Abstract
Fast and reliable evaluation of the hydrogen bond potential energy has a significant impact in the drug design and development since it allows the assessment of large databases of organic molecules in virtual screening projects focused on a protein of interest. Semi-empirical force fields implemented in molecular docking programs make it possible the evaluation of protein-ligand binding affinity where the hydrogen bond potential is a common term used in the calculation. In this chapter, we describe the concepts behind the programs used to predict hydrogen bond potential energy employing semi-empirical force fields as the ones available in the programs AMBER, AutoDock4, TreeDock, and ReplicOpter. We described here the 12-10 potential and applied it to evaluate the binding affinity for an ensemble of crystallographic structures for which experimental data about binding affinity are available.
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References
Pauling L, Corey RB, Branson HR (1951) The structure of proteins: two hydrogen-bonded helical configurations of the polypeptide chain. Proc Natl Acad Sci U S A 37:205–211
Pauling L, Corey RB (1951) Atomic coordinates and structure factors for two helical configurations of polypeptide chains. Proc Natl Acad Sci U S A 37:235–240
Pauling L, Corey RB (1951) The structure of synthetic polypeptides. Proc Natl Acad Sci U S A 37:241–250
Pauling L, Corey RB (1951) The pleated sheet, a new layer configuration of polypeptide chains. Proc Natl Acad Sci U S A 37:251–256
Kendrew JC, Bodo G, Dintzis HM, Parrish RG, Wyckoff H, Phillips DC (1958) A three-dimensional model of the myoglobin molecule obtained by X-ray analysis. Nature 181:662–666
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H et al (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242
Berman HM, Battistuz T, Bhat TN, Bluhm WF, Bourne PE, Burkhardt K et al (2002) The Protein Data Bank. Acta Crystallogr D Biol Crystallogr 58:899–907
Westbrook J, Feng Z, Chen L, Yang H, Berman HM (2003) The protein data Bank and structural genomics. Nucleic Acids Res 31:489–491
Hu L, Benson ML, Smith RD, Lerner MG, Carlson HA (2005) Binding MOAD (Mother Of All Databases). Proteins 60:333–340
Liu T, Lin Y, Wen X, Jorrisen RN, Gilson MK (2007) BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35:198–201
Wang R, Fang X, Lu Y, Wang S (2004) The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J Med Chem 47:2977–2980
Murray AW (1994) Cyclin-dependent kinases: regulators of the cell cycle and more. Chem Biol 1:191–195
Morgan DO (1995) Principles of CDK regulation. Nature 374:131–134
Volkart PA, Bitencourt-Ferreira G, Souto AA, de Azevedo WF (2019) Cyclin-dependent kinase 2 in cellular senescence and cancer. A structural and functional review. Curr Drug Targets 20(7):716–726. https://doi.org/10.2174/1389450120666181204165344
Levin NM, Pintro VO, de Ávila MB, de Mattos BB, De Azevedo WF Jr (2017) Understanding the structural basis for inhibition of Cyclin-dependent kinases. New pieces in the molecular puzzle. Curr Drug Targets 18:1104–1111
de Ávila MB, Xavier MM, Pintro VO, de Azevedo WF (2017) Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2. Biochem Biophys Res Commun 494:305–310
Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo WF Jr (2018) Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem 235:1–8
de Azevedo WF Jr (2016) Opinion paper: targeting multiple Cyclin-dependent kinases (CDKs): a new strategy for molecular docking studies. Curr Drug Targets 17:2
