Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

A Comparative Study to Explore the Effect of Different Compounds in Immune Proteins of Human Beings Against Tuberculosis: An In-silico Approach

Author(s): Manish Kumar Tripathi, Mohammad Yasir, Pushpendra Singh and Rahul Shrivastava*

Volume 15, Issue 2, 2020

Page: [155 - 164] Pages: 10

DOI: 10.2174/1574893614666190226153553

Price: $65

Abstract

Background: The lungs are directly exposed to pollutants, pathogens, allergens, and chemicals, which might lead to physiological disorders. During the Bhopal gas disaster, the lungs of the victims were exposed to various chemicals. Here, using molecular modelling studies, we describe the effects of these chemicals (Dimethyl urea, Trimethyl urea, Trimethyl isocyanurate, Alphanaphthol, Butylated hydroxytoluene and Carbaryl) on pulmonary immune proteins.

Objectives: In the current study, we performed molecular modelling methods like molecular docking and molecular dynamics simulation studies to identify the effects of hydrolytic products of MIC and dumped residues on the pulmonary immune proteins.

Methods: Molecular docking studies of (Dimethyl urea, Trimethyl urea, Trimethyl isocyanurate, Alphanaphthol, Butylated hydroxytoluene and Carbaryl) on pulmonary immune proteins was performed using the Autodock 4.0 tool, and gromacs was used for the molecular dynamics simulation studies to get an insight into the possible mode of protein-ligand interactions. Further, in silico ADMET studies was performed using the TOPKAT protocol of discovery studio.

Results: From docking studies, we found that surfactant protein-D is inhibited most by the chemicals alphanaphthol (dock score, -5.41Kcal/mole), butylated hydroxytoluene (dock score,-6.86 Kcal/mole), and carbaryl (dock score,-6.1 Kcal/mole). To test their stability, the obtained dock poses were placed in a lipid bilayer model system mimicking the pulmonary surface. Molecular dynamics simulations suggest a stable interaction between surfactant protein-D and carbaryl.

Conclusion: This, study concludes that functioning of surfactant protein-D is directly or indirectly affected by the carbaryl chemical, which might account for the increased susceptibility of Bhopal gas disaster survivors to pulmonary tuberculosis.

Keywords: Lungs, surfactant protein, autodock, molecular dynamics, tuberculosis, in-silico.

