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General Review Article

Docking Techniques in Toxicology: An Overview

Author(s): Meenakshi Gupta, Ruchika Sharma and Anoop Kumar*

Volume 15, Issue 6, 2020

Page: [600 - 610] Pages: 11

DOI: 10.2174/1574893614666191003125540

Price: $65

Abstract

A variety of environmental toxicants such as heavy metals, pesticides, organic chemicals, etc produce harmful effects in our living systems. In the literature, various reports have indicated the detrimental effects of toxicants such as immunotoxicity, cardiotoxicity, nephrotoxicity, etc. Experimental animals are generally used to investigate the safety profile of environmental chemicals, but research on animals has some limitations. Thus, there is a need for alternative approaches. Docking study is one of the alternate techniques which predict the binding affinity of molecules in the active site of a particular receptor without using animals. These techniques can also be used to check the interactions of environmental toxicants towards biological targets. Varieties of user-friendly software are available in the market for molecular docking, but very few toxicologists use these techniques in the field of toxicology. To increase the use of these techniques in the field of toxicology, understanding of basic concepts of these techniques is required among toxicological scientists. This article has summarized the fundamental concepts of docking in the context of its role in toxicology. Furthermore, these promising techniques are also discussed in this study.

Keywords: Environmental chemicals, docking study, doc score, toxicology, pesticides, organic chemicals.

