Computational Modeling of Environmental Co-exposure on Oil-Derived Hydrocarbon Overload by Using Substrate-Specific Transport Protein (TodX) with Graphene Nanostructures

Page: [2308 - 2325] Pages: 18

  • * (Excluding Mailing and Handling)

Abstract

Background: Bioremediation is a biotechnology field that uses living organisms to remove contaminants from soil and water; therefore, they could be used to treat oil spills from the environment.

Methods: Herein, we present a new mechanistic approach combining Molecular Docking Simulation and Density Functional Theory to modeling the bioremediation-based nanointeractions of a heterogeneous mixture of oil-derived hydrocarbons by using pristine and oxidized graphene nanostructures and the substrate-specific transport protein (TodX) from Pseudomonas putida.

Results: The theoretical evidences pointing that the binding interactions are mainly based on noncovalent bonds characteristic of physical adsorption mechanism mimicking the “Trojan-horse effect”.

Conclusion: These results open new horizons to improve bioremediation strategies in over-saturation conditions against oil-spills and expanding the use of nanotechnologies in the context of environmental modeling health and safety.

Keywords: Petroleum, TodX protein, Graphene, Molecular docking, DFT-simulation, Nanostructures.

Erratum In:
Computational Modeling of Environmental Co-exposure on Oil-Derived Hydrocarbon Overload by Using Substrate-Specific Transport Protein (TodX) with Graphene Nanostructures

