Structure Predictions of Hydrolase from Thermobifida Fusca Using Computational Tools

The growing use of plastics like polyethylene terephthalate (PET) has led to increased pollution and a need for sustainable ways to break down these materials. One promising approach is using microbial enzymes through biocatalysis. This study focuses on a hydrolase enzyme from Thermobifida Fusca (T. Fusca), a heat-loving bacterium known for its stable and active properties at high temperatures, making it ideal for industrial use. The main aim of this research is to predict the 3D structure of this hydrolase using homology modelling and other computational methods. The predicted structure was carefully validated using structural assessment techniques such as the Ramachandran plot to ensure high accuracy and reliability. Sequence alignment analysis helped identify evolutionary traits and potential catalytic sites, offering insights into how the enzyme functions and interacts with PET and other substrates.

Hydrolase, plastic degradation enzyme, protein structure prediction, homology modelling

1. Arregui, L., Ayala, M., Gómez-Gil, X., Gutiérrez-Soto, G., Hernández-Luna, C. E., Herrera De Los Santos, M., Levin, L., Rojo-Domínguez, A., Romero-Martínez, D., Saparrat, M. C. N., Trujillo-Roldán, M. A., & Valdez-Cruz, N. A. (2019). Bioremediation. In Microbial Cell Factories . BioMed Central Ltd. [Google Scholar] [Crossref]

2. Wang, H., Li, X., & Zhang, Y. (2024). A comprehensive review on enzymatic biodegradation of polyethylene terephthalate (PET). Environmental Research, 240, 117462 [Google Scholar] [Crossref]

3. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J. (1990) . Basic local alignment search tool. J. Mol. Biol. 215:403-410. [Google Scholar] [Crossref]

4. Sievers, F., Wilm, A., Dineen, D., Gibson, T. J., Karplus, K., Li, W., Lopez, R., McWilliam, H., Remmert, M., Söding, J., Thompson, J. D., & Higgins, D. G. (2011). Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Molecular Systems Biology, 7(1), 539 - 548. [Google Scholar] [Crossref]

5. Šali, A., & Blundell, T. L. (1993). Comparative protein modelling by satisfaction of spatial restraints. Journal of Molecular Biology, 234(3), 779-815. [Google Scholar] [Crossref]

6. Laskowski, R. A., MacArthur, M. W., Moss, D. S., & Thornton, J. M. (1993). PROCHECK: a program to check the stereochemical quality of protein structures. Journal of Applied Crystallography, 26(2), 283–291. [Google Scholar] [Crossref]

7. Polák, L., Škoda, P., Riedlová, K., Krivák, R., Novotný, M., & Hoksza, D. (2025). PrankWeb 4: a modular web server for protein–ligand binding site prediction and downstream analysis. Nucleic Acids Research, 53(W1), W466–W471. [Google Scholar] [Crossref]

8. Gala, M., & Žoldák, G. (2021). Classifying Residues in Mechanically Stable and Unstable Substructures Based on a Protein Sequence: The Case Study of the DnaK Hsp70 Chaperone. Nanomaterials, 11(9), 2198 [Google Scholar] [Crossref]

9. Elsliger, M. A., & Wilson, I. A. (2012). Structure validation and analysis. In Comprehensive Biophysics (Vol. 1, pp. 116–135). Elsevier Inc. [Google Scholar] [Crossref]

10. Santana, C. A., de Silveira, S. A., Moraes, J. P. A., Izidoro, S. C., de Melo-Minardi, R. C., Ribeiro, A. J. M., Tyzack, J. D., Borkakoti, N., & Thornton, J. M. (2020). Bioinformatics, 36, I726–I734. [Google Scholar] [Crossref]

11. Hwang, H., Dey, F., Petrey, D., & Honig, B. (2017). Structure-based prediction of ligand-protein interactions on a genome-wide scale. Proceedings of the National Academy of Sciences of the United States of America, 114(52), 13685–13690. https://doi.org/10.1073/pnas.170538111 [Google Scholar] [Crossref]