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[1]
Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 2004; 3(11): 935-49.
[http://dx.doi.org/10.1038/nrd1549] [PMID: 15520816]
[2]
Kavlock RJ, Ankley G, Blancato J, et al. Computational toxicology--a state of the science mini review. Toxicol Sci 2008; 103(1): 14-27.
[http://dx.doi.org/10.1093/toxsci/kfm297] [PMID: 18065772]
[3]
Doull J. The past, present, and future of toxicology. Pharmacol Rev 1984; 36(2): 15S-8S.
[PMID: 6473500]
[4]
Cohen M. Environmental toxins and health--the health impact of pesticides. Aust Fam Physician 2007; 36(12): 1002-4.
[PMID: 18075622]
[5]
Levin HS, Rodnitzky RL. Behavioral effects of organophosphate in man. Clin Toxicol 1976; 9(3): 391-403.
[http://dx.doi.org/10.3109/15563657608988138] [PMID: 782776]
[6]
Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 2007; 152(1): 9-20.
[http://dx.doi.org/10.1038/sj.bjp.0707305] [PMID: 17549047]
[7]
Lipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 2004; 1(4): 337-41.
[http://dx.doi.org/10.1016/j.ddtec.2004.11.007] [PMID: 24981612]
[8]
Hou T, Xu X. Recent development and application of virtual screening in drug discovery: an overview. Curr Pharm Des 2004; 10(9): 1011-33.
[http://dx.doi.org/10.2174/1381612043452721] [PMID: 15078130]
[9]
Doke SK, Dhawale SC. Alternatives to animal testing: A review. Saudi Pharm J 2015; 23(3): 223-9.
[http://dx.doi.org/10.1016/j.jsps.2013.11.002] [PMID: 26106269]
[10]
Esmon CT. Why do animal models (sometimes) fail to mimic human sepsis? Crit Care Med 2004; 32(5): S219-22.
[http://dx.doi.org/10.1097/01.CCM.0000127036.27343.48 PMID: 15118521]
[11]
Dayan AD. The relative worth of animal testingRisk-Benefit Analysis in Drug Research. Springer Netherlands 1981; pp. 97-112.
[http://dx.doi.org/10.1007/978-94-015-7132-6_9]
[12]
Latin H. Good science, bad regulation, and toxic risk assessment. Yale J Regul 1988; 5: 89.
[13]
Balls M. Replacement of animal procedures: alternatives in research, education and testing. Lab Anim 1994; 28(3): 193-211.
[http://dx.doi.org/10.1258/002367794780681714] [PMID: 7967458]
[14]
Thiel W, Hummer G. Nobel 2013 Chemistry:methods for computational chemistry. Nature 2013; 504(7478): 96-7.
[http://dx.doi.org/10.1038/504096a] [PMID: 24305156]
[15]
Leach AR, Shoichet BK, Peishoff CE. Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. J Med Chem 2006; 49(20): 5851-5.
[http://dx.doi.org/10.1021/jm060999m] [PMID: 17004700]
[16]
Hodgson E. Introduction to toxicology: A textbook of modern toxicology 2004; 3:1
[17]
Pelkonen O, Raunio H. Metabolic activation of toxins: tissue-specific expression and metabolism in target organs. Environ Health Perspect 1997; 105(Suppl. 4): 767-74.
[PMID: 9255560]
[18]
Baldi A. Computational approaches for drug design and discovery: An overview. Sys Rev Pharm 2010; 1: 99.
[http://dx.doi.org/10.4103/0975-8453.59519]
[19]
Dias R, de Azevedo WF Jr, Walter F. Molecular docking algorithms. Curr Drug Targets 2008; 9(12): 1040-7.
[http://dx.doi.org/10.2174/138945008786949432] [PMID: 19128213]
[20]
Warren GL, Andrews CW, Capelli AM, et al. A critical assessment of docking programs and scoring functions. J Med Chem 2006; 49(20): 5912-31.
[http://dx.doi.org/10.1021/jm050362n] [PMID: 17004707]
[21]
Goodsell DS, Morris GM, Olson AJ. Automated docking of flexible ligands: applications of AutoDock. J Mol Recognit 1996; 9(1): 1-5.
[http://dx.doi.org/10.1002/(SICI)1099-1352(199601)9:1<1::AIDJMR241>3.0.CO;2-6] [PMID: 8723313]
[22]
Michino M, Abola E, Brooks CL III, et al. GPCR Dock 2008 participants. Community-wide assessment of GPCR structure modelling and ligand docking: GPCR Dock 2008. Nat Rev Drug Discov 2009; 8(6): 455-63.
[http://dx.doi.org/10.1038/nrd2877] [PMID: 19461661]
[23]