Graphical Abstract

[1]
Barron, M.G. Ecological impacts of the deepwater horizon oil spill: implications for immunotoxicity. Toxicol. Pathol., 2012, 40(2), 315-320.
[http://dx.doi.org/10.1177/0192623311428474 ] [PMID: 22105647]
[2]
Goldstein, B.D.; Osofsky, H.J.; Lichtveld, M.Y. The Gulf oil spill. N. Engl. J. Med., 2011, 364(14), 1334-1348.
[http://dx.doi.org/10.1056/NEJMra1007197 ] [PMID: 21470011]
[3]
Beyer, J.; Trannum, H.C.; Bakke, T.; Hodson, P.V.; Collier, T.K. Environmental effects of the deepwater horizon oil spill: A review. Mar. Pollut. Bull., 2016, 110(1), 28-51.
[http://dx.doi.org/10.1016/j.marpolbul.2016.06.027 ] [PMID: 27301686]
[4]
Balba, M.T.; Al-Awadhi, N.; Al-Daher, R. Bioremediation of oil-contaminated soil: microbiological methods for feasibility assessment and field evaluation. J. Microbiol. Methods, 1998, 32, 155-164.
[http://dx.doi.org/10.1016/S0167-7012(98)00020-7]
[5]
Adams, G.O.; Fufeyin, P.T.; Okoro, S.E.; Ehinomen, I. Bioremediation, biostimulation and bioaugmention: A review. Inter. Journ. Environm. Bioremed & Biod, 2015, 3, 28-39.
[6]
Raghavan, P. U. M.; Vivekanandan, M. Bioremediation of oil-spilled sites through seeding of naturally adapted Pseudomonas putida. Intern biodeterioration biodegradation, 1999, 44, 29-32.
[http://dx.doi.org/10.1016/S0964-8305(99)00048-7]
[7]
Nwachukwu, S.C.; James, P.; Gurney, T.R. Inorganic nutrient utilisation by “adapted” Pseudomonas putida strain used in the bioremediation of agricultural soil polluted with crude petroleum. J. Environ. Biol., 2001, 22(3), 153-162.
[PMID: 12017254]
[8]
Belchik, S.M.; Schaeffer, S.M.; Hasenoehrl, S.; Xun, L. A β-barrel outer membrane protein facilitates cellular uptake of polychlorophenols in Cupriavidus necator. Biodegradation, 2010, 21(3), 431-439.
[http://dx.doi.org/10.1007/s10532-009-9313-8 ] [PMID: 19937267]
[9]
van den Berg, B. Going forward laterally: transmembrane passage of hydrophobic molecules through protein channel walls. ChemBioChem, 2010, 11(10), 1339-1343.
[http://dx.doi.org/10.1002/cbic.201000105 ] [PMID: 20533493]
[10]
Mardani, G.; Mahvi, A.H.; Hashemzadeh-Chaleshtori, M.; Naseri, S.; Dehghani, M.H.; Ghasemi-Dehkordi, P. Application of genetically engineered dioxygenase producing pseudomonas putida on decomposition of oil from spiked soil. Jundishapur J. Nat. Pharm. Prod., 2017, 12(3)(Suppl.), e64313.
[http://dx.doi.org/10.5812/jjnpp.64313]
[11]
Kharisov, B.I.; Dias, H.R.; Kharissova, O.V. Nanotechnology-based remediation of petroleum impurities from water. J. Petrol. Sci. Eng., 2014, 122, 705-718.
[http://dx.doi.org/10.1016/j.petrol.2014.09.013]
[12]
Wang, J.; Chen, Z.; Chen, B. Adsorption of polycyclic aromatic hydrocarbons by graphene and graphene oxide nanosheets. Environ. Sci. Technol., 2014, 48(9), 4817-4825.
[http://dx.doi.org/10.1021/es405227u ] [PMID: 24678934]
[13]
Iqbal, M.Z.; Abdala, A.A. Oil spill cleanup using graphene. Environ. Sci. Pollut. Res. Int., 2013, 20(5), 3271-3279.
[http://dx.doi.org/10.1007/s11356-012-1257-6 ] [PMID: 23093418]
[14]
Robbins, W.K.; Hsu, C.S. Petroleum, Composition. In: Kirk-Othmer Encyclopedia of Chemical Technology; John Wiley & Sons, Inc: Hoboken, 2000.
[http://dx.doi.org/10.1002/0471238961.0315131618150202.a01]
[15]
Naasz, S.; Altenburger, R.; Kühnel, D. Environmental mixtures of nanomaterials and chemicals: The Trojan-horse phenomenon and its relevance for ecotoxicity. Sci. Total Environ., 2018, 635, 1170-1181.
[http://dx.doi.org/10.1016/j.scitotenv.2018.04.180 ] [PMID: 29710572]
[16]
