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Computational Approaches for Drug Target Identification

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Computer-Aided Drug Design

Abstract

It is assumed that due to the enormous investment in terms of time, money, human volunteers, and other resources, sometimes failure at the later stage mostly put pharmaceutical companies on the back foot. For the last two decades, pharmaceutical companies felt that the traditional drug designing process should be optimized to avoid huge financial loss and save time. Thus, despite its limitations, the use of computer-aided drug design (CADD) techniques in drug discovery and development process is successful. CADD approaches support almost all phases of the drug designing process, including drug target identification, lead identification, optimization of leads, and simulations. Drug target identification and characterization is a first and most essential step that begins with identifying the function of a possible molecular target (gene/protein) and its role in the disease. The availability of the huge amount of molecular data, i.e., big data, for human as well as pathogens with applications of knowledge-based data mining approaches can provide a list of probable drug targets which further can be validated through experiments can save time and cost of pharmaceutical companies and boost their research towards the development of new drugs. This chapter focuses on the computational approaches for drug target identification, which play a crucial role in the drug discovery and development process.

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References

  • Ba-Alawi W, Soufan O, Essack M, Kalnis P, Bajic VB (2016) DASPfind: new efficient method to predict drug-target interactions. J Cheminf 8:15

    Article  CAS  Google Scholar 

  • Behar M, Barken D, Werner SL, Hoffmann A (2013) The dynamics of signaling as a pharmacological target. Cell 155(2):448–461

    Article  CAS  PubMed  Google Scholar 

  • Bottegoni G, Favia AD, Recanatini M, Cavalli A (2012) The role of fragment-based and computational methods in polypharmacology. Drug Discov Today 17(1–2):23–34

    Article  CAS  PubMed  Google Scholar 

  • Chartier M, Adriansen E, Najmanovich R (2016) IsoMIF Finder: online detection of binding site molecular interaction field similarities. Bioinformatics 32(4):621–623

    Article  CAS  PubMed  Google Scholar 

  • Chen H, Zhang Z (2013) A semi-supervised method for drug-target interaction prediction with consistency in networks. PLoS One 8(5):e62975

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chen X, Ji ZL, Chen YZ (2002) TTD: therapeutic target database. Nucleic Acids Res 30(1):412–415

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chen L, Yang J, Yu J, Yao Z, Sun L, Shen Y, Jin Q (2005) VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res 33:D325–D328

    Article  CAS  PubMed  Google Scholar 

  • Cheng F, Liu C, Jiang J et al (2012) Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol 8(5):e1002503

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Clough E, Barrett T (2016) The gene expression omnibus database. Methods Mol Biol 1418:93–110

    Article  PubMed  PubMed Central  Google Scholar 

  • Cohen P (2002) Protein kinases—the major drug targets of the twenty-first century? Nat Rev Drug Discov 1(4):309–315

    Article  CAS  PubMed  Google Scholar 

  • De-Alarcón PA, Pascual-Montano A, Gupta A, Carazo JM (2002) Modeling shape and topology of low-resolution density maps of biological macromolecules. Biophys J 83(2):619–632

    Article  PubMed  PubMed Central  Google Scholar 

  • Dersch P, Khan MA, Mühlen S, Görke B (2017) Roles of regulatory RNAs for antibiotic resistance in bacteria and their potential value as novel drug targets. Front Microbiol 8:803

    Article  PubMed  PubMed Central  Google Scholar 

  • Docherty AJ, Crabbe T, O’Connell JP, Groom CR (2003) Proteases as drug targets. Biochem Soc Symp 70:147–161

    Article  CAS  Google Scholar 

  • Fauman EB, Rai BK, Huang ES (2011) Structure-based druggability assessment--identifying suitable targets for small molecule therapeutics. Curr Opin Chem Biol 15(4):463–468

    Article  CAS  PubMed  Google Scholar 

  • Finan C, Gaulton A, Kruger FA, Lumbers RT, Shah T, Engmann J, Galver L, Kelley R, Karlsson A, Santos R, Overington JP, Hingorani AD, Casas JP (2017) The druggable genome and support for target identification and validation in drug development. Sci Transl Med 9(383):eaag1166

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Gao Z, Li H, Zhang H, Liu X, Kang L, Luo X, Zhu W, Chen K, Wang X, Jiang H (2008) PDTD: a web-accessible protein database for drug target identification. BMC Bioinf 9:104

    Article  CAS  Google Scholar 

  • Gashaw I, Ellinghaus P, Sommer A, Asadullah K (2011) What makes a good drug target? Drug Discov Today 16(23–24):1037–1043

    Article  CAS  PubMed  Google Scholar 

  • Ghersi D, Sanchez R (2011) Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures. J Struct Funct Genom 12(2):109–117

    Article  CAS  Google Scholar 

  • Gupta S, Mishra M, Sen N, Parihar R, Dwivedi GR, Khan F, Sharma A (2011) DbMDR: a relational database for multidrug resistance genes as potential drug targets. Chem Biol Drug Des 78(4):734–738