Perez PC, Caceres RA, Canduri F, de Azevedo WF Jr (2009) Molecular modeling and dynamics simulation of human cyclin-dependent kinase 3 complexed with inhibitors. Comput Biol Med 39:130–140
Canduri F, Perez PC, Caceres RA, de Azevedo WF Jr (2008) CDK9 a potential target for drug development. Med Chem 4:210–218
Dos Santos NFP, Canduri F (2018) The emerging picture of CDK11: genetic, functional and medicinal aspects. Curr Med Chem 25:880–888
Paparidis NF, Durvale MC, Canduri F (2017) The emerging picture of CDK9/P-TEFb: more than 20 years of advances since PITALRE. Mol BioSyst 13:246–276
Leopoldino AM, Canduri F, Cabral H, Junqueira M, de Marqui AB, Apponi LH et al (2006) Expression, purification, and circular dichroism analysis of human CDK9. Protein Expr Purif 47:614–620
Krystof V, Cankar P, Frysová I, Slouka J, Kontopidis G, Dzubák P et al (2006) 4-arylazo-3,5-diamino-1H-pyrazole CDK inhibitors: SAR study, crystal structure in complex with CDK2, selectivity, and cellular effects. J Med Chem 49:6500–6509
Canduri F, de Azevedo WF Jr (2005) Structural basis for interaction of inhibitors with Cyclin-dependent kinase 2. Curr Comput Aided Drug Des 1:53–64
Canduri F, Uchoa HB, de Azevedo WF Jr (2004) Molecular models of cyclin-dependent kinase 1 complexed with inhibitors. Biochem Biophys Res Commun 324:661–666
De Azevedo WF Jr, Gaspar RT, Canduri F, Camera JC Jr, Da Silveira NJF (2002) Molecular model of cyclin-dependent kinase 5 complexed with roscovitine. Biochem Biophys Res Commun 297:1154–1158
de Azevedo WF Jr, Canduri F, da Silveira NJ (2002) Structural basis for inhibition of cyclin-dependent kinase 9 by flavopiridol. Biochem Biophys Res Commun 293:566–571
De Azevedo WF, Leclerc S, Meijer L, Havlicek L, Strnad M, Kim SH (1997) Inhibition of cyclin-dependent kinases by purine analogues: crystal structure of human cdk2 complexed with roscovitine. Eur J Biochem 243:518–526
De Azevedo WF Jr, Mueller-Dieckmann HJ, Schulze-Gahmen U, Worland PJ, Sausville E, Kim SH (1996) Structural basis for specificity and potency of a flavonoid inhibitor of human CDK2, a cell cycle kinase. Proc Natl Acad Sci U S A 93:2735–2740
Iwata H (2018) Clinical development of CDK4/6 inhibitor for breast cancer. Breast Cancer 25:402–406
Banys-Paluchowski M, Krawczyk N, Paluchowski P (2019) Cyclin-dependent kinase 4/6 inhibitors: what have we learnt across studies, therapy situations and substances. Curr Opin Obstet Gynecol 31:56–66
Roskoski R Jr (2019) Cyclin-dependent protein serine/threonine kinase inhibitors as anticancer drugs. Pharmacol Res 139:471–488
Kim S, Tiedt R, Loo A, Horn T, Delach S, Kovats S et al (2018) The potent and selective cyclin-dependent kinases 4 and 6 inhibitor ribociclib (LEE011) is a versatile combination partner in preclinical cancer models. Oncotarget 9:35226–35240
Choo JR, Lee SC (2018) CDK4-6 inhibitors in breast cancer: current status and future development. Expert Opin Drug Metab Toxicol 14:1123–1138
Ribnikar D, Volovat SR, Cardoso F (2018) Targeting CDK4/6 pathways and beyond in breast cancer. Breast 43:8–17
Martin JM, Goldstein LJ (2018) Profile of abemaciclib and its potential in the treatment of breast cancer. Onco Targets Ther 11:5253–5259
Robert M, Frenel JS, Bourbouloux E, Rigaud DB, Patsouris A, Augereau P et al (2018) An update on the clinical use of CDK4/6 inhibitors in breast cancer. Drugs 78:1353–1362
