A comparative analysis of protein targets of withdrawn cardiovascular drugs in human and mouse

Journal of Clinical Bioinformatics

Jingfei Huang³  Yanjie Wang²  Jingwen Wang¹  Yuqi Zhao⁴ 

[1] Yantai Yuhuangding Hospital, Yantai, Shandong Province, 264000, China;Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, 650223, China;Kunming Institute of Zoology-Chinese University of Hongkong Joint Research Center for Bio-resources and Human Disease Mechanisms, Kunming, 650223, China;State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, 32, Eastern Jiaochang Road, Kunming, Yunnan, 650223, China

关键词: Drug-binding pocket;    Side effects;    Sequence divergence;    Animal modeling;    Withdrawn cardiovascular drugs;   

Others  :  805844
DOI  :  10.1186/2043-9113-2-10

 received in 2011-12-23, accepted in 2012-05-01,  发布年份 2012

【 摘 要 】

Background

Mouse is widely used in animal testing of cardiovascular disease. However, a large number of cardiovascular drugs that have been experimentally proved to work well on mouse were withdrawn because they caused adverse side effects in human.

Methods

In this study, we investigate whether binding patterns of withdrawn cardiovascular drugs are conserved between mouse and human through computational dockings and molecular dynamic simulations. In addition, we also measured the level of conservation of gene expression patterns of the drug targets and their interacting partners by analyzing the microarray data.

Results

The results show that target proteins of withdrawn cardiovascular drugs are functionally conserved between human and mouse. However, all the binding patterns of withdrawn drugs we retrieved show striking difference due to sequence divergence in drug-binding pocket, mainly through loss or gain of hydrogen bond donors and distinct drug-binding pockets. The binding affinities of withdrawn drugs to their receptors tend to be reduced from mouse to human. In contrast, the FDA-approved and best-selling drugs are little affected.

Conclusions

Our analysis suggests that sequence divergence in drug-binding pocket may be a reasonable explanation for the discrepancy of drug effects between animal models and human.

