Abstract
Regulation of cancer and its development is a complex process that combines an aggregation of numerous mutations with dynamic changes and a complicated cross-talk among various types of cells involved in tumours. Poor understanding of the underlying cellular mechanisms involving various biological molecules associated with cancer has resulted in confusing prognosis. Thus, interpretation of relationships between these biological molecules, networks and pathways is necessary to understand the intricacies of cancer biology. Emergence of a specialised branch of biology, namely, bioinformatics, has enabled cancer biologists to bridge the gap between genomics and proteomics. One of its subdomains is structural bioinformatics, which includes techniques such as molecular modelling, high-throughput docking, mutation analysis, network modelling and drug designing. Robustness of the algorithms and pipelines involving these techniques has made efficient handling of heterogeneous and ever-evolving tumour data possible. In this chapter, we have tried to elucidate the application of structural bioinformatics in the analysis of cancer signalling pathways.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Akavia UD, Litvin O, Kim J, Sanchez-Garcia F, Kotliar D, Causton HC et al (2010) An integrated approach to uncover drivers of cancer. Cell 143(6):1005–1017
Altshuler DL, Durbin RM, Abecasis GR, Bentley DR, Chakravarti A, Clark AG et al (2010) A map of human genome variation from population-scale sequencing. Nature 467(7319):1061–1073
Arnold K, Bordoli L, Kopp J, Schwede T (2006 Jan) The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics 22(2):195–201
Baasiri RA, Glasser SR, Steffen DL, Wheeler DA (1999) The breast cancer gene database: a collaborative information resource. Oncogene 18:7958
Barabási AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101
Bejugam PR, Kuppili RR, Singh N, Gadewal N, Chaganti LK, Sastry GM et al (2013) Allosteric regulation of serine protease HtrA2 through novel non-canonical substrate binding pocket. Srinivasula SM, editor. PLoS One [Internet] 8(2):e55416. https://doi.org/10.1371/journal.pone.0055416
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H et al (2000 Jan) The protein data bank. Nucleic Acids Res 28(1):235–242
Béroud C, Soussi T (1998) p53 gene mutation: software and database. Nucleic Acids Res 26(1):200–204
Bonnet E, Tatari M, Joshi A, Michoel T, Marchal K, Berx G et al (2010) Module network inference from a cancer gene expression data set identifies MicroRNA regulated modules. PLoS One 5(4):e10162
Bourne PE, Weissig H (2003) Structural bioinformatics. Wiley-Liss, Hoboken, 649 p
Buscà R, Pouysségur J, Lenormand P (2016) ERK1 and ERK2 map kinases: specific roles or functional redundancy? Front Cell Dev Biol 4:53
Carter H, Chen S, Isik L, Tyekucheva S, Velculescu VE, Kinzler KW et al (2009) Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations. Cancer Res 69:6660
Caspi R, Billington R, Ferrer L, Foerster H, Fulcher CA, Keseler IM et al (2016) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 42:D459–D471
Chandrasekaran G, Hwang EC, Kang TW, Kwon DD, Park K, Lee J-J et al (2017 Dec) Computational modeling of complete HOXB13 protein for predicting the functional effect of SNPs and the associated role in hereditary prostate cancer. Sci Rep 7(1):43830
Cheung KH, Osier MV, Kidd JR, Pakstis AJ, Miller PL, Kidd KK (2000) ALFRED: an allele frequency database for diverse populations and DNA polymorphisms. Nucleic Acids Res 28:361
Ciriello G, Cerami E, Aksoy BA, Sander C, Schultz N (2013) Using MEMo to discover mutual exclusivity modules in cancer. Curr Protoc Bioinforma 41(1):8.17.1–8.17.12
Dees ND, Zhang Q, Kandoth C, Wendl MC, Schierding W, Koboldt DC et al (2012) MuSiC: identifying mutational significance in cancer genomes. Genome Res 22(8):1589–1598