Halgren TA, Murphy RB, Friesner RA, et al. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 2004; 47(7): 1750-9.
[http://dx.doi.org/10.1021/jm030644s] [PMID: 15027866]
[24]
Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD. Improved protein-ligand docking using GOLD. Proteins 2003; 52(4): 609-23.
[http://dx.doi.org/10.1002/prot.10465] [PMID: 12910460]
[25]
Tovchigrechko A, Vakser IA. GRAMM-X public web server for protein-protein docking. Nucleic Acids Res 2006; 34W310-4
[http://dx.doi.org/10.1093/nar/gkl206] [PMID: 16845016]
[26]
van Dijk M, van Dijk AD, Hsu V, Boelens R, Bonvin AM. Information-driven protein-DNA docking using HADDOCK: it is a matter of flexibility. Nucleic Acids Res 2006; 34(11): 3317-25.
[http://dx.doi.org/10.1093/nar/gkl412] [PMID: 16820531]
[27]
Meng EC, Kuntz ID, Abraham DJ, Kellogg GE. Evaluating docked complexes with the HINT exponential function and empirical atomic hydrophobicities. J Comput Aided Mol Des 1994; 8(3): 299-306.
[http://dx.doi.org/10.1007/BF00126747] [PMID: 7964929]
[28]
Wallace AC, Laskowski RA, Thornton JM. LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng 1995; 8(2): 127-34.
[http://dx.doi.org/10.1093/protein/8.2.127] [PMID: 7630882]
[29]
Halperin I, Ma B, Wolfson H, Nussinov R. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins 2002; 47(4): 409-43.
[http://dx.doi.org/10.1002/prot.10115] [PMID: 12001221]
[30]
B-Rao C. Subramanian J, Sharma SD. Managing protein flexibility in docking and its applications. Drug Discov Today 2009; 14(7-8): 394-400.
[http://dx.doi.org/10.1016/j.drudis.2009.01.003] [PMID: 19185058]
[31]
Wang WJ, Huang Q, Zou J, Li LL, Yang SY. TS-Chemscore, a Target-Specific Scoring Function, Significantly Improves the Performance of Scoring in Virtual Screening. Chem Biol Drug Des 2015; 86(1): 1-8.
[http://dx.doi.org/10.1111/cbdd.12470] [PMID: 25358259]
[32]
Wang R, Lu Y, Wang S. Comparative evaluation of 11 scoring functions for molecular docking. J Med Chem 2003; 46(12): 2287-303.
[http://dx.doi.org/10.1021/jm0203783] [PMID: 12773034]
[33]
Bar-Shalom Y, Blackman SS, Fitzgerald RJ. Dimensionless score function for multiple hypothesis tracking. ‎. IEEE Trans Aerosp Electron Syst 2007; 43: 392-400.
[http://dx.doi.org/10.1109/TAES.2007.357141]
[34]
Liang S, Zhang C, Liu S, Zhou Y. Protein binding site prediction using an empirical scoring function. Nucleic Acids Res 2006; 34(13): 3698-707.
[http://dx.doi.org/10.1093/nar/gkl454] [PMID: 16893954]
[35]
Muegge I. PMF scoring revisited. J Med Chem 2006; 49(20): 5895-902.
[http://dx.doi.org/10.1021/jm050038s] [PMID: 17004705]
[36]
Velec HF, Gohlke H, Klebe G. DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J Med Chem 2005; 48(20): 6296-303.
[http://dx.doi.org/10.1021/jm050436v] [PMID: 16190756]
[37]
Ishchenko AV, Shakhnovich EI. SMall Molecule Growth 2001 (SMoG2001): an improved knowledge-based scoring function for protein-ligand interactions. J Med Chem 2002; 45(13): 2770-80.
[http://dx.doi.org/10.1021/jm0105833] [PMID: 12061879]
[38]
Korb O, Stützle T, Exner TE. Empirical scoring functions for advanced protein-ligand docking with PLANTS. J Chem Inf Model 2009; 49(1): 84-96.
[http://dx.doi.org/10.1021/ci800298z] [PMID: 19125657]
[39]
Yin S, Biedermannova L, Vondrasek J, Dokholyan NV. MedusaScore: an accurate force field-based scoring function for virtual drug screening. J Chem Inf Model 2008; 48(8): 1656-62.
[http://dx.doi.org/10.1021/ci8001167] [PMID: 18672869]
[40]
Gohlke H, Hendlich M, Klebe G. Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol 2000; 295(2): 337-56.
[http://dx.doi.org/10.1006/jmbi.1999.3371] [PMID: 10623530]
[41]
Blundell TL, Jhoti H, Abell C. High-throughput crystallography for lead discovery in drug design. Nat Rev Drug Discov 2002; 1(1): 45-54.
[http://dx.doi.org/10.1038/nrd706] [PMID: 12119609]