【 授权许可】

   
2012 Zhao et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Baker M: Animal models: inside the minds of mice and men. Nature 2011, 475:123-128.
• [2]Dolgin E: Animal testing alternatives come alive in US. Nat Med 2010, 16:1348-1348.
• [3]Shah RR: Can pharmacogenetics help rescue drugs withdrawn from the market? Pharmacogenomics 2006, 7:889-908.
• [4]Miller G: PHARMACOLOGY The Puzzling Rise and Fall of a Dark-Horse Alzheimer’s Drug. Science 2010, 327:1309-1309.
• [5]Wang J, Ferruzzi MG, Varghese M, Qian XJ, Cheng A, Xie M, Zhao W, Ho L, Pasinetti GM: Preclinical study of dimebon on beta-amyloid-mediated neuropathology in Alzheimer’s disease. Molecular Neurodegeneration 2011, 6:7. BioMed Central Full Text
• [6]Benfey PN, Mitchell-Olds T: Perspective - From genotype to phenotype: Systems biology meets natural variation. Science 2008, 320:495-497.
• [7]Miller JA, Horvath S, Geschwind DH: Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proceedings of the National Academy of Sciences of the United States of America 2010, 107:12698-12703.
• [8]Brawand D, Soumillon M, Necsulea A, Julien P, Csardi G, Harrigan P, Weier M, Liechti A, Aximu-Petri A, Kircher M: The evolution of gene expression levels in mammalian organs. Nature 2011, 478:343-+.
• [9]Liao BY, Zhang JZ: Null mutations in human and mouse orthologs frequently result in different phenotypes. Proceedings of the National Academy of Sciences of the United States of America 2008, 105:6987-6992.
• [10]Shoemaker BA, Panchenko AR: Deciphering protein-protein interactions. Part II. Computational methods to predict protein and domain interaction partners. Plos Comput Biol 2007, 3:595-601.
• [11]Andreasen PA, Riccio A, Welinder KG, Douglas R, Sartorio R, Nielsen LS, Oppenheimer C, Blasi F, Dano K: Plasminogen-Activator Inhibitor Type-1 - Reactive Center and Amino-Terminal Heterogeneity Determined by Protein and Cdna Sequencing. Febs Lett 1986, 209:213-218.
• [12]Hopkins AL, Groom CR: The druggable genome. Nat Rev Drug Discov 2002, 1:727-730.
• [13]Fan H, Mark AE: Refinement of homology-based protein structures by molecular dynamics simulation techniques. Protein Sci 2004, 13:211-220.
• [14]Aljada A, Garg R, Ghanim H, Mohanty P, Hamouda W, Assian E, Dandona P: Nuclear factor-kappa B suppressive and inhibitor-kappa B stimulatory effects of troglitazone in obese patients with type 2 diabetes: Evidence of an antiinflammatory action? J Clin Endocr Metab 2001, 86:3250-3256.
• [15]Wang Y, Fang F, Wong CW: Troglitazone is an estrogen-related receptor alpha and gamma inverse agonist. Biochem Pharmacol 2010, 80:80-85.
• [16]Ganne F, Vasse M, Beaudeux JL, Peynet J, Francois A, Mishal Z, Chartier A, Tobelem G, Vannier JP, Soria J, Soria C: Cerivastatin, an inhibitor of HMG-CoA reductase, inhibits urokinase/urokinase-receptor expression and MMP-9 secretion by peripheral blood monocytes - A possible protective mechanism against atherothrombosis. Thromb Haemostasis 2000, 84:680-688.
• [17]Istvan ES, Palnitkar M, Buchanan SK, Deisenhofer J: Crystal structure of the catalytic portion of human HMG-CoA reductase: insights into regulation of activity and catalysis. Embo Journal 2000, 19:819-830.
• [18]Guo J, Zhan S, Somers J, Westenbroek RE, Catterall WA, Roach DE, Sheldon RS, Lees-Miller JP, Li P, Shimoni Y, Duff HJ: Decrease in density of I-Na is in the common final pathway to heart block in murine hearts overexpressing calcineurin. Am J Physiol-Heart C 2006, 291:H2669-H2679.
• [19]Mocanu MM, Gadgil S, Yellon DM, Baxter GF: Mibefradil, a T-type and L-type calcium channel blocker, limits infarct size through a glibenclamide-sensitive mechanism. Cardiovasc Drug Ther 1999, 13:115-122.
• [20]Monnier N, Procaccio V, Stieglitz P, Lunardi J: Malignant-hyperthermia susceptibility is associated with a mutation of the alpha(1)-subunit of the human dihydropyridine-sensitive L-type voltage-dependent calcium-channel receptor in skeletal muscle. Am J Hum Genet 1997, 60:1316-1325.
• [21]Testa L, Bhindi R, Agostoni P, Abbate A, Zoccai GGLB, van Gaal WJ: The direct thrombin inhibitor ximelagatran/melagatran: a systematic review on clinical applications and an evidence based assessment of risk benefit profile. Expert Opin Drug Saf 2007, 6:397-406.
• [22]Iyengar R, Boran ADW: Systems approaches to polypharmacology and drug discovery. Curr Opin Drug Disc 2010, 13:297-309.
• [23]Feuerstein GZ, Zaleska MM, Krams M, Wang X, Day M, Rutkowski JL, Finklestein SP, Pangalos MN, Poole M, Stiles GL, et al.: Missing steps in the STAIR case: a Translational Medicine perspective on the development of NXY-059 for treatment of acute ischemic stroke. J Cerebr Blood F Met 2008, 28:217-219.
• [24]Le L, Lee EH, Hardy DJ, Truong TN, Schulten K: Molecular Dynamics Simulations Suggest that Electrostatic Funnel Directs Binding of Tamiflu to Influenza N1 Neuraminidases. Plos Comput Biol 2010, 6(9):pii:e1000939.
