The invention of plastics more than a hundred years ago was a game-changer, offering many convenience and benefits to our daily lives. Many new plastic products with an enormous range of desirable properties have been developed over the years, and we can find them in every vehicle, office, factory, and home environment. With the widespread use of plastic products, there has been an exponential increase in global plastic waste produced since the 1950s. Plastic packaging is presumably the major contributor to plastic waste and is responsible for approximately half of the global total (Ritchie & Rosser, 2018). Hence, the accumulation of plastic waste in the environment is a global problem of enormous proportions and is exacerbated by the fact that most plastic wastes are non-biodegradable. Herein, our iGEM team aims to design and construct an enhanced multi-plastic degrading bacterium that could potentially be used to develop an effective and sustainable technology to help reduce plastic waste and better protect our environment.
This project was carried out in 5 stages. The computer softwares used in this project are listed in Table 1.
Stage 1: Enzymes selection and protein structure modelling
In stage 1 of this project, our team selected 3 enzymes - polyethylene terephthalate hydrolase (PETase), papain (papaya proteinase 1), and polyurethanase esterase A (PueA) - for protein engineering investigations using a computational modeling approach. PETase is an extensively well studied PET-degrading enzyme and the molecular mechanism of action has been confirmed by crystallographic analysis (Chen et al., 2018). In contrast, although papain and PueA enzymes are less well studied, their enzymatic activity on polyurethane (PU) plastic has been previously demonstrated (Phua et al., 1987; Stern and Howard, 2000). The 3D structures of PETase and papain have been reported previously by other researchers (Kim et al., 1992; Joo et al., 2018) and are available from the Protein Data Bank. However, the 3D structure of the PueA enzyme has not been elucidated and was therefore derived by homology modeling in Stage 1 of this project using the MODELLER and YASARA (Yet Another Scientific Artificial Reality Application) software and refined using the PROCHECK program. The predicted 3D PueA structure was then further examined (along with the PETase and papain enzymes) by docking analysis in Stage 2.
Stage 2: In silico molecular docking analysis of
enzyme proteins to plastic substrates
The second stage was carried out in two parts. In Part 1, the performance reliability of five in silico docking programs (Autodock Vina, DockThor, EDock, SwissDock, and PatchDock) was compared using the published PETase crystal structure of Ideonella sakaiensis as the reference model (Joo et al., 2018). The five softwares have all been previously used for protein structure studies (Duhovny et al., 2002; Schneidman-Duhovny et al., 2005; Trott & Olson, 2009; Grosdidier et al., 2011; Santos et al., 2020). However, the performance of these softwares for in silico docking analysis of plastic-degrading enzymes with plastic ligands (i.e., enzyme-substrate complex) have not been tested. To identify the docking software most suitable for our project, docking simulations of the PETase enzyme-PET substrate (i.e. enzyme-substrate) complex were run using these five docking programs. The docking software yielding binding conformations and free energies (-Gb in -kcal/mol) most similar to those reported by Joo et al. (2018) was then chosen for docking analysis of the “papain enzyme-plastic substrate” and the “PueA enzyme-plastic substrate” complexes in Part 2 where the interaction and binding affinity of the enzyme-substrate complexes (papain and pueA) were evaluated based on the Gibb’s free energy (ΔG values) (Du et al., 2016).
Stage 3: In silico site-directed mutagenesis
of papain and PueA enzyme genes
In stage 3, we investigated the possibility of using computational modelling and in silico site-directed mutagenesis to change specific amino acid residues in the papain and polyurethane esterase A (PueA) proteins to create mutant papain and PueA enzymes with increased binding affinity to PU (polyurethane) plastic substrates.
Stage 4: In silico molecular docking analysis of
enzyme proteins to plastic substrates
The fourth stage of the project aims to assess the binding affinity of the mutant papain and mutant PueA enzymes to PU by performing “Redocking” analysis on the respective enzyme-substrate complexes to assess the engineering success of the in silico mutagenesis approach adopted in Stage 3.
Stage 5: Construction of a “Papain-PueA-PETase” biobrick
to create “Plastilicious Coli”
Based on the computational data obtained, we will design a bio-brick that contains genes encoding the mutant papain and mutant PueA enzymes, and wild-type PETase, which will be used to construct an Escherichia coli clone (which will be named as Plastilicious Coli) to develop an efficient multi-plastic bio-degradation system.
