteins 41:535–544
Joerger AC, Fersht AR (2010) The tumor suppressor p53: from structures to drug discovery. Cold Spring Harbor Perspect Biol 2:a000919
Jomova K, Vondrakova D, Lawson M, Valko M (2010) Metals, oxidative stress and neurodegenerative disorders. Mol Cell Biochem 345:91–104
Joy MP, Brock A, Ingber DE, Huang S (2005) High-betweenness proteins in the yeast protein interaction network. J Biomed Biotechnol 2005:96–103
Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–2637
Kabsch W, Sander C (1984) On the use of sequence homologies to predict protein structure: identical pentapeptides can have completely different conformations. Proc Natl Acad Sci USA 81:1075–1078
Kamada R, Toguchi Y, Nomura T, Imagawa T, Sakaguchi K (2016) Tetramer formation of tumor suppressor protein p53: structure, function, and applications. Biopolymers 106:598–612
Kar G, Gursoy A, Keskin O (2009) Human cancer protein-protein interaction network: a structural perspective. PLoS Comput Biol 5:e1000601
Kaur G, Levy E (2012) Cystatin C in Alzheimer’s disease. Front Mol Neurosci 5:79
Krishna N, Guruprasad K (2011) Certain heptapeptide and large sequences representing an entire helix, strand or coil conformation in proteins are associated as chameleon sequences. Int J Biol Macromol 49:218–222
Kuan CT, Wikstrand CJ, Bigner DD (2001) EGF mutant receptor vIII as a molecular target in cancer therapy. Endocr Relat Cancer 8:83–96
Kulikov R, Winter M, Blattner C (2006) Binding of p53 to the central domain of Mdm2 is regulated by phosphorylation. J Biol Chem 281:28575–28583
Li Y, Zhang T, Schwartz SJ, Sun D (2009) New developments in Hsp90 inhibitors as anti-cancer therapeutics: mechanisms, clinical perspective and more potential. Drug Resist Updates 12:17–27
Li W, Kinch LN, Karplus PA, Grishin NV (2015) ChSeq: a database of chameleon sequences. Protein Sci 24:1075–1086
Marcos-Carcavilla A, Calvo JH, Gonzalez C, Moazami-Goudarzi K, Laurent P, Bertaud M, Hayes H, Beattie AE, Serrano C, Lyahyai J, Martin-Burriel I, Serrano M (2008) Structural and functional analysis of the HSP90AA1 gene: distribution of polymorphisms among sheep with different responses to scrapie. Cell Stress Chaperones 13:19–29
Michelsen K, Jordan JB, Lewis J, Long AM, Yang E, Rew Y, Zhou J, Yakowec P, Schnier PD, Huang X, Poppe L (2012) Ordering of the N-terminus of human MDM2 by small molecule inhibitors. J Am Chem Soc 134:17059–17067
Moll UM, Petrenko O (2003) The MDM2-p53 interaction. Mol Cancer Res 1:1001–1008
Mozaffari-Jovin S, Wandersleben T, Santos KF, Will CL, Luhrmann R, Wahl MC (2014) Novel regulatory principles of the spliceosomal Brr2 RNA helicase and links to retinal disease in humans. RNA Biol 11:298–312
Normanno N, De Luca A, Bianco C, Strizzi L, Mancino M, Maiello MR, Carotenuto A, De Feo G, Caponigro F, Salomon DS (2006) Epidermal growth factor receptor (EGFR) signaling in cancer. Gene 366:2–16
Notebaart RA, Teusink B, Siezen RJ, Papp B (2008) Co-regulation of metabolic genes is better explained by flux coupling than by network distance. PLoS Comput Biol 4:e26
Notebaart RA, Kensche PR, Huynen MA, Dutilh BE (2009) Asymmetric relationships between proteins shape genome evolution. Genome Biol 10:R19
Notebaart RA, Szappanos B, Kintses B, Pal F, Gyorkei A, Bogos B, Lazar V, Spohn R, Csorgo B, Wagner A, Ruppin E, Pal C, Papp B (2014) Network-level architecture and the evolutionary potential of underground metabolism. Proc Natl Acad Sci USA 111:11762–11767
Pal C, Papp B, Lercher MJ, Csermely P, Oliver SG, Hurst LD (2006) Chance and necessity in the evolution of minimal metabolic networks. Nature 440:667–670
Papp B, Notebaart RA, Pal C (2011) Systems-biology approaches for predicting genomic evolution. Nat Rev Genet 12:591–602
Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612
Pinero J, Queralt-Rosinach N, Bravo A, Deu-Pons J, Bauer-Mehren A, Baron M, Sanz F, Furlong LI (2015) DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database (Oxford) 2015:bav028
Pinotsis N, Wilmanns M (2008) Protein assemblies with palindromic structure motifs. Cell Mol Life Sci 65:2953–2956
Rose PW, Bi C, Bluhm WF, Christie CH, Dimitropoulos D, Dutta S, Green RK, Goodsell DS, Prlic A, Quesada M, Quinn GB, Ramos AG, Westbrook JD, Young J, Zardecki C, Berman HM, Bourne PE (2013) The RCSB Protein Data Bank: new resources for research and education. Nucleic Acids Res 41:D475–D482