[42]
Vranken WF, Boucher W, Stevens TJ, et al. The CCPN data model for NMR spectroscopy: development of a software pipeline. Proteins 2005; 59(4): 687-96.
[http://dx.doi.org/10.1002/prot.20449] [PMID: 15815974]
[43]
Berman HM, Westbrook J, Feng Z, et al. The Protein Data BankInternational Tables for Crystallography Volume F: Crystallography of biological macromolecules. Springer Netherlands 2006; 675-84.
[44]
Berman HM, Battistuz T, Bhat TN, et al. The protein data bank Acta Crystallogr D Biol Crystallogr 2002; 58( Pt 6 No 1): 899-907.
[http://dx.doi.org/10.1107/S0907444902003451] [PMID: 12037327]
[45]
Kramer B, Rarey M, Lengauer T. Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins 1999; 37(2): 228-41.
[http://dx.doi.org/10.1002/(SICI)1097-0134(19991101)37:2<228:AID-PROT8>3.0.CO;2-8 PMID: 10584068]
[46]
Hooft RW, Sander C, Vriend G. Objectively judging the quality of a protein structure from a Ramachandran plot. Comput Appl Biosci 1997; 13(4): 425-30.
[http://dx.doi.org/10.1093/bioinformatics/13.4.425] [PMID: 9283757]
[47]
Guido RV, Oliva G, Andricopulo AD. Virtual screening and its integration with modern drug design technologies. Curr Med Chem 2008; 15(1): 37-46.
[http://dx.doi.org/10.2174/092986708783330683] [PMID: 18220761]
[48]
Sousa SF, Fernandes PA, Ramos MJ. Protein-ligand docking: current status and future challenges. Proteins 2006; 65(1): 15-26.
[http://dx.doi.org/10.1002/prot.21082] [PMID: 16862531]
[49]
Holdgate GA, Ward WH. Measurements of binding thermodynamics in drug discovery. Drug Discov Today 2005; 10(22): 1543-50.
[http://dx.doi.org/10.1016/S1359-6446(05)03610-X PMID: 16257377]
[50]
Klepeis JL, Lindorff-Larsen K, Dror RO, Shaw DE. Long-timescale molecular dynamics simulations of protein structure and function. Curr Opin Struct Biol 2009; 19(2): 120-7.
[http://dx.doi.org/10.1016/j.sbi.2009.03.004] [PMID: 19361980]
[51]
Schlick T. Molecular modeling and simulation: an interdisciplinary guide: an interdisciplinary guide. 2nd ed. Springer Science & Business Media 2010; p. 21.
[http://dx.doi.org/10.1007/978-1-4419-6351-2]
[52]
Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJ. GROMACS: fast, flexible, and free. J Comput Chem 2005; 26(16): 1701-18.
[http://dx.doi.org/10.1002/jcc.20291] [PMID: 16211538]
[53]
Berendsen HJ, van der Spoel D, van Drunen R. GROMACS: a message-passing parallel molecular dynamics implementation. Comput Phys Commun 1995; 91: 43-56.
[http://dx.doi.org/10.1016/0010-4655(95)00042-E]
[54]
Moroy G, Martiny VY, Vayer P, Villoutreix BO, Miteva MA. Toward in silico structure-based ADMET prediction in drug discovery. Drug Discov Today 2012; 17(1-2): 44-55.
[http://dx.doi.org/10.1016/j.drudis.2011.10.023] [PMID: 22056716]
[55]
Sun H, Scott DO. Structure-based drug metabolism predictions for drug design. Chem Biol Drug Des 2010; 75(1): 3-17.
[http://dx.doi.org/10.1111/j.1747-0285.2009.00899.x PMID: 19878193]
[56]
Unwalla RJ, Cross JB, Salaniwal S, et al. Using a homology model of cytochrome P450 2D6 to predict substrate site of metabolism. J Comput Aided Mol Des 2010; 24(3): 237-56.
[http://dx.doi.org/10.1007/s10822-010-9336-6] [PMID: 20361239]
[57]
Shi R, Li J, Cao X, Zhu X, Lu X. Exploration of the binding of proton pump inhibitors to human P450 2C9 based on docking and molecular dynamics simulation. J Mol Model 2011; 17(8): 1941-51.
[http://dx.doi.org/10.1007/s00894-010-0903-5] [PMID: 21120554]
[58]
King CD, Rios GR, Green MD, Tephly TR. UDP-glucuronosyltransferases. Curr Drug Metab 2000; 1(2): 143-61.
[http://dx.doi.org/10.2174/1389200003339171] [PMID: 11465080]
[59]
Song JH, Cui L, An LB, et al. Inhibition of UDP-Glucuronosyl-transferases (UGTs) Activity by constituents of Schisandra chinensis. Phytother Res 2015; 29(10): 1658-64.
