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

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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.

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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. The published manuscript is available at: https://doi.org/10.2174/1568026620666200820145412

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This work received financial support from Fundação para a Ciência e a Tecnologia (FCT/MEC). The work of M. G.-D. and M. N. D. S. Cordeiro was supported by UID/QUI/50006/2019 with funding from FCT/MCTES through national funds. J.M.R acknowledge Xunta de Galicia (ED431B 2017/21, ED41E2018/08). Also, the authors acknowledge the financial support from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES), CNPq, and the Centro Nacional de Processamento de Alto Desempenho (CENAPAD) for the computational time.

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