« Previous Next »
Graphical Abstract

[1]
Wright JR. Immunoregulatory functions of surfactant proteins. Nat Rev Immunol 2005; 5(1): 58-68.
[http://dx.doi.org/10.1038/nri1528] [PMID: 15630429]
[2]
Kingma PS, Whitsett JA. In defense of the lung: surfactant protein A and surfactant protein D. Curr Opin Pharmacol 2006; 6(3): 277-83.
[http://dx.doi.org/10.1016/j.coph.2006.02.003] [PMID: 16580255]
[3]
Vigerust DJ, Shepherd VL. Virus glycosylation: role in virulence and immune interactions. Trends Microbiol 2007; 15(5): 211-8.
[http://dx.doi.org/10.1016/j.tim.2007.03.003] [PMID: 17398101]
[4]
Erpenbeck VJ, Krug N, Hohlfeld JM. Therapeutic use of surfactant components in allergic asthma. Naunyn Schmiedebergs Arch Pharmacol 2009; 379(3): 217-24.
[http://dx.doi.org/10.1007/s00210-008-0354-z] [PMID: 18854984]
[5]
Kishore U, Greenhough TJ, Waters P, et al. Surfactant proteins SP-A and SP-D: structure, function and receptors. Mol Immunol 2006; 43(9): 1293-315.
[http://dx.doi.org/10.1016/j.molimm.2005.08.004] [PMID: 16213021]
[6]
Gupta G, Surolia A. Collectins: sentinels of innate immunity. BioEssays 2007; 29(5): 452-64.
[http://dx.doi.org/10.1002/bies.20573] [PMID: 17450595]
[7]
Pastva AM, Wright JR, Williams KL. Immunomodulatory roles of surfactant proteins A and D: implications in lung disease. Proc Am Thorac Soc 2007; 4(3): 252-7.
[http://dx.doi.org/10.1513/pats.200701-018AW] [PMID: 17607008]
[8]
Haczku A. Protective role of the lung collectins surfactant protein A and surfactant protein D in airway inflammation. J Allergy Clin Immunol 2008; 122(5): 861-79.
[http://dx.doi.org/10.1016/j.jaci.2008.10.014] [PMID: 19000577]
[9]
Yu SH, Possmayer F. Lipid compositional analysis of pulmonary surfactant monolayers and monolayer-associated reservoirs. J Lipid Res 2003; 44(3): 621-9.
[http://dx.doi.org/10.1194/jlr.M200380-JLR200] [PMID: 12562850]
[10]
Ferguson JS, Schlesinger LS. Pulmonary surfactant in innate immunity and the pathogenesis of tuberculosis. Tuber Lung Dis 2000; 80(4-5): 173-84.
[http://dx.doi.org/10.1054/tuld.2000.0242] [PMID: 11052906]
[11]
Mehta PS, Mehta AS, Mehta SJ, Makhijani AB. Bhopal tragedy’s health effects. A review of methyl isocyanate toxicity. JAMA 1990; 264(21): 2781-7.
[http://dx.doi.org/10.1001/jama.1990.03450210081037] [PMID: 2232065]
[12]
Broughton E. The Bhopal disaster and its aftermath: a review. Environ Health 2005; 4(1): 6.
[http://dx.doi.org/10.1186/1476-069X-4-6] [PMID: 15882472]
[13]
Dhara VR, Dhara R, Acquilla SD, Cullinan P. Personal exposure and long-term health effects in survivors of the union carbide disaster at bhopal. Environ Health Perspect 2002; 110(5): 487-500.
[http://dx.doi.org/10.1289/ehp.02110487] [PMID: 12003752]
[14]
Mishra PK, Samarth RM, Pathak N, Jain SK, Banerjee S, Maudar KK. Bhopal Gas Tragedy: review of clinical and experimental findings after 25 years. Int J Occup Med Environ Health 2009; 22(3): 193-202.
[http://dx.doi.org/10.2478/v10001-009-0028-1] [PMID: 19819837]
[15]
Mishra PK, Bhargava A, Pathak N, et al. Molecular surveillance of hepatitis and tuberculosis infections in a cohort exposed to methyl isocyanate. Int J Occup Med Environ Health 2011; 24(1): 94-101.
[http://dx.doi.org/10.2478/s13382-011-0006-2] [PMID: 21468906]
[16]
Akhtar N. Identification of pulmonary samples of Mycobacterium tuberculosis NTM isolated from the victims of the Bhopal gas disaster by using PCR-RFLP methodology. Nanobiotech universal 2010; 1(1): 67-9.
[17]
Shrivastava R, Yasir M, Tripathi M, Singh P. In silico interaction of methyl isocyanate with immune protein responsible for Mycobacterium tuberculosis infection using molecular docking. Toxicol Ind Health 2016; 32(1): 162-7.
[http://dx.doi.org/10.1177/0748233713498447] [PMID: 24081639]
[18]
Tripathi MK, Yasir M, Gurjar VS, Bose P, Dubey A, Shrivastava R. Insights from the molecular docking of hydrolytic products of methyl isocyanate (MIC) to inhibition of human immune proteins. Interdiscip Sci 2015; 7(3): 287-94.
[http://dx.doi.org/10.1007/s12539-015-0012-3] [PMID: 26297312]
[19]
Tripathi MK, Yasir M, Singh P, Tayubi IA, Gupta R, Shrivastava R. Toxic effect of chemicals dumped in premises of UCIL, Bhopal leading to environmental pollution: An in silico approach. Asian Pac J Trop Dis 2016; 6(4): 284-90.
[http://dx.doi.org/10.1016/S2222-1808(15)61032-5]
[20]
Liu B, Liu F, Wang X, Chen J, Fang L, Chou KC. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res 2015; 43(W1): W65-71
[http://dx.doi.org/10.1093/nar/gkv458] [PMID: 25958395]
[21]
Liu B. BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches. Brief Bioinform 2019; 20(4): 1280-94.