2.1 Sequence retrieval
The PETase (polyethylene terephthalate hydrolase; accession number PDB:6EQD_A), papain (papaya proteinase 1; accession number P00784), and PueA (polyurethanase esterase A; accession number AF069748) protein sequences were downloaded from the NCBI database.
2.2 Homology modelling and model evaluation
Homology modelling of the PueA protein 3D structure was performed using the MODELLER software (Webb & Sali, 2016). The structure models with the lowest DOPE (Discrete optimized protein energy) scores were selected due to their higher probability of resembling the correct 3D structure of the target protein (Eswar et al., 2006). Due to loop re-arrangements and repositioning of secondary structure elements which are formed as an intermediate before a protein folds into its final form, 3D structures were further analysed using the YASARA (Yet Another Scientific Artificial Reality Application) software which takes into consideration a consistent set of force field parameters ranging from van der Waals radius to ionic charges to enhance the reliability of the models produced (Krieger et al., 2009). The software performs energy minimization using the YASARA force field to curtail damages done to the protein crystal structure, giving a correct prediction to the protein structure. The structure models generated thus have a lower energy and are more likely to resemble the real 3D structure. The overall quality of the protein structure was further evaluated using the PROCHECK software to validate the reliability of the model (Laskowski et al., 1993).
AutoDock Vina was used to perform docking analysis of PETase with PET plastic, papain with PUR plastic, and PueA with PUR plastic. The binding affinity (ΔG value) of each enzyme to its plastic ligand was evaluated using plastic oligomers of different lengths (Du et al., 2016). The binding affinity (ΔG°) as a function of the binding constant (Kb) can be expressed using the formula:
ΔG° = −RTlnKb
ΔG° represents the binding affinity of a plastic-degrading enzyme with the respective plastic oligomers.
3 Results and Discussion
3.1 Homology modelling and model evaluation of PueA
A homology model of PueA using the MODELLER software is shown in Figure 1a. The structure was refined using YASARA for energy minimisation to obtain the optimal conformation of the protein. The refined PueA structure was further stabilized using YASARA as the total energy of the enzyme structure was improved from -274773.4 kJ/mol to -353947.5 kJ/mol (Figure 1b). Energy minimization helps to remove some atom crashes (Krieger et al., 2009). The optimised PueA structure was then validated using the PROCHECK software which showed that 98.4% of residues in PueA reside in the most favourable and additional allowed regions, whilst only 0.98% and 0.59% of the residues reside in the “generously allowed” and “disallowed” regions, respectively (Figure 2). Model validation using PROCHECK indicated that the model likely resembles the native structure of the PueA protein.
3.2 Molecular Docking and Model Evaluation
The performance of five in silico docking programs - Autodock Vina, DockThor, EDock, SwissDock, and PatchDock - was tested by docking simulations using the published PETase -PET substrate complex (Joo et al., 2018) as reference., and LIGPLOTs showing the docking simulations of PETase-PET interactions produced by the different docking softwares are depicted in Figure 3. The amino acid residues in the substrate-binding site of the PETase enzyme predicted by the 5 docking softwares were compared to the 19 residues reported by Joo et al. (2018) and the findings are summarized in Table 2. AutoDock Vina is shown to produce the best match, with 10 of the 19 amino acid residues in perfect agreement with the findings of Joo et al. (2008), while EDock and PatchDock each yielded a 7-residue match, and SwissDoc only yielded a 4-residue match.
Based on the above analysis, AutoDock Vina was therefore chosen and used to simulate the interactions of the “papain-PU” and “PueA-PU” enzyme-substrate complexes. The ΔG scores of papain and PueA enzymes with PU ligands of different lengths are shown in Table 3. Of the different size plastic substrates tested, PET and PU tetramers yielded the most optimal ΔG values of -7.1, -7.6 and -8.2 for the PETase, papain and PueA enzymes, respectively. Schematic representations (LIGPLOTs) of the output portraying intermolecular interactions such as (1) hydrogen-bonding interaction patterns, and (2) hydrophobic contacts between the ligand tetramer and key amino acid residues of the papain and PueA enzymes are depicted in Figures 4a and b, respectively. A complex with a ΔG value less than -7.0 is generally considered as a stable. Hence, PET and PU tetramers were used as enzyme substrates for in silico mutagenesis investigations in the next stage of the project.
