Molecular and Interactions Modelling of PETase and Its Variant with Different Types of Crosslinker in Enzyme Immobilization
DOI:
https://doi.org/10.11113/bioprocessing.v1n1.7Keywords:
Molecular docking, plastics, enzyme immobilization, PET, mutationAbstract
Plastics are made from non-renewable resources and due to the tremendous production of plastics nowadays, they can lead to high levels of pollution. Biodegradation of plastic by utilizing enzymatic catalytic reaction is an environmentally friendly strategy that produce less or no negative carbon footprint. PETase from Ideonella sakaiensis (IsPETase) is an enzyme that able to degrade polyethylene terephthalate (PET), a building block of plastic. However, free enzyme has several limitations such as unstable in harsh conditions and lack of reusability. One of the strategies to overcome this drawback is through enzyme immobilization that able to improve the enzymatic properties. A suitable crosslinker is very important as it would determine the interactions of the enzymatic particles. Crosslinker should be chosen before performing the enzyme immobilization and this can be accomplished by molecular docking. Thus, the purpose of this research is to determine the suitability of glutaraldehyde, chitosan, dialdehyde starch (DAS) and ethylene glycol as the crosslinker for IsPETase and its variant. Three-dimensional structure of the enzymes was built and docked with different types of crosslinkers. Binding affinity and interactions between the enzymes and the crosslinkers were analyzed and it was found that chitosan has the lowest binding affinity (-7.9 kcal/mol) and the highest number of interactions. This is followed by DAS, ethylene glycol and glutaraldehyde. By using computational analysis, suitable crosslinker for IsPETase could be determine and this would a cost-effective practice in enzyme immobilization strategy.
References
Ansari, S. A., Jafri, M. A., Satar, R., Ahmad, S. I., Chibber, S. 2017. Molecular Docking as A Computational Tool for Analyzing Product Mediated Inhibition for β- Galactosidase Immobilized on Glutaraldehyde Modified Matrices. Oriental Journal Of Chemistry. 34(2): 820.
Barbosa, O. et al. 2014. Glutaraldehyde in Bio-Catalysts Design: A Useful Crosslinker and A Versatile Tool in Enzyme Immobilization. RSC Advances. 4(4): 1583–1600.
Girgih, A. T., He, R. and Aluko, R.E. 2014. Kinetics and Molecular Docking Studies of the Inhibitions of Angiotensin Converting Enzyme and Renin Activities by Hemp Seed (Cannabis sativa L.) Peptides. Journal of Agricultural and Food Chemistry. 62(18): 4135–4144.
Heo, L. and Feig, M. 2018. What Makes It Difficult To Refine Protein Models Further Via Molecular Dynamics Simulations? Proteins: Structure, Function and Bioinformatics, 86(Suppl 1): 177–188.
Homaei, A. A., Sariri, R., Vianello, F. and Stevanato, R. 2013. Enzyme Immobilization: An update. Journal of Chemical Biology. 6(4): 185–205.
Jaghoori, M. M., Bleijlevens, B. and Olabarriaga, S. D. 2016. 1001 Ways to Run Autodock Vina for Virtual Screening. Journal of Computer-Aided Molecular Design. 30(3): 237–249.
Kastritis, P. L. and Bonvin, A. M. J. J. 2013. On the Binding Affinity of Macromolecular Interactions: Daring to Ask Why Proteins Interact. Journal of the Royal Society Interface. 10(79): 20120835.
Klein, M.P. et al. 2016. Chitosan Crosslinked with Genipin as Support Matrix for Application in Food Process: Support Characterization and Β-D-Galactosidase Immobilization. Carbohydrate Polymers. 137: 184–190.
Li, S. 2017. Fundamentals of Biochemical Reaction Engineering. In: Chemical Reaction Engineering. Elsevier, pp. 491–539.
Liu, C. et al. 2019. Structural and Functional Characterization of Polyethylene Terephthalate Hydrolase from Ideonella sakaiensis. Biochemical and Biophysical Research Communications. 508(1): 289–294.
Lucero, H.A. and Kagan, H.M. 2006. Lysyl oxidase: An Oxidative Enzyme and Effector of Cell Function. Cellular and Molecular Life Sciences. 63(19–20): 2304–2316.
Migneault, I., Dartiguenave, C., Bertrand, M. J. and Waldron, K.C. 2004. Glutaraldehyde: Behavior in Aqueous Solution, Reaction with Proteins, and Application to Enzyme Crosslinking. BioTechniques. 37(5): 790–802.
Nawawi, N.N. Hashim, Z., Rahman, R.A., Murad, A.M.A., Abu Bakar, F.D., Illias, R.M. 2020. Entrapment of Porous Cross-Linked Enzyme Aggregates of Maltogenic Amylase From Bacillus lehensis G1 into Calcium Alginate for Maltooligosaccharides Synthesis. International Journal of Biological Macromolecules. 150: 80–89.
Nguyen, N. T. et al. 2020. Autodock Vina Adopts More Accurate Binding Poses but Autodock4 Forms Better Binding Affinity. Journal of Chemical Information and Modeling. 60(1): 204–211.
Oktay, B., Demir, S. and Kayaman‐Apohan, N. 2019. Preparation of a Poly (ethylene glycol) ‐Based Cross‐Linked Network from a Click Reaction for Enzyme Immobilization. ChemistrySelect. 4(20): 6055–6059.
Pace, C. N., Fu, H., Fryar, K. L., Landua, J., Trevino, S. R., Shirley, B. A., Hendricks, M. M., Iimura, S., Gajiwala, K., Scholtz, J. M., Grimsley, G. R. 2011. Contribution of Hydrophobic Interactions to Protein Stability.
Journal of Molecular Biology. 408(3): 514-28. Rogers T. 2015. How Plastics Are Made and What You Need to Know About Them [Online]. Available at: https://www.creativemechanisms.com/blog/howplastics-are-made-and-what-you-need-to-knowabout-them.
Salem, M., Mauguen, Y. and Prangé, T. 2010. Revisiting glutaraldehyde cross-linking: The Case of the ArgLys Intermolecular Doublet. Acta Crystallographica Section F: Structural Biology and Crystallization Communications. 66(3): 225–228.
Shabalin, I. G., Porebski, P. J., Minor, W. 2018. Refining the Macromolecular Model - Achieving the Best Agreement with the Data from X-ray Diffraction Experiment. Crystallography Review. 24(4): 236-262.
Skopinska-Wisniewska, J. et al., 2016. Is Dialdehyde Starch A Valuable Cross-Linking Agent For Collagen/ElastinBased Materials? Journal of Materials Science: Materials in Medicine. 27(4): 1–10.
Trott, O. and Olson, A.J. 2009. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. Journal of Computational Chemistry. 31(2).
Wu, W., Wang, Z., Cong, P. and Li, T. 2017. Accurate Prediction of Protein Relative Solvent Accessibility Using a Balanced Model. BioData Mining. 10(1): 1–14.
Yoshida, S., Hiraga, K., Takehana, T., Taniguchi, I., Yamaji, H., Maeda, Y., Toyohara, K., Miyamoto, K., Kimura, Y., Oda, K. 2016. A Bacterium That Degrades and Assimilates Poly(Ethylene Terephthalate). Science. 351(6278): 1196-1199.
Yuan, S., Chan, H. C. S. and Hu, Z. 2017. Using PyMOL as a platform for computational drug design. WIREs Computational Molecular Science. 7(2)
