Team:Hong Kong CityU/Engineering

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1. Introduction

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. Methods

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.