• [25]Sun H, Jiang YJ, Yu QS, Luo CC, Zou JW: Effect of mutation K85R on GSK-3 beta: Molecular dynamics simulation. Biochem Bioph Res Co 2008, 377:962-965.
• [26]Heinz S, Haehnel V, Karaghiosoff M, Schwarzfischer L, Muller M, Krause SW, Rehli M: Species-specific regulation of Toll-like receptor 3 genes in men and mice. J Biol Chem 2003, 278:21502-21509.
• [27]Fraenkel E, Odom DT, Dowell RD, Jacobsen ES, Gordon W, Danford TW, MacIsaac KD, Rolfe PA, Conboy CM, Gifford DK: Tissue-specific transcriptional regulation has diverged significantly between human and mouse. Nat Genet 2007, 39:730-732.
• [28]Musshoff F: Illegal or legitimate use? Precursor compounds to amphetamine and methamphetamine. Drug Metab Rev 2000, 32:15-44.
• [29]Cody JT: Precursor medications as a source of methamphetamine and/or amphetamine positive drug testing results. J Occup Environ Med 2002, 44:435-450.
• [30]Kraemer T, Roditis SK, Peters FT, Maurer HH: Amphetamine concentrations in human urine following single-dose administration of the calcium antagonist prenylamine - Studies using fluorescence polarization immunoassay (FPIA) and GC-MS. J Anal Toxicol 2003, 27:68-73.
• [31]Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M: DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 2008, 36:D901-D906.
• [32]Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D: The Database of Interacting Proteins: 2004 update. Nucleic Acids Res 2004, 32:D449-D451.
• [33]Alfarano C, Andrade CE, Anthony K, Bahroos N, Bajec M, Bantoft K, Betel D, Bobechko B, Boutilier K, Burgess E, et al.: The Biomolecular Interaction Network Database and related tools 2005 update. Nucleic Acids Res 2005, 33:D418-D424.
• [34]Pandey A, Mishra GR, Suresh M, Kumaran K, Kannabiran N, Suresh S, Bala P, Shivakumar K, Anuradha N, Reddy R, et al.: Human protein reference database - 2006 update. Nucleic Acids Res 2006, 34:D411-D414.
• [35]Tyers M, Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A: BioGRID: a general repository for interaction datasets. Nucleic Acids Res 2006, 34:D535-D539.
• [36]Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, Cesareni G: MINT: a Molecular INTeraction database. Febs Lett 2002, 513:135-140.
• [37]Kerrien S, Alam-Faruque Y, Aranda B, Bancarz I, Bridge A, Derow C, Dimmer E, Feuermann M, Friedrichsen A, Huntley R, et al.: IntAct - open source resource for molecular interaction data. Nucleic Acids Res 2007, 35:D561-D565.
• [38]Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003, 13:2498-2504.
• [39]Muchir A, Pavlidis P, Decostre V, Herron AJ, Arimura T, Bonne G, Worman HJ: Activation of MAPK pathways links LMNA mutations to cardiomyopathy in Emery-Dreifuss muscular dystrophy. Journal of Clinical Investigation 2007, 117:1282-1293.
• [40]Blekhman R, Oshlack A, Chabot AE, Smyth GK,, Gilad Y: Gene Regulation in Primates Evolves under Tissue-Specific Selection Pressures. Plos Genet 2008, 4(11):e1000271.
• [41]Zhao Y, Sheng Z, Huang J: A systematic analysis of heart transcriptome highlights divergent cardiovascular disease pathways between animal models and humans. Mol Biosyst 2012, 8:504-510.
• [42]Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The Protein Data Bank. Nucleic Acids Res 2000, 28:235-242.
• [43]Sali A, Blundell TL: Comparative Protein Modeling by Satisfaction of Spatial Restraints. J Mol Biol 1993, 234:779-815.
• [44]Mackerell AD, Feig M, Brooks CL: Extending the treatment of backbone energetics in protein force fields: Limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations. J Comput Chem 2004, 25:1400-1415.
• [45]Buck M, Bouguet-Bonnet S, Pastor RW, MacKerell AD: Importance of the CMAP correction to the CHARMM22 protein force field: Dynamics of hen lysozyme. Biophys J 2006, 90:L36-L38.
• [46]Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML: Comparison of Simple Potential Functions for Simulating Liquid Water. J Chem Phys 1983, 79:926-935.
• [47]Wu GS, Robertson DH, Brooks CL, Vieth M: Detailed analysis of grid-based molecular docking: A case study of CDOCKER - A CHARMm-based MD docking algorithm. J Comput Chem 2003, 24:1549-1562.
• [48]Koska J, Spassov VZ, Maynard AJ, Yan L, Austin N, Flook PK, Venkatachalam CM: Fully Automated Molecular Mechanics Based Induced Fit Protein-Ligand Docking Method. J Chem Inf Model 2008, 48:1965-1973.
• [49]Huang JF, Li GH: CMASA: an accurate algorithm for detecting local protein structural similarity and its application to enzyme catalytic site annotation. Bmc Bioinformatics 2010, 11:-439.
• [50]Andreeva A, Howorth D, Brenner SE, Hubbard TJP, Chothia C, Murzin AG: SCOP database in 2004: refinements integrate structure and sequence family data. Nucleic Acids Res 2004, 32:D226-D229.
• [51]Jorgensen WL, Tirado-Rives J: Contribution of conformer focusing to the uncertainty in predicting free energies for protein-ligand binding. J Med Chem 2006, 49:5880-5884.