3.3 In silico mutagenesis
Based on the assumption that conserved amino acid residues in the turns and coils of enzymes may be involved in ligand binding (Ma et al., 2003), attempts to identify conserved residues in the turns and coils within the papain and PueA enzymes were made by performing multiple sequence alignment analysis (MSA) of these two enzymes with related enzyme proteins derived from other sources (Appendix 1: MSA softwares and protein sources). It is hypothesized that changing a specific polar amino acid residue near the substrate-binding site to a non-polar residue would improve the binding affinity of an enzyme (Eldehna et al., 2016). The presumptive locations of the substrate binding site of the papain and PueA enzymes have been predicted in the previous section using AutoDock Vina. Single point mutations were therefore created by in silico mutagenesis (on conserved polar amino acid residues within the presumptive binding site) of the papain and PueA enzymes and the outputs were simulated using the PyMOL software (Pires et al., 2016).
Based on ΔG values, changing the glycine residue at position 23 of papain to tryptophan (to produce mutant papain-G23W), and changing the arginine residue at position 392 of PueA to phenylalanine (to produce mutant PueA-R392F) by in silico mutagenesis significantly enhanced the stability and hydrophobicity of the binding sites in the two enzymes. LIGPLOTs of the two mutant enzyme-substrate complexes are depicted in Figures 5a and b.
3.4 Re-docking analysis of the mutant papain-G23W and PueA-
R392F enzymes with PU (polyurethane) tetramer as substrate
Re-docking analysis using the AutoDock Vina software demonstrated that PU tetramers bind to similar regions on the wild-type and mutant papain (Figures 6a and b) and wild-type and mutant PueA (Figures 7a and b) enzymess. Moreover, no appreciable change in protein conformation was observed in the two mutant proteins with respect to the wild-type counterparts. Importantly, the binding affinity of the mutant papain-G23W for PU (∆G = -8.6 kcal/mol) is higher than the wild-type papain enzyme (∆G = -7.6 kcal/mol). Similarly, the binding affinity of the mutant PueA-R392F enzyme for PU (∆G = -8.8 kcal/mol) is also higher than the wild-type PueA protein ((∆G = -8.2 kcal/mol), which indicate that in silico mutagenesis of the targeted amino acid residues in papain and PueA has enhanced the binding affinity of the two enzymes.
Using a rational design approach, we have successfully designed two polyurethane-degrading enzymes – papain-G23W and pueA-R392F – that showed increased binding affinity for polyurethane plastic. These two mutant enzyme biobricks (BBa_K3511000 and BBa_K3511001) will be combined with the wild-type PETase biobrick (BBa_K2013002) to construct a “3-enzyme” biobrick that will be introduced into E. coli to create a superbug (Plastilicious Coli) to develop an efficient and sustainable multi-plastic biodegradation system.
1. Chen, C.C., Han, X., Ko, T.P. & Liu, W.D. (2018). Structural studies reveal the molecular mechanism of PETase. The FEBS Journal. 285 (2), 3717-3723.
2. Du, X., Li, Y., Xia, Y., Ai, S., Liang, J., Sang, P., . . . Liu, S. (2016). Insights into protein–ligand interactions: mechanisms, models, and methods. International Journal of Molecular Sciences 17(2), 144.
3. Duhovny, D., Nussinov, R., & Wolfson, H. J. (2002). Efficient unbound docking of rigid molecules. Lecture Notes in Computer Science Algorithms in Bioinformatics, p185-200.
4. Eldehna, W. M., Abou-Seri, S. M., Kerdawy, A. M., Ayyad, R. R., Hamdy, A. M., Ghabbour, H. A., . . . Ella, D. A. (2016). Increasing the binding affinity of VEGFR-2 inhibitors by extending their hydrophobic interaction with the active site: Design, synthesis and biological evaluation of 1-substituted-4-(4-methoxybenzyl)phthalazine derivatives. European Journal of Medicinal Chemistry 113, 50-62.
5. Eswar, N., Webb, B., Marti‐Renom, M.A., Madhusudhan, M., Eramian, D., Shen, M.‐y., Pieper, U. & Sali, A. (2006), Comparative protein structure modeling using Modeller. Current Protocols in Bioinformatics, 15: 5.6.1-5.6.30.
6. Grosdidier, A., Zoete, V., & Michielin, O. (2011). SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Research 39 (Suppl 2, pW270-W277).