Saha T, Kar RK, Sa G (2015) Structural and sequential context of p53: a review of experimental and theoretical evidence. Prog Biophys Mol Biol 117:250–263
Santiago JA, Potashkin JA (2014) A network approach to clinical intervention in neurodegenerative diseases. Trends Mol Med 20:694–703
Saravanan KM, Selvaraj S (2012) Search for identical octapeptides in unrelated proteins: structural plasticity revisited. Biopolymers 98:11–26
Schellenberg MJ, Ritchie DB, Wu T, Markin CJ, Spyracopoulos L, MacMillan AM (2010) Context-dependent remodeling of structure in two large protein fragments. J Mol Biol 402:720–730
Schellenberg MJ, Wu T, Ritchie DB, Fica S, Staley JP, Atta KA, LaPointe P, MacMillan AM (2013) A conformational switch in PRP8 mediates metal ion coordination that promotes pre-mRNA exon ligation. Nat Struct Mol Biol 20:728–734
Schipper HM (2011) Heme oxygenase-1 in Alzheimer disease: a tribute to Moussa Youdim. J Neural Transm (Vienna) 118:381–387
Seo YH (2015) Small molecule inhibitors to disrupt protein-protein interactions of heat shock protein 90 chaperone machinery. J Cancer Prev 20:5–11
Shangary S, Wang S (2008) Targeting the MDM2-p53 interaction for cancer therapy. Clin Cancer Res 14:5318–5324
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 13:2498–2504
Sigalov AB (2016) Structural biology of intrinsically disordered proteins: revisiting unsolved mysteries. Biochimie 125:112–118
Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27:431–432
Sprenger J, Lynn Fink J, Karunaratne S, Hanson K, Hamilton NA, Teasdale RD (2008) LOCATE: a mammalian protein subcellular localization database. Nucleic Acids Res 36:D230–D233
Stanga S, Lanni C, Govoni S, Uberti D, D’Orazi G, Racchi M (2010) Unfolded p53 in the pathogenesis of Alzheimer’s disease: is HIPK2 the link? Aging (Albany NY) 2:545–554
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, von Mering C (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43:D447–D452
Takano K, Katagiri Y, Mukaiyama A, Chon H, Matsumura H, Koga Y, Kanaya S (2007) Conformational contagion in a protein: structural properties of a chameleon sequence. Proteins 68:617–625
Tidow H, Lauber T, Vitzithum K, Sommerhoff CP, Rosch P, Marx UC (2004) The solution structure of a chimeric LEKTI domain reveals a chameleon sequence. Biochemistry 43:11238–11247
Uversky VN, Oldfield CJ, Dunker AK (2008) Intrinsically disordered proteins in human diseases: introducing the D2 concept. Annu Rev Biophys 37:215–246
van der Lee R, Buljan M, Lang B, Weatheritt RJ, Daughdrill GW, Dunker AK, Fuxreiter M, Gough J, Gsponer J, Jones DT, Kim PM, Kriwacki RW, Oldfield CJ, Pappu RV, Tompa P, Uversky VN, Wright PE, Babu MM (2014) Classification of intrinsically disordered regions and proteins. Chem Rev 114:6589–6631
Wang W, Hu Y (2012) Small molecule agents targeting the p53-MDM2 pathway for cancer therapy. Med Res Rev 32:1159–1196
Wang J, Niemevz F, Lad L, Huang L, Alvarez DE, Buldain G, Poulos TL, de Montellano PR (2004) Human heme oxygenase oxidation of 5- and 15-phenylhemes. J Biol Chem 279:42593–42604
Xu Y, Ding Y, Li X, Wu X (2015) Cystatin C is a disease-associated protein subject to multiple regulation. Immunol Cell Biol 93:442–451
Young JC, Moarefi I, Hartl FU (2001) Hsp90: a specialized but essential protein-folding tool. J Cell Biol 154:267–273
Zhang Y, Thiele I, Weekes D, Li Z, Jaroszewski L, Ginalski K, Deacon AM, Wooley J, Lesley SA, Wilson IA, Palsson B, Osterman A, Godzik A (2009) Three-dimensional structural view of the central metabolic network of Thermotoga maritima. Science 325:1544–1549
Acknowledgements
The authors would like to thank Nasibeh Khayyer for her valuable advice on the work. The work was done at the Bioinformatics Lab of the National Institute of Genetic Engineering and Biotechnology Grant No. 303 and at the Bioinformatics Group of University of Tehran.
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Bahramali, G., Goliaei, B., Minuchehr, Z. et al. A network biology approach to understanding the importance of chameleon proteins in human physiology and pathology. Amino Acids 49, 303–315 (2017). https://doi.org/10.1007/s00726-016-2361-6
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DOI: https://doi.org/10.1007/s00726-016-2361-6