[http://dx.doi.org/10.1002/ptr.5395] [PMID: 26084208]
[60]
He XM, Carter DC. Atomic structure and chemistry of human serum albumin. Nature 1992; 358(6383): 209-15.
[http://dx.doi.org/10.1038/358209a0] [PMID: 1630489]
[61]
Rabbani G, Ahn SN. Structure, enzymatic activities, glycation and therapeutic potential of human serum albumin: A natural cargo. Int J Biol Macromol 2019; 123: 979-90.
[http://dx.doi.org/10.1016/j.ijbiomac.2018.11.053] [PMID: 30439428]
[62]
Varshney A, Rehan M, Subbarao N, Rabbani G, Khan RH. Elimination of endogenous toxin, creatinine from blood plasma depends on albumin conformation: site specific uremic toxicity & impaired drug binding. PLoS One 2011; 6(2)e17230
[http://dx.doi.org/10.1371/journal.pone.0017230] [PMID: 21386972]
[63]
Ishtikhar M, Rabbani G, Khan S, Khan RH. Biophysical investigation of thymoquinone binding to ‘N’and ’B’isoforms of human serum albumin: exploring the interaction mechanism and radical scavenging activity. RSC Advances 2015; 5: 18218-32.
[http://dx.doi.org/10.1039/C4RA09892G]
[64]
Ishtikhar M, Rabbani G, Khan RH. Interaction of 5-fluoro-5′-deoxyuridine with human serum albumin under physiological and non-physiological condition: a biophysical investigation. Colloids Surf B Biointerfaces 2014; 123: 469-77.
[http://dx.doi.org/10.1016/j.colsurfb.2014.09.044] [PMID: 25448717]
[65]
Ahmad E, Rabbani G, Zaidi N, et al. Stereo-selectivity of human serum albumin to enantiomeric and isoelectronic pollutants dissected by spectroscopy, calorimetry and bioinformatics. PLoS One 2011; 6(11)e26186
[http://dx.doi.org/10.1371/journal.pone.0026186] [PMID: 22073150]
[66]
Ahmad E, Rabbani G, Zaidi N, Ahmad B, Khan RH. Pollutant-induced modulation in conformation and β-lactamase activity of human serum albumin. PLoS One 2012; 7(6)e38372
[http://dx.doi.org/10.1371/journal.pone.0038372] [PMID: 22685563]
[67]
Rabbani G, Baig MH, Lee EJ, Cho WK, Ma JY, Choi I. Biophysical study on the interaction between eperisone hydrochloride and human serum albumin using spectroscopic, calorimetric, and molecular docking analyses. Mol Pharm 2017; 14(5): 1656-65.
[http://dx.doi.org/10.1021/acs.molpharmaceut.6b01124 PMID: 28380300]
[68]
Rabbani G, Baig MH, Jan AT, et al. Binding of erucic acid with human serum albumin using a spectroscopic and molecular docking study. Int J Biol Macromol 2017; 105(Pt 3): 1572-80.
[http://dx.doi.org/10.1016/j.ijbiomac.2017.04.051] [PMID: 28414112]
[69]
Rabbani G, Lee EJ, Ahmad K, Baig MH, Choi I. Binding of tolperisone hydrochloride with human serum albumin: effects on the conformation, thermodynamics, and activity of HSA. Mol Pharm 2018; 15(4): 1445-56.
[http://dx.doi.org/10.1021/acs.molpharmaceut.7b00976 PMID: 29432019]
[70]
Abdullah SM, Fatma S, Rabbani G, Ashraf JM. A spectroscopic and molecular docking approach on the binding of tinzaparin sodium with human serum albumin. J Mol Struct 2017; 1127: 283-8.
[http://dx.doi.org/10.1016/j.molstruc.2016.07.108]
[71]
Nishi K, Ono T, Nakamura T, et al. Structural insights into differences in drug-binding selectivity between two forms of human α1-acid glycoprotein genetic variants, the A and F1*S forms. J Biol Chem 2011; 286(16): 14427-34.
[http://dx.doi.org/10.1074/jbc.M110.208926] [PMID: 21349832]
[72]
Sanguinetti MC, Tristani-Firouzi M. hERG potassium channels and cardiac arrhythmia. Nature 2006; 440(7083): 463-9.
[http://dx.doi.org/10.1038/nature04710] [PMID: 16554806]
[73]
Mitcheson JS, Chen J, Lin M, Culberson C, Sanguinetti MC. A structural basis for drug-induced long QT syndrome. Proc Natl Acad Sci USA 2000; 97(22): 12329-33.
[http://dx.doi.org/10.1073/pnas.210244497] [PMID: 11005845]
[74]
Higgins CF. ABC transporters: from microorganisms to man. Annu Rev Cell Biol 1992; 8: 67-113.
[http://dx.doi.org/10.1146/annurev.cb.08.110192.000435 PMID: 1282354]