Demir E, Cary MP, Paley S, Fukuda K, Lemer C, Vastrik I et al (2010) The BioPAX community standard for pathway data sharing. Nat Biotechnol 28(9):935–942
Eswar N, Webb B, Marti-Renom MA, Madhusudhan MS, Eramian D, Shen M et al (2006 Sep) Comparative protein structure modeling using modeller. Curr Protoc Bioinforma 15(1):5.6.1–5.6.30
Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P et al (2018) The reactome pathway knowledgebase. Nucleic Acids Res 44:D481–D487
Finch AR, Caunt CJ, Perrett RM, Tsaneva-Atanasova K, McArdle CA (2012) Dual specificity phosphatases 10 and 16 are positive regulators of EGF-stimulated ERK activity: indirect regulation of ERK signals by JNK/p38 selective MAPK phosphatases. Cell Signal 24:1002
Fu LJ, Wang B (2013) Investigation of the hub genes and related mechanism in ovarian cancer via bioinformatics analysis. J Ovarian Res 6:92
Giannelli F, Green PM, Sommer SS, Poon MC, Ludwig M, Schwaab R et al (1998) Haemophilia B: database of point mutations and short additions and deletions – eighth edition. Nucleic Acids Res 26:265
Gómez J, García LJ, Salazar GA, Villaveces J, Gore S, García A et al (2013) BioJS: an open source JavaScript framework for biological data visualization. Bioinformatics 29(8):1103–1104
González-Pérez A, López-Bigas N (2011) Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score, Condel. Am J Hum Genet 88:440
Gonzalez-Perez A, Mustonen V, Reva B, Ritchie GRS, Creixell P, Karchin R et al (2013) Computational approaches to identify functional genetic variants in cancer genomes. Nat Methods 10(8):723–729
Hamosh A, Scott AF, Amberger J, Valle D, McKusick VA (2000) Online Mendelian Inheritance in Man (OMIM). Hum Mutat 15:57
Huang YJ, Hang D, Lu LJ, Tong L, Gerstein MB, Montelione GT (2008 Oct) Targeting the human cancer pathway protein interaction network by structural genomics. Mol Cell Proteomics 7(10):2048–2060
Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19:524
Ideker T, Krogan NJ (2012) Differential network biology. Mol Syst Biol 8:565
Imai K, Mitaku S (2005) Mechanisms of secondary structure breakers in soluble proteins. Biophysics (Oxf) 1:55–65
Isberg V, Mordalski S, Munk C, Rataj K, Harpsøe K, Hauser AS et al (2016) GPCRdb: an information system for G protein-coupled receptors. Nucleic Acids Res 26(1):277–281
Isserlin R, El-Badrawi RA, Badery GD (2011) The biomolecular interaction network database in PSI-MI 2.5. Database 2011:baq037
Ivanov AA, Khuri FR, Fu H (2013) Targeting protein-protein interactions as an anticancer strategy. Trends Pharmacol Sci 34(7):393–400
Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J et al (2009) STRING 8 – a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res 37:D412
Jiang Y, Xu C (2009) Evaluation model for breast cancer susceptibly gene and its implementation using cytoscape. In: Proceedings of the 2009 2nd international conference on Biomedical Engineering and Informatics, BMEI 2009, 1–5 p
Kalari S, Pfeifer GP (2010) Identification of driver and passenger DNA methylation in Cancer by epigenomic analysis. Adv Genet 70:277
Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M (2016) KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44:D457–D462
Kann MG (2007 Jun) Protein interactions and disease: computational approaches to uncover the etiology of diseases. Brief Bioinform 8(5):333–346
Kar G, Gursoy A, Keskin O (2009 Dec) Human cancer protein-protein interaction network: a structural perspective. PLoS Comput Biol 5(12):e1000601
Kawabata T, Ota M, Nishikawa K (1999) The protein mutant database. Nucleic Acids Res 35:D690–D695
Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJE (2015 May) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10(6):845–858
Keskin O, Gursoy A, Ma B, Nussinov R (2008) Principles of protein-protein interactions: what are the preferred ways for proteins to interact? Chem Rev 108:1225–1244
