Creating enzymes beyond nature – tools in enzyme engineering

1.4 Creating enzymes beyond nature – tools in enzyme

feature are selected. Then, this hit is used as template for consecutive diversifications at residual hot-spot regions or positions. This process is continued in an iterative fashion until the desired feature is engineered. It has been shown that choosing a limited amount of hot-spot sites but exchanging those against all 20 canonical amino acids is a strategy as viable as doing the opposite, selecting a large amount of hot-spots, which are, however, only exchanged against a limited set of amino acids.¹⁰² Both strategies can provide sufficient diversity.

Due to the limited library-sizes, the method is especially suited for enantioselectivity engineering because of the limited ability to quickly distinguish chiral information in a high-throughput assay.¹⁰³ Examples of effective screening assays like UV/VIS spectroscopy-based detection of more selective lipases and others have been reported.¹⁰⁴ However, these assays require certain molecular motifs and are thus not generally applicable. This explains why most studies rely on chiral GC or HPLC analysis.¹⁰⁵ In consequence, the screenable library sizes are then limited.

A recent ERED-focused review highlights that many features like activity, substrate scope, stability and also selectivity could be optimized successfully by different enzyme engineering approaches.⁷⁹ For the present work, two specific ISM examples of engineered ERED selectivities are especially relevant to mention. Reetz et al.

demonstrated the usefulness of the ISM strategy by engineering both, the R- and S-selective reduction of 3-methylcyclohexenone to 3-methylcyclohexanone in the thermophilic ERED YqjM.¹⁰⁶ Furthermore, Stewart et al. were also successful in the engineering of stereoselective OYE1 variants for the synthesis of the building block methyl 3-hydroxy-2-methylpropionate (Roche ester) from the respective Baylis-Hillman adduct.¹⁰⁷ Despite these efforts, so far no report is known that tried to engineer an enantioconvergent R-selective citral reduction. This might be explained by the fact that in contrast to other selectivity engineering studies, the selectivity towards both citral isomers needs to be engineered concomitantly, which is complicated by a possible opposite substrate control as described before (Figure 8).

1.4.2 In silico methods for understanding and guiding enzyme engineering

Many computational tools have been developed that can be used to guide enzyme engineering.¹⁰⁸ They enable visualizations of enzymatically relevant situations and processes that are experimentally difficult to achieve. In a so called de novo design approach, it is even possible to design new enzyme functions from scratch.¹⁰⁹ The concepts of three in this work applied in silico methods are introduced (Figure 14).

Figure 14: Conceptual representation of selected in silico methods from the view point of enzyme catalysis. The figure highlights basic differences in the application of these methods as described in this work. QM is referring to quantum mechanics.

Molecular docking simulation

The objective of ligand-receptor docking is to computationally predict the binding of rigid or flexible small molecules to a rigid protein.¹¹⁰ The computation aims to find optimal non-covalent interactions between a receptor (protein) and a ligand (inhibitor or substrate), which it does by the calculation of binding energies. The fact that protein dynamics are usually not accounted for in this method makes this technique fast and relatively easy to apply, but one should also be aware that the lacking protein dynamics might conceal interactions that are in fact essential.¹¹¹ The probably most valuable feature of this method is that it allows a graphical representation of ligand-receptor binding that can be useful together with experimental verifications. Concisely spoken, the method relies on force field calculations, which simplify atoms and bonds as point charges and springs that span a force field in which a ligand is set randomly and thus exposed to these forces.¹¹²

Molecular dynamics simulation

In principle, molecular docking and molecular dynamics (MD) simulations are related methods because usually both apply force field calculations.¹¹³ The main advantage over docking simulations is the consideration of protein dynamics, which increases the meaningfulness of these calculations but also complicates the computation significantly.^108,114 Because of the sheer size of proteins, a lot more interactions need to be calculated. Further on, in contrast to docking, laws of kinetic motion are added to the calculations that are needed to explicitly describe motion. The thus present kinetic energy allows the switch between different conformational states. Usually, such simulations include a solvent, so for proteins an aqueous buffer solution. The simulation time is an essential parameter in MD simulations and depends on the nature of the questions that such a calculation tries to answer.¹¹⁴ Short simulations in the picosecond to nanosecond scale allow for bond vibrations and some side chain rotations. After a docking simulation this can be regarded as MD refinement. However, microsecond- to second-simulations are needed to simulate various degrees of secondary structure and domain movements.

Semi-empirical quantum mechanics modelling

The described force field calculations are usually not suited for the simulation of chemical reactions, which in enzyme catalysis might be valuable for understanding mechanistic details or potential mechanistic promiscuity.¹¹⁵ Chemistry is basically the breaking and formation of bonds, which is guided by the interaction of electrons. For an accurate representation of these interactions the quantum mechanical nature of these small particles like the wave-particle duality cannot be neglected.¹¹² Quantum mechanical simulations that reproduce chemical reactions and their transition states solve the Schrödinger equation, which describes these interactions. Due to a trade-off of calculation accuracy and effort, so called semi-empirical methods like PM7 proved their applicability to simulate an enzyme’s underlying chemistry.¹¹⁵ They are suitable for a system accounting several hundreds of atoms, which is the typical size of an enzyme’s active site. Hence, an active site model is generated, which is for example retrieved from an available crystal structure or previous force field simulations. A common simplification is to fix the backbone of selected model amino acids in space to account for the limited degrees of backbone atoms.