Messina C, Cattrini C, Buzzatti G, Cerbone L, Zanardi E, Messina M et al (2018) CDK4/6 inhibitors in advanced hormone receptor-positive/HER2-negative breast cancer: a systematic review and meta-analysis of randomized trials. Breast Cancer Res Treat 172:9–21
Cintrón MS, Johnson GP, French AD (2017) Quantum mechanics models of the methanol dimer: OH⋯O hydrogen bonds of β-d-glucose moieties from crystallographic data. Carbohydr Res 443:87–94
Heifetz A, Chudyk EI, Gleave L, Aldeghi M, Cherezov V, Fedorov DG et al (2016) The fragment molecular orbital method reveals new insight into the chemical nature of GPCR-ligand interactions. J Chem Inf Model 56:159–172
Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM, Ferguson DM et al (1995) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 117:5179–5197
Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65:712–725
Huey R, Morris GM, Olson AJ, Goodsell DS (2007) A semiempirical free energy force field with charge-based desolvation. J Comput Chem 28:1145–1152
Fahmy A, Wagner G (2002) TreeDock: a tool for protein docking based on minimizing van der Waals energies. J Am Chem Soc 124:1241–1250
Demerdash ON, Buyan A, Mitchell JC (2010) ReplicOpter: a replicate optimizer for flexible docking. Proteins 78:3156–3165
Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49:3315–3321
de Azevedo WF Jr (2010) MolDock applied to structure-based virtual screening. Curr Drug Targets 11:327–334
Heberlé G, de Azevedo WF Jr (2011) Bio-inspired algorithms applied to molecular docking simulations. Curr Med Chem 18:1339–1352
Humphrey W, Dalke A, Schulten K (1996) VMD—visual molecular dynamics. J Mol Graph 14:33–38
Pereira JH, de Oliveira JS, Canduri F, Dias MV, Palma MS, Basso LA et al (2004) Structure of shikimate kinase from Mycobacterium tuberculosis reveals the binding of shikimic acid. Acta Crystallogr D Biol Crystallogr 60:2310–2319
Wallace AC, Laskowski RA, Thornton JM (1995) LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng 8:127–134
Laskowski RA, Swindells MB (2011) LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J Chem Inf Model 51:2778–2786
Lennard-Jones JE (1931) Cohesion. Proc Phys Soc 43:461–482
Parish T, Stoker NG (2002) The common aromatic amino acid biosynthesis pathway is essential in Mycobacterium tuberculosis. Microbiology 148:3069–3077
Pereira JH, Canduri F, de Oliveira JS, da Silveira NJ, Basso LA, Palma MS et al (2003) Structural bioinformatics study of EPSP synthase from Mycobacterium tuberculosis. Biochem Biophys Res Commun 312:608–614
Arcuri HA, Canduri F, Pereira JH, da Silveira NJ, Camera JC Jr, de Oliveira JS et al (2004) Molecular models for shikimate pathway enzymes of Xylella fastidiosa. Biochem Biophys Res Commun 320:979–991
Dias MV, Ely F, Canduri F, Pereira JH, Frazzon J, Basso LA et al (2004) Crystallization and preliminary X-ray crystallographic analysis of chorismate synthase from Mycobacterium tuberculosis. Acta Crystallogr D Biol Crystallogr 60:2003–2005
Uchôa HB, Jorge GE, Freitas Da Silveira NJ, Camera JC Jr, Canduri F, De Azevedo WF Jr (2004) Parmodel: a web server for automated comparative modeling of proteins. Biochem Biophys Res Commun 325:1481–1486
Silveira NJ, Uchôa HB, Pereira JH, Canduri F, Basso LA, Palma MS et al (2005) Molecular models of protein targets from Mycobacterium tuberculosis. J Mol Model 11:160–166
Dias MV, Borges JC, Ely F, Pereira JH, Canduri F, Ramos CH et al (2006) Structure of chorismate synthase from Mycobacterium tuberculosis. J Struct Biol 154:130–143