Xu, X.; Liu, W.; Tian, S.; Wang, W.; Qi, Q.; Jiang, P.; Gao, X.; Li, F.; Li, H.; Yu, H. Petroleum hydrocarbon-degrading bacteria for the remediation of oil pollution under aerobic conditions: a perspective analysis. Front. Microbiol., 2018, 9, 2885.
[http://dx.doi.org/10.3389/fmicb.2018.02885 ] [PMID: 30559725]
[17]
Adebajo, M.O.; Frost, R.L.; Kloprogge, J.T.; Carmody, O.; Kokot, S. Porous materials for oil spill cleanup: a review of synthesis and absorbing properties. J. Porous Mater., 2003, 10, 159-170.
[http://dx.doi.org/10.1023/A:1027484117065]
[18]
Martín de Lucía, I.; Campos-Mañas, M.C.; Agüera, A.; Rodea-Palomares, I.G. Francisco Leganés, Fernández-Piñas, F., and Rosa, R. Reverse Trojan-horse effect decreased wastewater toxicity in the presence of inorganic nanoparticles. Environ. Sci. Nano, 2017, 4, 1273-1282.
[http://dx.doi.org/10.1039/C6EN00708B]
[19]
Hsiao, I.L.; Hsieh, Y.K.; Wang, C.F.; Chen, I.C.; Huang, Y.J. Trojan-horse mechanism in the cellular uptake of silver nanoparticles verified by direct intra- and extracellular silver speciation analysis. Environ. Sci. Technol., 2015, 49(6), 3813-3821.
[http://dx.doi.org/10.1021/es504705p ] [PMID: 25692749]
[20]
Jiménez, J.; Doerr, S.; Martínez-Rosell, G.; Rose, A.S.; De Fabritiis, G. DeepSite: protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics, 2017, 33(19), 3036-3042.
[http://dx.doi.org/10.1093/bioinformatics/btx350 ] [PMID: 28575181]
[21]
Feinstein, W.P.; Brylinski, M. Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J. Cheminform., 2015, 7, 18.
[http://dx.doi.org/10.1186/s13321-015-0067-5 ] [PMID: 26082804]
[22]
Forli, S.; Huey, R.; Pique, M.E.; Sanner, M.F.; Goodsell, D.S.; Olson, A.J. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc., 2016, 11(5), 905-919.
[http://dx.doi.org/10.1038/nprot.2016.051 ] [PMID: 27077332]
[23]
Vincent, B. Chen, W. Bryan Arendall III, Jeffrey J. Headd, Daniel A. Keedy, Robert M. Immormino, Gary J. Kapral, Laura W. Murray, Jane S. Richardson and David C. Richardson. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr., 2010, 66, 12-21.
[24]
Mitternacht, S.; Berezovsky, I.N. Coherent conformational degrees of freedom as a structural basis for allosteric communication. PLOS Comput. Biol., 2011, 7(12), e1002301.
[http://dx.doi.org/10.1371/journal.pcbi.1002301 ] [PMID: 22174669]
[25]
Keskin, O.; Durell, S.R.; Bahar, I.; Jernigan, R.L.; Covell, D.G. Relating molecular flexibility to function: a case study of tubulin. Biophys. J., 2002, 83(2), 663-680.
[http://dx.doi.org/10.1016/S0006-3495(02)75199-0 ] [PMID: 12124255]
[26]
Greener, J.G.; Sternberg, M.J.E. AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis. BMC Bioinformatics, 2015, 16, 335.
[http://dx.doi.org/10.1186/s12859-015-0771-1 ] [PMID: 26493317]
[27]
Hearn, E.M.; Patel, D.R.; van den Berg, B. Outer-membrane transport of aromatic hydrocarbons as a first step in biodegradation. Proc. Natl. Acad. Sci. USA, 2008, 105(25), 8601-8606.
[http://dx.doi.org/10.1073/pnas.0801264105 ] [PMID: 18559855]
[28]
Trott, O.; Olson, A.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461.
[PMID: 19499576]
[29]
Morris, G.M.; Goodsell, D.S.; Halliday, R.S.; Huey, R.; Hart, W.E.; Belew, R.K.; Olson, A.J. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem., 1998, 19, 1639-1662.
[http://dx.doi.org/10.1002/(SICI)1096-987X(19981115)19:14<1639::AID-JCC10>3.0.CO;2-B]
[30]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res., 2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235 ] [PMID: 10592235]
[31]
Xie, Z.R.; Hwang, M.J. An interaction-motif-based scoring function for protein-ligand docking. BMC Bioinformatics, 2010, 11, 298.