Kim DE, Chivian D, Baker D (2004) Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res 32(Web Server):W526–W531
Koboldt DC, Chen K, Wylie T, Larson DE, McLellan MD, Mardis ER et al (2009) VarScan: variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics 25(17):2283–2285
Kono N, Arakawa K, Tomita M (2006) MEGU: pathway mapping web-service based on KEGG and SVG. In Silico Biol 6(6):621–625
Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C et al (2017) The ClusPro web server for protein-protein docking. Nat Protoc 12(2):255–278
Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A et al (2013) Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499:214
Leung A, Bader GD, Reimand J (2014) HyperModules: identifying clinically and phenotypically significant network modules with disease mutations for biomarker discovery. Bioinformatics 30(15):2230–2232
Levinthal C, Wodak SJ, Kahn P, Dadivanian AK (1975 Apr) Hemoglobin interaction in sickle cell fibers. I: theoretical approaches to the molecular contacts. Proc Natl Acad Sci U S A 72(4):1330–1334
Lloyd CM, Lawson JR, Hunter PJ, Nielsen PF (2008) The CellML model repository. Bioinformatics 24(18):2122–2123
Luo W, Brouwer C (2013) Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics 29(14):1830–1831
Martin A, Ochagavia ME, Rabasa LC, Miranda J, Fernandez-de-Cossio J, Bringas R (2010) BisoGenet: a new tool for gene network building, visualization and analysis. BMC Bioinformatics 11(1):91
Miosge LA, Field MA, Sontani Y, Cho V, Johnson S, Palkova A et al (2015) Comparison of predicted and actual consequences of missense mutations. Proc Natl Acad Sci 112(37):E5189–E5198
Mitra K, Carvunis AR, Ramesh SK, Ideker T (2013) Integrative approaches for finding modular structure in biological networks. Nat Rev Genet 14(10):719–732
Modell SM, Lehmann MH (2006) The long QT syndrome family of cardiac ion channelopathies: a HuGE review. Genet Med 8:143
Montojo J, Zuberi K, Rodriguez H, Bader GD, Morris Q (2014) GeneMANIA: Fast gene network construction and function prediction for Cytoscape. F1000Research 3:153
Moult J, Pedersen JT, Judson R, Fidelis K (1995) A large-scale experiment to assess protein structure prediction methods. Proteins Struct Funct Genet 23(3):ii–iv
Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A (2018 Mar) Critical assessment of methods of protein structure prediction (CASP)—round XII. Proteins Struct Funct Bioinforma 86:7–15
Mushegian AR (2007) How many protein families are there? Found Comp Genomics:139–150
Ng PC, Henikoff S (2003) SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 31(13):3812–3814
Pavlopoulos GA, Secrier M, Moschopoulos CN, Soldatos TG, Kossida S, Aerts J et al (2011) Using graph theory to analyze biological networks. BioData Mining 4(1):1–27
Pe’er D, Regev A, Elidan G, Friedman N (2001) Inferring subnetworks from perturbed expression profiles. Bioinformatics 17(1):S215–S224
Petrey D, Honig B (2014) Structural bioinformatics of the interactome. Annu Rev Biophys 43:193–210
Pierce BG, Wiehe K, Hwang H, Kim B-H, Vreven T, Weng Z (2014) ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers. Bioinformatics 30(12):1771–1773
Platzer KEB, Momany FA, Scheraga HA (1972) Conformational energy calculations of enzyme-substrate interactions. II. Computation of the binding energy for substrates in the active site of α-chymotrypsin. Int J Pept Protein Res 4(3):201–219
Pon JR, Marra MA (2015) Driver and passenger mutations in cancer. Annu Rev Pathol Mech Dis 10(1):25–50
Sachidanandam R, Weissman D, Schmidt SC, Kakol JM, Stein LD, Marth G et al (2001) A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature 409(6822):928–933
Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC et al (2018) Oncogenic signaling pathways in the cancer genome atlas. Cell 173(2):321–337.e10
Schäfer J, Strimmer K (2005) An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21(6):754–764