da Silveira NJ, Bonalumi CE, Uchõa HB, Pereira JH, Canduri F, de Azevedo WF (2006) DBMODELING: a database applied to the study of protein targets from genome projects. Cell Biochem Biophys 44:366–374
Borges JC, Pereira JH, Vasconcelos IB, dos Santos GC, Olivieri JR, Ramos CH et al (2006) Phosphate closes the solution structure of the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) from Mycobacterium tuberculosis. Arch Biochem Biophys 452:156–164
da Silveira NJF, Bonalumi CE, Arcuri HA, de Azevedo WF Jr (2007) Molecular modeling databases: a new way in the search of proteins targets for drug development. Curr Bioinf 2:1–10
Dias MV, Faím LM, Vasconcelos IB, de Oliveira JS, Basso LA, Santos DS et al (2007) Effects of the magnesium and chloride ions and shikimate on the structure of shikimate kinase from Mycobacterium tuberculosis. Acta Crystallogr Sect F Struct Biol Cryst Commun 63:1–6
Dias MV, Ely F, Palma MS, de Azevedo WF Jr, Basso LA, Santos DS (2007) Chorismate synthase: an attractive target for drug development against orphan diseases. Curr Drug Targets 8:437–444
Marques MR, Pereira JH, Oliveira JS, Basso LA, de Azevedo WF Jr, Santos DS et al (2007) The inhibition of 5-enolpyruvylshikimate-3-phosphate synthase as a model for development of novel antimicrobials. Curr Drug Targets 8:445–457
Pereira JH, Vasconcelos IB, Oliveira JS, Caceres RA, de Azevedo WF Jr, Basso LA et al (2007) Shikimate kinase: a potential target for development of novel antitubercular agents. Curr Drug Targets 8:459–468
Marques MR, Vaso A, Neto JR, Fossey MA, Oliveira JS, Basso LA et al (2008) Dynamics of glyphosate-induced conformational changes of Mycobacterium tuberculosis 5-enolpyruvylshikimate-3-phosphate synthase (EC 2.5.1.19) determined by hydrogen-deuterium exchange and electrospray mass spectrometry. Biochemistry 47:7509–7522
Arcuri HA, Borges JC, Fonseca IO, Pereira JH, Neto JR, Basso LA et al (2008) Structural studies of shikimate 5-dehydrogenase from Mycobacterium tuberculosis. Proteins 72:720–730
Pauli I, Caceres RA, de Azevedo WF Jr (2008) Molecular modeling and dynamics studies of Shikimate kinase from Bacillus anthracis. Bioorg Med Chem 16:8098–8108
de Azevedo WF Jr (2008) Protein-drug interactions. Curr Drug Targets 9:1030
de Azevedo WF Jr, Dias R (2008) Computational methods for calculation of ligand-binding affinity. Curr Drug Targets 92:1031–1039
Dias R, de Azevedo WF Jr (2008) Molecular docking algorithms. Curr Drug Targets 9:1040–1047
Canduri F, de Azevedo WF (2008) Protein crystallography in drug discovery. Curr Drug Targets 9:1048–1053
Pauli I, Timmers LF, Caceres RA, Soares MB, de Azevedo WF Jr (2008) In silico and in vitro: identifying new drugs. Curr Drug Targets 9:1054–1061
Dias R, Timmers LF, Caceres RA, de Azevedo WF Jr (2008) Evaluation of molecular docking using polynomial empirical scoring functions. Curr Drug Targets 9:1062–1070
de Azevedo WF Jr, Dias R (2008) Experimental approaches to evaluate the thermodynamics of protein-drug interactions. Curr Drug Targets 9:1071–1076
Caceres RA, Pauli I, Timmers LF, de Azevedo WF Jr (2008) Molecular recognition models: a challenge to overcome. Curr Drug Targets 9:1077–1083
Barcellos GB, Caceres RA, de Azevedo WF Jr (2009) Structural studies of shikimate dehydrogenase from Bacillus anthracis complexed with cofactor NADP. J Mol Model 15:147–155
de Azevedo WF Jr, Dias R, Timmers LF, Pauli I, Caceres RA, Soares MB (2009) Bioinformatics tools for screening of antiparasitic drugs. Curr Drug Targets 10:232–239