[http://dx.doi.org/10.1186/1471-2105-11-298 ] [PMID: 20525216]
[32]
Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A.; Wang, J.; Yu, B.; Zhang, J.; Bryant, S.H. PubChem substance and compound databases. Nucleic Acids Res., 2016, 44(D1), D1202-D1213.
[http://dx.doi.org/10.1093/nar/gkv951 ] [PMID: 26400175]
[33]
Feldman, H.A. Mathematical theory of complex ligand-binding systems of equilibrium: some methods for parameter fitting. Anal. Biochem., 1972, 48(2), 317-338.
[http://dx.doi.org/10.1016/0003-2697(72)90084-X ] [PMID: 4627078]
[34]
Haiech, J.; Gendrault, Y.; Kilhoffer, M.C.; Ranjeva, R.; Madec, M.; Lallement, C. A general framework improving teaching ligand binding to a macromolecule. Biochim. Biophys. Acta, 2014, 1843(10), 2348-2355.
[http://dx.doi.org/10.1016/j.bbamcr.2014.03.013 ] [PMID: 24657812]
[35]
Seeliger, D.; de Groot, B.L. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J. Comput. Aided Mol. Des., 2010, 24(5), 417-422.
[http://dx.doi.org/10.1007/s10822-010-9352-6 ] [PMID: 20401516]
[36]
Laskowski, R.A.; Swindells, M.B. LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J. Chem. Inf. Model., 2011, 51(10), 2778-2786.
[http://dx.doi.org/10.1021/ci200227u ] [PMID: 21919503]
[37]
Tao, A.; Huang, Y.; Shinohara, Y.; Caylor, M.L.; Pashikanti, S.; Xu, D. ezCADD: A rapid 2d/3d visualization-enabled web modeling environment for democratizing computer-aided drug design. J. Chem. Inf. Model., 2019, 59(1), 18-24.
[http://dx.doi.org/10.1021/acs.jcim.8b00633 ] [PMID: 30403855]
[38]
da Silveira, C.H.; Pires, D.E.; Minardi, R.C.; Ribeiro, C.; Veloso, C.J.; Lopes, J.C.; Meira, W., Jr; Neshich, G.; Ramos, C.H.; Habesch, R.; Santoro, M.M. Protein cutoff scanning: A comparative analysis of cutoff dependent and cutoff free methods for prospecting contacts in proteins. Proteins, 2009, 74(3), 727-743.
[http://dx.doi.org/10.1002/prot.22187 ] [PMID: 18704933]
[39]
Rarey, M.; Kramer, B.; Lengauer, T. Multiple automatic base selection: protein-ligand docking based on incremental construction without manual intervention. J. Comput. Aided Mol. Des., 1997, 11(4), 369-384.
[http://dx.doi.org/10.1023/A:1007913026166 ] [PMID: 9334903]
[40]
Shoichet, B.K. Virtual screening of chemical libraries. Nature, 2004, 432(7019), 862-865.
[http://dx.doi.org/10.1038/nature03197 ] [PMID: 15602552]
[41]
Soler, J.M.; Artacho, E.; Gale, J.D.; García, A.; Junquera, J.; Ordejón, P.; Sánchez-Portal, D. The SIESTA method for ab initio order-N materials simulation. J. Phys. Condens. Matter, 2002, 14, 2745.
[http://dx.doi.org/10.1088/0953-8984/14/11/302]
[42]
Kohn, W.; Sham, L.J. Self-consistent equations including exchange and correlation effects. Phys. Rev., 1965, 140, 1133.
[http://dx.doi.org/10.1103/PhysRev.140.A1133]
[43]
Perdew, J.P.; Zunger, A. Self-interaction correction to density-functional approximations for many-electron systems. Phys. Rev. B Condens. Matter, 1981, 23, 5048.
[http://dx.doi.org/10.1103/PhysRevB.23.5048]
[44]
Troullier, N.; Martins, J.L. Efficient pseudopotentials for plane-wave calculations. Phys. Rev. B Condens. Matter, 1991, 43(3), 1993-2006.
[http://dx.doi.org/10.1103/PhysRevB.43.1993 ] [PMID: 9997467]
[45]
Adipah, S. Introduction of petroleum hydrocarbons contaminants and its human effects. J. Env. Sci. Pub. Health., 2019, 3, 001-009.
[46]
Das, N.; Chandran, P. Microbial degradation of petroleum hydrocarbon contaminants: An overview. Biotechnol. Res. Int., 2010, 10, 1-13.
[PMID: 21350672]
[47]
Gray, J.S. Biomagnification in marine systems: the perspective of an ecologist. Mar. Pollut. Bull., 2002, 45(1-12), 46-52.