    Article  CAS  PubMed  Google Scholar 

  • Haider S, Ballester B, Smedley D, Zhang J, Rice P, Kasprzyk A (2009) BioMart Central Portal—unified access to biological data. Nucleic Acids Res 37:W23–W27

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Halgren TA (2009) Identifying and characterizing binding sites and assessing druggability. J Chem Inf Model 49(2):377–389

    Article  CAS  PubMed  Google Scholar 

  • Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4(11):682–690

    Article  CAS  PubMed  Google Scholar 

  • Hughes JP, Rees S, Kalindjian SB, Philpott KL (2011) Principles of early drug discovery. Br J Pharmacol 162(6):1239–1249

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hussein HA, Borrel A, Geneix C, Petitjean M, Regad L, Camproux AC (2015) PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins. Nucleic Acids Res 3(W1):W436–W442

    Article  CAS  Google Scholar 

  • Imoto S, Tamada Y, Savoie CJ, Miyano S (2007) Analysis of gene networks for drug target discovery and validation. Methods Mol Biol 360:33–56

    CAS  PubMed  Google Scholar 

  • Kaczorowski GJ, McManus OB, Priest BT, Garcia ML (2008) Ion channels as drug targets: the next GPCRs. J Gen Physiol 131(5):399–405

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kanehisa M, Goto S (2000) KEGG: Kyoto Encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Katara P (2013) Role of bioinformatics and pharmacogenomics in drug discovery and development process. Netw Model Anal Health Inform Bioinf 2(4):225–230

    Article  Google Scholar 

  • Katara P (2017) Stem cell: a key to solving the drug screening enigma. In: Verma V, Singh MP, Kumar M (eds) Stem cells from culture dish to clinic. Nova Science, New York, pp 257–268

    Google Scholar 

  • Katara P, Grover A, Kuntal H, Sharma V (2011) In silico prediction of drug targets in Vibrio cholerae. Protoplasma 248(4):799–804

    Article  CAS  PubMed  Google Scholar 

  • Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT (2016) Computational approaches in target identification and drug discovery. Comput Struct Biotechnol J 14:177–184

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Keum J, Nam H (2017) SELF-BLM: prediction of drug-target interactions via self-training SVM. PLoS One 12(2):e0171839

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kim B, Jo J, Han J, Park C, Lee H (2017) In silico re-identification of properties of drug target proteins. BMC Bioinf 18(Suppl 7):248

    Article  CAS  Google Scholar 

  • Klaeger S, Heinzlmeir S, Wilhelm M et al (2017) The target landscape of clinical kinase drugs. Science 358(6367):eaan4368

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kotlyar M, Fortney K, Jurisica I (2012) Network-based characterization of drug-regulated genes, drug targets, and toxicity. Methods 57(4):499–507

    Article  CAS  PubMed  Google Scholar 

  • Krasowski A, Muthas D, Sarkar A, Schmitt S, Brenk R (2011) DrugPred: a structure-based approach to predict protein druggability developed using an extensive nonredundant data set. J Chem Inf Model 51(11):2829–2842

    Article  CAS  PubMed  Google Scholar 

  • Kumar P, Kaalia R, Srinivasan A, Ghosh I (2018) Multiple target-based pharmacophore design from active site structures. SAR QSAR Environ Res 29(1):1–19

    Article  PubMed  Google Scholar 

  • Kurbatova N, Chartier M, Zylber MI, Najmanovich R (2013) IsoCleft Finder—a web-based tool for the detection and analysis of protein binding-site geometric and chemical similarities. F1000Res 2:117

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Lamb J, Crawford ED, Peck D et al (2006) The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313(5795):1929–1935

    Article  CAS  PubMed  Google Scholar 

  • Lipinski CA (2000) Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 44:235–249

    Article  CAS  PubMed  Google Scholar 

  • Lu T, Yao B, Zhang C (2012) DFVF: database of fungal virulence factors. Database 2012:bas032

    PubMed  PubMed Central  Google Scholar 

  • Magariños MP, Carmona SJ, Crowther GJ et al (2012) TDR targets: a chemogenomics resource for neglected diseases. Nucleic Acids Res 40:D1118–D1127

    Article  PubMed  CAS  Google Scholar 

  • McInnes C (2007) Virtual screening strategies in drug discovery. Curr Opin Chem Biol 11(5):494–502

    Article  CAS  PubMed  Google Scholar 

  • Melak T, Gakkhar S (2015) Comparative genome and network centrality analysis to identify drug targets of Mycobacterium tuberculosis H37Rv. Biomed Res Int 2015:1. https://doi.org/10.1155/2015/212061

    Article  CAS  Google Scholar 

  • Nisius B, Sha F, Gohlke H (2012) Structure-based computational analysis of protein binding sites for function and druggability prediction. J Biotechnol 159(3):123–134

    Article  CAS  PubMed  Google Scholar 

  • Oughtred R, Stark C, Breitkreutz BJ et al (2019) The BioGRID interaction database: 2019 update. Nucleic Acids Res 47(D1):D529–D541