Arcuri HA, Zafalon GF, Marucci EA, Bonalumi CE, da Silveira NJ, Machado JM et al (2010) SKPDB: a structural database of shikimate pathway enzymes. BMC Bioinformatics 11:12
Hernandes MZ, Cavalcanti SM, Moreira DR, de Azevedo WF Jr, Leite AC (2010) Halogen atoms in the modern medicinal chemistry: hints for the drug design. Curr Drug Targets 11:303–314
De Azevedo WF Jr (2010) Structure-based virtual screening. Curr Drug Targets 11:261–263
de Azevedo WF Jr (2011) Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr Med Chem 18:1353–1366
de Azevedo WF Jr (2011) Protein targets for development of drugs against Mycobacterium tuberculosis. Curr Med Chem 18:1255–1257
Vianna CP, de Azevedo WF Jr (2012) Identification of new potential Mycobacterium tuberculosis shikimate kinase inhibitors through molecular docking simulations. J Mol Model 18:755–764
Azevedo LS, Moraes FP, Xavier MM, Pantoja EO, Villavicencio B, Finck JA et al (2012) Recent Progress of molecular docking simulations applied to development of drugs. Curr Bioinf 7:352–365
Coracini JD, de Azevedo WF Jr (2014) Shikimate kinase, a protein target for drug design. Curr Med Chem 21:592–604
Xavier MM, Heck GS, de Avila MB, Levin NM, Pintro VO, Carvalho NL et al (2016) SAnDReS a computational tool for statistical analysis of docking results and development of scoring functions. Comb Chem High Throughput Screen 19:801–812
Pintro VO, Azevedo WF (2017) Optimized virtual screening workflow. Towards target-based polynomial scoring functions for HIV-1 protease. Comb Chem High Throughput Screen 20:820–827
Freitas PG, Elias TC, Pinto IA, Costa LT, de Carvalho PVSD, Omote DQ et al (2018) Computational approach to the discovery of phytochemical molecules with therapeutic potential targets to the PKCZ protein. Lett Drug Des Discovery 15:488–499
Amaral MEA, Nery LR, Leite CE, de Azevedo WF Jr, Campos MM (2018) Pre-clinical effects of metformin and aspirin on the cell lines of different breast cancer subtypes. Invest New Drugs 36:782–796
de Ávila MB, de Azevedo WF Jr (2018) Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase. Chem Biol Drug Des 92:1468–1474
Bitencourt-Ferreira G, de Azevedo WF Jr (2018) Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes. Biophys Chem 240:63–69
de Azevedo WF Jr, Dias R (2008) Evaluation of ligand-binding affinity using polynomial empirical scoring functions. Bioorg Med Chem 16:9378–9382
Delatorre P, Rocha BA, Souza EP, Oliveira TM, Bezerra GA, Moreno FB et al (2007) Structure of a lectin from Canavalia gladiata seeds: new structural insights for old molecules. BMC Struct Biol 7:52
de Azevedo WF Jr, Canduri F, dos Santos DM, Pereira JH, Bertacine Dias MV, Silva RG et al (2003) Crystal structure of human PNP complexed with guanine. Biochem Biophys Res Commun 312:767–772
Filgueira de Azevedo W Jr, dos Santos GC, dos Santos DM, Olivieri JR, Canduri F, Silva RG et al (2003) Docking and small angle X-ray scattering studies of purine nucleoside phosphorylase. Biochem Biophys Res Commun 309:923–928
Canduri F, Perez PC, Caceres RA, de Azevedo WF Jr (2007) Protein kinases as targets for antiparasitic chemotherapy drugs. Curr Drug Targets 8:389–398
Silva RG, Pereira JH, Canduri F, de Azevedo WF Jr, Basso LA, Santos DS (2005) Kinetics and crystal structure of human purine nucleoside phosphorylase in complex with 7-methyl-6-thio-guanosine. Arch Biochem Biophys 442:49–58