[http://dx.doi.org/10.1016/S0025-326X(01)00323-X] [PMID: 12398366]
[48]
Riisgaard, H.U.; Hansen, S. Biomagnification of mercury in a marine grazing food-chain: algal cells Phaeodactylum tricornutum, mussels Mytilus edulis and flounders Platichthys flesusstudied by means of a stepwise-reduction-CVAA method. Mar. Ecol. Prog. Ser., 1990, 62, 259-270.
[http://dx.doi.org/10.3354/meps062259]
[49]
Strandberg, B.; Bandh, C.; van Bavel, B.; Bergqvist, P.A.; Broman, D.; Näf, C.; Pettersen, H.; Rappe, C. Concentrations, biomagnification and spatial variation of organochlorine compounds in a pelagic food web in the northern part of the Baltic Sea. Sci. Total Environ., 1998, 217(1-2), 143-154.
[http://dx.doi.org/10.1016/S0048-9697(98)00173-9 ] [PMID: 9695178]
[50]
Ikeda, K.; Hirokawa, T.; Higo, J.; Tomii, K. Protein-segment universe exhibiting transitions at intermediate segment length in conformational subspaces. BMC Struct. Biol., 2008, 8, 37.
[http://dx.doi.org/10.1186/1472-6807-8-37 ] [PMID: 18700043]
[51]
Patlewicz, G.; Worth, A.P.; Ball, N. Validation of computational methods. Adv. Exp. Med. Biol., 2016, 856, 165-187.
[http://dx.doi.org/10.1007/978-3-319-33826-2_6 ] [PMID: 27671722]
[52]
OECD; Principles for the validation, for regulatory purposes of (quantitative) structure activity relationship model, 2019.Available from: . http: www.oecd.org/
[53]
Clive Willis; ISO TC 229 International Standards for Nanotechnology, 2011.Available from: . https://www.tappi.org/content/events/09nano/papers/09nan45.pdf
[54]
Benko, H. ISO Technical Committee 229 Nanotechnologies. In: Metrology and Standardization of Nanotechnology: Protocols and Industrial Innovations; Wiley-VCH: Weinheim, 2017, pp. 261-267.
[55]
Ghosh, S.; Ojha, P.K.; Roy, K. Exploring QSPR modeling for adsorption of hazardous synthetic organic chemicals (SOCs) by SWCNTs. Chemosphere, 2019, 228, 545-555.
[http://dx.doi.org/10.1016/j.chemosphere.2019.04.124 ] [PMID: 31051358]
[56]
Haiech, J.; Vallet, B.; Aquaron, R.; Demaille, J.G. Ligand binding to macromolecules: determination of binding parameters by combined use of ligand buffers and flow dialysis; application to calcium-binding proteins. Anal. Biochem., 1980, 105(1), 18-23.
[http://dx.doi.org/10.1016/0003-2697(80)90416-9 ] [PMID: 6969558]
[57]
Fletcher, J.E.; Spector, A.A.; Ashbrook, J.D. Analysis of macromolecule--ligand binding by determination of stepwise equilibrium constants. Biochemistry, 1970, 9(23), 4580-4587.
[http://dx.doi.org/10.1021/bi00825a018 ] [PMID: 5474150]
[58]
Klotz, I.M.; Hunston, D.L. Mathematical models for ligand-receptor binding. Real sites, ghost sites. J. Biol. Chem., 1984, 259(16), 10060-10062.
[PMID: 6469955]
[59]
Klotz, I.M. Ligand--receptor interactions: facts and fantasies. Q. Rev. Biophys., 1985, 18(3), 227-259.
[http://dx.doi.org/10.1017/S0033583500000354 ] [PMID: 3916127]
[60]
Konkoli, Z. Safe uses of Hill’s model: an exact comparison with the Adair-Klotz model. Theor. Biol. Med. Model., 2011, 8, 10.
[http://dx.doi.org/10.1186/1742-4682-8-10 ] [PMID: 21521501]
[61]
Sánchez-Linares, I.; Pérez-Sánchez, H.; Cecilia, J.M.; García, J.M. High-throughput parallel blind virtual screening using BINDSURF. BMC Bioinformatics, 2012, 13(Suppl. 14), S13.
[http://dx.doi.org/10.1186/1471-2105-13-S14-S13 ] [PMID: 23095663]
[62]
Jauris, I.M.; Matos, C.F.; Saucier, C.; Lima, E.C.; Zarbin, A.J.G.; Fagan, S.B.; Machado, F.M.; Zanella, I. Adsorption of sodium diclofenac on graphene: a combined experimental and theoretical study. Phys. Chem. Chem. Phys., 2016, 18(3), 1526-1536.
[http://dx.doi.org/10.1039/C5CP05940B ] [PMID: 26671178]