[75]
O’Mara ML, Tieleman DP. P-glycoprotein models of the apo and ATP-bound states based on homology with Sav1866 and MalK. FEBS Lett 2007; 581(22): 4217-22.
[http://dx.doi.org/10.1016/j.febslet.2007.07.069] [PMID: 17706648]
[76]
Becker JP, Depret G, Van Bambeke F, Tulkens PM, Prévost M. Molecular models of human P-glycoprotein in two different catalytic states. BMC Struct Biol 2009; 9: 3.
[http://dx.doi.org/10.1186/1472-6807-9-3] [PMID: 19159494]
[77]
Gupta M, Sharma R, Kumar A. Docking techniques in pharmacology: How much promising? Comput Biol Chem 2018; 76: 210-7.
[http://dx.doi.org/10.1016/j.compbiolchem.2018.06.005 PMID: 30067954]
[78]
Kant K, Lal UR, Kumar A, Ghosh M. A merged molecular docking, ADME-T and dynamics approaches towards the genus of Arisaema as herpes simplex virus type 1 and type 2 inhibitors. Comput Biol Chem 2019; 78: 217-26.
[http://dx.doi.org/10.1016/j.compbiolchem.2018.12.005 PMID: 30579134]
[79]
Gupta M, Kant K, Sharma R, Kumar A. Evaluation of In Silico Anti-parkinson Potential of β-asarone. Cent Nerv Syst Agents Med Chem 2018; 18(2): 128-35.
[http://dx.doi.org/10.2174/1871524918666180416153742 PMID: 29658442]
[80]
Sumathy R, Ashwath SK, Gopalakrishan VK. Theoretical modeling and docking studies of silkworm Serotonin receptor. J Proteomics Bioinform 2012; 5: 230-4.
[http://dx.doi.org/10.4172/jpb.1000242]
[81]
Kumar A, Sasmal D, Sharma N. Deltamethrin induced an apoptogenic signalling pathway in murine thymocytes: exploring the molecular mechanism. J Appl Toxicol 2014; 34(12): 1303-10.
[http://dx.doi.org/10.1002/jat.2948] [PMID: 24217896]
[82]
Kumar A, Sasmal D, Bhaskar A, Mukhopadhyay K, Thakur A, Sharma N. Deltamethrin-induced oxidative stress and mitochondrial caspase-dependent signaling pathways in murine splenocytes. Environ Toxicol 2016; 31(7): 808-19.
[http://dx.doi.org/10.1002/tox.22091] [PMID: 25534813]
[83]
Kumar A, Sasmal D, Sharma N. Immunomodulatory role of piperine in deltamethrin induced thymic apoptosis and altered immune functions. Environ Toxicol Pharmacol 2015; 39(2): 504-14.
[http://dx.doi.org/10.1016/j.etap.2014.12.021] [PMID: 25682002]
[84]
Kumar A, Sharma N. Comparative efficacy of piperine and curcumin in deltamethrin induced splenic apoptosis and altered immune functions. Pestic Biochem Physiol 2015; 119: 16-27.
[http://dx.doi.org/10.1016/j.pestbp.2015.03.003] [PMID: 25868812]
[85]
Kumar A, Sasmal D, Jadav SS, Sharma N. Mechanism of immunoprotective effects of curcumin in DLM-induced thymic apoptosis and altered immune function: an in silico and in vitro study. Immunopharmacol Immunotoxicol 2015; 37(6): 488-98.
[http://dx.doi.org/10.3109/08923973.2015.1091004 PMID: 26471321]
[86]
Yang L, Chen J, He L. Harvesting candidate genes responsible for serious adverse drug reactions from a chemical-protein interactome. PLOS Comput Biol 2009; 5(7)e1000441
[http://dx.doi.org/10.1371/journal.pcbi.1000441] [PMID: 19629158]
[87]
Li CY, Yu Q, Ye ZQ, et al. A nonsynonymous SNP in human cytosolic sialidase in a small Asian population results in reduced enzyme activity: potential link with severe adverse reactions to oseltamivir. Cell Res 2007; 17(4): 357-62.
[http://dx.doi.org/10.1038/cr.2007.27] [PMID: 17426694]
[88]
Drwal MN, Banerjee P, Dunkel M, Wettig MR, Preissner R. ProTox: a web server for the in silico prediction of rodent oral toxicity. Nucleic Acids Res 2014; 42: W53-8.
[http://dx.doi.org/10.1093/nar/gku401] [PMID: 24838562]
[89]
Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Raghava GP. Open Source Drug Discovery Consortium. In silico approach for predicting toxicity of peptides and proteins. PLoS One 2013; 8(9)e73957
[http://dx.doi.org/10.1371/journal.pone.0073957] [PMID: 24058508]

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