Timmers LF, Caceres RA, Vivan AL, Gava LM, Dias R, Ducati RG et al (2008) Structural studies of human purine nucleoside phosphorylase: towards a new specific empirical scoring function. Arch Biochem Biophys 479:28–38
Caceres RA, Saraiva Timmers LF, Dias R, Basso LA, Santos DS, de Azevedo WF Jr (2008) Molecular modeling and dynamics simulations of PNP from Streptococcus agalactiae. Bioorg Med Chem 16:4984–4993
de Azevedo WF Jr, Ward RJ, Canduri F, Soares A, Giglio JR, Arni RK (1998) Crystal structure of piratoxin-I: a calcium-independent, myotoxic phospholipase A2-homologue from Bothrops pirajai venom. Toxicon 36:1395–1406
da Silveira NJ, Uchôa HB, Canduri F, Pereira JH, Camera JC Jr, Basso LA et al (2004) Structural bioinformatics study of PNP from Schistosoma mansoni. Biochem Biophys Res Commun 322:100–104
Bezerra GA, Oliveira TM, Moreno FB, de Souza EP, da Rocha BA, Benevides RG et al (2007) Structural analysis of Canavalia maritima and Canavalia gladiata lectins complexed with different dimannosides: new insights into the understanding of the structure-biological activity relationship in legume lectins. J Struct Biol 160:168–176
Canduri F, Fadel V, Dias MV, Basso LA, Palma MS, Santos DS et al (2005) Crystal structure of human PNP complexed with hypoxanthine and sulfate ion. Biochem Biophys Res Commun 326:335–338
Delatorre P, Rocha BA, Gadelha CA, Santi-Gadelha T, Cajazeiras JB, Souza EP et al (2006) Crystal structure of a lectin from Canavalia maritima (ConM) in complex with trehalose and maltose reveals relevant mutation in ConA-like lectins. J Struct Biol 154:280–286
Rádis-Baptista G, Moreno FB, de Lima Nogueira L, Martins AM, de Oliveira Toyama D, Toyama MH et al (2006) Crotacetin, a novel snake venom C-type lectin homolog of convulxin, exhibits an unpredictable antimicrobial activity. Cell Biochem Biophys 44:412–423
Breda A, Basso LA, Santos DS, de Azevedo WF Jr (2008) Virtual screening of drugs: score functions, docking, and drug design. Curr Comput Aided Drug Des 4(4):265–272
Nolasco DO, Canduri F, Pereira JH, Cortinóz JR, Palma MS, Oliveira JS et al (2004) Crystallographic structure of PNP from Mycobacterium tuberculosis at 1.9A resolution. Biochem Biophys Res Commun 324:789–794
Soares MB, Silva CV, Bastos TM, Guimarães ET, Figueira CP, Smirlis D et al (2012) Anti-Trypanosoma cruzi activity of nicotinamide. Acta Trop 12:224–229
Rocha BA, Delatorre P, Oliveira TM, Benevides RG, Pires AF, Sousa AA et al (2011) Structural basis for both pro- and anti-inflammatory response induced by mannose-specific legume lectin from Cymbosema roseum. Biochimie 93:806–816
Ducati RG, Basso LA, Santos DS, de Azevedo WF Jr (2010) Crystallographic and docking studies of purine nucleoside phosphorylase from Mycobacterium tuberculosis. Bioorg Med Chem 18:4769–4774
Acknowledgments
This work was supported by grants from CNPq (Brazil) (308883/2014-4). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior—Brasil (CAPES)—Finance Code 001. GB-F acknowledges support from PUCRS/BPA fellowship. MV-A acknowledges support from PUCRS/IC Jr. WFA is a senior researcher for CNPq (Brazil) (Process Numbers: 308883/2014-4 and 309029/2018-0).
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Bitencourt-Ferreira, G., Veit-Acosta, M., de Azevedo, W.F. (2019). Hydrogen Bonds in Protein-Ligand Complexes. In: de Azevedo Jr., W. (eds) Docking Screens for Drug Discovery. Methods in Molecular Biology, vol 2053. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9752-7_7
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