[PMID: 29272359]
[22]
Kinjo AR, Suzuki H, Yamashita R, et al. Protein Data Bank Japan (PDBj): maintaining a structural data archive and resource description framework format. Nucleic Acids Res 2012; 40(Database issue): D453-60.
[http://dx.doi.org/10.1093/nar/gkr811] [PMID: 21976737]
[23]
O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform 2011; 7: 3-33.
[24]
Morris GM, Goodsell DS, Halliday RS, et al. Automated docking using a Lamarckian genetic algorithm and empirical binding free energy function. J Comput Chem 1998; 19: 1639-62.
[http://dx.doi.org/10.1002/(SICI)1096-987X(19981115)19:14<1639:AID-JCC10>3.0.CO;2-B]
[25]
Berendsen HJC. vander Spoel D, van Drunen R. GROMACS: a messagepassing parallel molecular dynamics implementation. Comput Phys Commun 1995; 91: 43-56.
[http://dx.doi.org/10.1016/0010-4655(95)00042-E]
[26]
Lindah E, Hess B. vander Spoel D. GROMACS 3.0: a package for molecular simulation and trajectory analysis. J Mol Model 2001; 7: 306-17.
[http://dx.doi.org/10.1007/s008940100045]
[27]
Jo S, Kim T, Iyer VG, Im W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem 2008; 29(11): 1859-65.
[http://dx.doi.org/10.1002/jcc.20945] [PMID: 18351591]
[28]
Tieleman DP, Berendsen HJC. Molecular dynamics simulations of a fully hydrated dipalmitoylphosphatidylcholine bilayer with different macroscopic boundary conditions and parameters. J Chem Phys 1996; 105: 4871-80.
[http://dx.doi.org/10.1063/1.472323]
[29]
Lomize MA, Lomize AL, Pogozheva ID, Mosberg HI. OPM: orientations of proteins in membranes database. Bioinformatics 2006; 22(5): 623-5.
[http://dx.doi.org/10.1093/bioinformatics/btk023] [PMID: 16397007]
[30]
Schüttelkopf AW, van Aalten DM. PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr D Biol Crystallogr 2004; 60(Pt 8): 1355-63.
[http://dx.doi.org/10.1107/S0907444904011679] [PMID: 15272157]
[31]
Van Gunsteren WF, Daura X, Mark AE. GROMOS force fieldEncyclopedia of computational chemistry. Chichester: Wiley-VCH 1998.
[32]
Cheng A, Merz KM Jr. Prediction of aqueous solubility of a diverse set of compounds using quantitative structure-property relationships. J Med Chem 2003; 46(17): 3572-80.
[http://dx.doi.org/10.1021/jm020266b] [PMID: 12904062]
[33]
Egan WJ, Merz KM Jr, Baldwin JJ. Prediction of drug absorption using multivariate statistics. J Med Chem 2000; 43(21): 3867-77.
[http://dx.doi.org/10.1021/jm000292e] [PMID: 11052792]
[34]
Colmenarejo G, Alvarez-Pedraglio A, Lavandera JL. Cheminformatic models to predict binding affinities to human serum albumin. J Med Chem 2001; 44(25): 4370-8.
[http://dx.doi.org/10.1021/jm010960b] [PMID: 11728183]
[35]
Kelder J, Grootenhuis PD, Bayada DM, Delbressine LP, Ploemen JP. Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs. Pharm Res 1999; 16(10): 1514-9.
[http://dx.doi.org/10.1023/A:1015040217741] [PMID: 10554091]
[36]
Susnow RG, Dixon SL. Use of robust classification techniques for the prediction of human cytochrome P450 2D6 inhibition. J Chem Inf Comput Sci 2003; 43(4): 1308-15.
[http://dx.doi.org/10.1021/ci030283p] [PMID: 12870924]
[37]
Cheng A, Dixon SL. In silico models for the prediction of dose-dependent human hepatotoxicity. J Comput Aided Mol Des 2003; 17(12): 811-23.
[http://dx.doi.org/10.1023/B:JCAM.0000021834.50768.c6] [PMID: 15124930]
[38]
Kuan SF, Rust K, Crouch E. Interactions of surfactant protein D with bacterial lipopolysaccharides. Surfactant protein D is an Escherichia coli-binding protein in bronchoalveolar lavage. J Clin Invest 1992; 90(1): 97-106.
[http://dx.doi.org/10.1172/JCI115861] [PMID: 1634623]
[39]
Eda S, Suzuki Y, Kawai T, et al. Structure of a truncated human surfactant protein D is less effective in agglutinating bacteria than the native structure and fails to inhibit haemagglutination by influenza A virus. Biochem J 1997; 323(Pt 2): 393-9.
[http://dx.doi.org/10.1042/bj3230393] [PMID: 9163329]
[40]
Madan T, Eggleton P, Kishore U, et al. Binding of pulmonary surfactant proteins A and D to Aspergillus fumigatus conidia enhances phagocytosis and killing by human neutrophils and alveolar macrophages. Infect Immun 1997; 65(8): 3171-9.
[http://dx.doi.org/10.1128/IAI.65.8.3171-3179.1997] [PMID: 9234771]
[41]
Ferguson JS, Voelker DR, McCormack FX, Schlesinger LS. Surfactant protein D binds to Mycobacterium tuberculosis bacilli and lipoarabinomannan via carbohydrate-lectin interactions resulting in reduced phagocytosis of the bacteria by macrophages. J Immunol 1999; 163(1): 312-21.
[PMID: 10384130]

Rights & Permissions Print Cite
Article Metrics
15
© 2024 Bentham Science Publishers | Privacy Policy