7. Howard, G. T. (2002). Biodegradation of polyurethane: A review. International Biodeterioration & Biodegradation, 49(4), 245-252.
8. Joo, S., Cho, I. J., Seo, H., Son, H. F., Sagong, H., Shin, T. J., . . . Kim, K. (2018). Structural insight into molecular mechanism of poly(ethylene terephthalate) degradation. Nature Communications, 9(1).
9. Kim, M. J., Yamamoto, D., Matsumoto, K., Inoue, M., Ishida, T., Mizuno, H., . . . Kitamura, K. (1992). Crystal structure of papain-E64-c complex. Binding diversity of E64-c to papain S2 and S3 subsites. Biochemical Journal, 287(3), 797-803.
10. Krieger, E., Joo, K., Lee, J., Lee, J., Raman, S., Thompson, J., Tyka, M., Baker, D. & Karplus, K. (2009). Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8. Proteins: Structure, Function, and Bioinformatics, 77(S9), 114–122.
11. Laskowski, R.A., MacArthur, M.W., Moss, D.S. & Thornton, J.M. (1993), PROCHECK: a program to check the stereochemical quality of protein structures. J. Appl. Cryst., 26: 283-291.
12. Ma, B., Elkayam, T., Wolfson, H., & Nussinov, R. (2003). Protein-protein interactions: Structurally conserved residues distinguish between binding sites and exposed protein surfaces. Proceedings of the National Academy of Sciences, 100(10), 5772–5777.
13. Pires DE, Chen J, Blundell TL, Ascher DB. (2016). In silico functional dissection of saturation mutagenesis: Interpreting the relationship between phenotypes and changes in protein stability, interactions and activity. Sci Rep. 6, 19848.
14. Rentzsch, R., & Renard, B. Y. (2015). Docking small peptides remains a great challenge: An assessment using AutoDock Vina. Briefings in Bioinformatics 16(6), 1045-1056.
15. Ritchie, H. & Rosser, H. (2018). Plastic pollution. In “Our World in Data” (https://ourworldindata.org/plastic-pollution).
16. Šali, A., & Blundell, T. L. (1993). Comparative protein modelling by satisfaction of spatial restraints. Journal of Molecular Biology, 234(3), 779–815.
17. Santos, K. B., Guedes, I. A., Karl, A. L., & Dardenne, L. E. (2020). Highly flexible ligand docking: benchmarking of the DockThor program on the LEADS-PEP protein–peptide data set. Journal of Chemical Information and Modeling 60(2), 667-683.
18. Schneidman-Duhovny, D., Inbar, Y., Nussinov, R., & Wolfson, H. J. (2005). PatchDock and SymmDock: Servers for rigid and symmetric docking. Nucleic Acids Research 33, (Suppl_2).
19. Shimao, M. (2001). Biodegradation of plastics. Current Opinion in Biotechnology, 12(3), 242-247.
20. Stern, R. V., & Howard, G. T. (2000). The polyester polyurethanase gene (PueA) from Pseudomonas chlororaphis encodes a lipase. FEMS microbiology letters, 185(2), 163–168.
21. Trott, O., & 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, 455-461.
22. Wallace A C, Laskowski R A & Thornton J M (1996). LIGPLOT: A program to generate schematic diagrams of protein-ligand interactions. Prot. Eng., 8, 127-134.
23. Webb, B., & Sali, A. (2016). Comparative protein structure modeling using MODELLER. Current Protocols in Bioinformatics, 5.6.1–5.6.37.
24. Zhang, W., Bell, E. W., Yin, M., & Zhang, Y. (2020). EDock: Blind protein–ligand docking by replica-exchange monte carlo simulation. Journal of Cheminformatics 12(1), 37.
Program used for multiple sequence alignment (MSA):
1. Clustal Omega from European Molecular Biology Laboratory- European Bioinformatics Institute (EMBL-EBI). URL: https://www.ebi.ac.uk/Tools/msa/clustalo/
2. Protein BLAST from National Center for Biotechnology Information (NCBI). URL: https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins
Papain sequences from different sources used for MSA:
Polyurethanase esterase A (PueA) from different sources used for MSA:
4. https://www.ncbi.nlm.nih.gov/protein/WP_060843495.1? report=genbank&log$=prottop&blast_rank=30&RID=T9WRVD97016