    Article  CAS  PubMed  Google Scholar 

  • Pinto JP, Machado RS, Xavier JM, Futschik ME (2014) Targeting molecular networks for drug research. Front Genet 5:160

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Ramsay RR, Popovic-Nikolic MR, Nikolic K, Uliassi E, Bolognesi ML (2018) A perspective on multi-target drug discovery and design for complex diseases. Clin Transl Med 7(1):3

    Article  PubMed  PubMed Central  Google Scholar 

  • Rayhan F, Ahmed S, Shatabda S et al (2017) iDTI-ESBoost: identification of drug target interaction using evolutionary and structural features with boosting. Sci Rep 7(1):17731

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Schalon C, Surgand JS, Kellenberger E, Rognan D (2008) A simple and fuzzy method to align and compare druggable ligand-binding sites. Proteins 71(4):1755–1778

    Article  CAS  PubMed  Google Scholar 

  • Schmidtke P, Le Guilloux V, Maupetit J, Tufféry P (2010) Fpocket: online tools for protein ensemble pocket detection and tracking. Nucleic Acids Res 38:W582–W589

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Seal A, Wild DJ (2018) Netpredictor: R and shiny package to perform drug-target network analysis and prediction of missing links. BMC Bioinf 19(1):265

    Article  CAS  Google Scholar 

  • Shekhar C (2008) In silico pharmacology: computer-aided methods could transform drug development. Chem Biol 15(5):413–414

    Article  CAS  PubMed  Google Scholar 

  • Shin WH, Christoffer CW, Kihara D (2017) In silico structure-based approaches to discover protein-protein interaction-targeting drugs. Methods 131:22–32

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shulman-Peleg A, Shatsky M, Nussinov R, Wolfson HJ (2008) MultiBind and MAPPIS: webservers for multiple alignment of protein 3D-binding sites and their interactions. Nucleic Acids Res 36:W260–W264

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sridhar P, Song B, Kahveci T, Ranka S (2008) Mining metabolic networks for optimal drug targets. Pac Symp Biocomput 13:291–302

    Google Scholar 

  • Sriram K, Insel PA (2018) G protein-coupled receptors as targets for approved drugs: how many targets and how many drugs? Mol Pharmacol 93(4):251–258

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sugaya N, Furuya T (2011) Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions. BMC Bioinf 12:50

    Article  CAS  Google Scholar 

  • Vasaikar S, Bhatia P, Bhatia PG, Chu Yaiw K (2016) Complementary approaches to existing target based drug discovery for identifying novel drug targets. Biomedicines 4(4):E27

    Article  PubMed  Google Scholar 

  • Verma Y, Yadav A, Katara P (2020) Mining of cancer core-genes and their protein interactome using expression profiling based PPI network approach. Gene Rep 18:100583

    Article  Google Scholar 

  • Wang W, Yang S, Li J (2013) Drug target predictions based on heterogeneous graph inference. Biocomputing 2013:53–64

    Google Scholar 

  • Wishart DS, Knox C, Guo AC et al (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36:D901–D906

    Article  CAS  PubMed  Google Scholar 

  • Xia J, Sinelnikov IV, Han B, Wishart DS (2015) MetaboAnalyst 3.0—making metabolomics more meaningful. Nucleic Acids Res 43(W1):W251–W257

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xu Y, Wang S, Hu Q et al (2018) CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction. Nucleic Acids Res 46(W1):W374–W379

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yang Y, Han L, Yuan Y, Li J, Hei N, Liang H (2014) Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types. Nat Commun 5:3231

    Article  PubMed  CAS  Google Scholar 

  • Zauhar RJ, Moyna G, Tian L, Li Z, Welsh WJ (2003) Shape signatures: a new approach to computer-aided ligand- and receptor-based drug design. J Med Chem 46(26):5674–5690

    Article  CAS  PubMed  Google Scholar 

  • Zhang R, Ou HY, Zhang CT (2004) DEG: a database of essential genes. Nucleic Acids Res 32:D271–D272

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zheng X, Gan L, Wang E, Wang J (2013) Pocket-based drug design: exploring pocket space. AAPS J 15(1):228–241

    Article  CAS  PubMed  Google Scholar 

  • Zhou CE, Smith J, Lam M, Zemla A, Dyer MD, Slezak T (2007) MvirDB—a microbial database of protein toxins, virulence factors and antibiotic resistance genes for bio-defence applications. Nucleic Acids Res 35:D391–D394

    Article  CAS  PubMed  Google Scholar 

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The author declares that there are no competing interests.

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Authors and Affiliations

  1. Computational Omics Lab, Centre of Bioinformatics, University of Allahabad, Prayagraj, India

    Pramod Katara

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  1. Pramod Katara
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Editors and Affiliations

  1. Department of Biotechnology, Institute of Biosciences and Biotechnology, Chhatrapati Shahu Ji Maharaj University, Kanpur, Uttar Pradesh, India

    Dev Bukhsh Singh

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Katara, P. (2020). Computational Approaches for Drug Target Identification. In: Singh, D.B. (eds) Computer-Aided Drug Design. Springer, Singapore. https://doi.org/10.1007/978-981-15-6815-2_8

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