Schwarz JM, Rödelsperger C, Schuelke M, Seelow D (2010) MutationTaster evaluates disease-causing potential of sequence alterations. Nat Method 7(8):575–576
Sever R, Brugge JS (2015) Signal transduction in cancer. Cold Spring Harb Perspect Med 5(4)
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al (2003) Cytoscape: a software Environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504
Sheinerman FB, Norel R, Honig B (2000) Electrostatic aspects of protein-protein interactions. Curr Opin Struct Biol 10:153
Slabinski L, Jaroszewski L, Rodrigues APC, Rychlewski L, Wilson IA, Lesley SA et al (2007) The challenge of protein structure determination-lessons from structural genomics. Protein Sci 16(11):2472–2482
Slenter DN, Kutmon M, Hanspers K, Riutta A, Windsor J, Nunes N et al (2018) WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Res 46:D661–D667
Smigielski EM (2000) dbSNP: a database of single nucleotide polymorphisms. Nucleic Acids Res 28(1):352–355
Stenson PD, Mort M, Ball EV, Howells K, Phillips AD, Cooper DN et al (2009) The human gene mutation database: 2008 update. Genome Med 1:13
Tabangin ME, Woo JG, Martin LJ (2009) The effect of minor allele frequency on the likelihood of obtaining false positives. BMC Proc 3(S7):S41
Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R et al (2003) PANTHER: a library of protein families and subfamilies indexed by function. Genome Res 13:2129–2141
Torchala M, Moal IH, Chaleil RAG, Fernandez-Recio J, Bates PA (2013 Mar) SwarmDock: a server for flexible protein–protein docking. Bioinformatics 29(6):807–809
Torti D, Trusolino L (2011) Oncogene addiction as a foundational rationale for targeted anti-cancer therapy: promises and perils. EMBO Mol Med 3:623
Vakser IA (2014) Protein-protein docking: from interaction to interactome. Biophys J Biophys Soc 107:1785–1793
Vallat B, Madrid-Aliste C, Fiser A (2015) Modularity of protein folds as a tool for template-free modeling of structures. Marti-Renom MA, editor. PLOS Comput Biol 11(8):e1004419
van Baal S, Kaimakis P, Phommarinh M, Koumbi D, Cuppens H, Riccardino F et al (2007) FINDbase: a relational database recording frequencies of genetic defects leading to inherited disorders worldwide. Nucleic Acids Res
Varret M, Rabés JP, Thiart R, Kotze MJ, Baron H, Cenarro A et al (1998) LDLR database (second edition): new additions to the database and the software, and results of the first molecular analysis. Nucleic Acids Res 26:248
Wang Z-X (1996 Oct) How many fold types of protein are there in nature? Proteins Struct Funct Genet 26(2):186–191
Wang X, Gulbahce N, Yu H (2011a) Network-based methods for human disease gene prediction. Brief Funct Genomics 10(5):280–293
Wang K, Kan J, Yuen ST, Shi ST, Chu KM, Law S et al (2011b) Exome sequencing identifies frequent mutation of ARID1A in molecular subtypes of gastric cancer. Nat Genet 43:1219
Wass MN, Sternberg MJM (2008) ConFunc – functional annotation in the twilight zone. Bioinformatics 24(6):798–806
Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y (2015 Jan) The I-TASSER suite: protein structure and function prediction. Nat Methods 12(1):7–8
Zhang X, Wang Y, Wang J, Sun F, Wang J, Wang J et al (2016) Protein-protein interactions among signaling pathways may become new therapeutic targets in liver cancer (review). Oncol Rep 35(2):625–638
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Doshi, J., Dutta, S., Bose, K. (2019). Analysing Cancer Signalling Pathways: A Structural Bioinformatics Approach. In: Bose, K., Chaudhari, P. (eds) Unravelling Cancer Signaling Pathways: A Multidisciplinary Approach. Springer, Singapore. https://doi.org/10.1007/978-981-32-9816-3_11
Download citation
DOI: https://doi.org/10.1007/978-981-32-9816-3_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9815-6
Online ISBN: 978-981-32-9816-3
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)