Enzyme evolution can be used to take a wild-type enzyme that is in some way unsuitable for a bioprocess of interest and create a strain with enhanced capabilities. This iterative process is based on the design-build-test-learn (DBTL) cycle,1 and can be used to achieve a wide variety of aims, from improving the temperature or pH tolerance of the native enzyme, to eliminating product inhibition, to increasing its catalytic capacity and/or selectivity for a specific substrate.
Overcoming a shortcoming in biocatalysis requires improvement of either the turnover efficiency, kcat, or the affinity of the enzyme to the substrate, KM. A high kcat value enables faster turnover of substrate to product, while keeping the KM value as low as possible ensures maximal activity; conversely, a higher KM requires a greater substrate concentration to reach a given reaction rate. kcat/KM, otherwise known as catalytic efficiency, is therefore an important kinetic parameter, as it reflects the overall effectiveness of the enzyme’s ability to convert substrate into product. Enzyme engineering can be used to optimise the individual kcat and KM values, either individually or in combination, to deliver the attributes necessary for a bioprocess to succeed commercially.
A comprehensive approach to enzyme evolution
Design
An essential starting point for any enzyme evolution process is to ensure that the needs and objectives of the overall bioprocess are fully understood from the outset. Clear communication between parties – whether with in-house teams or an external contract research organisation – is vital to establish the nature of the problem and what success looks like, ideally with a written summary of the problem that specifies the objectives and outcomes required from the improved enzyme. As with any R&D project, it is tempting to keep refining the process – which, if left unchecked, can incur unwanted additional costs – and so it is crucial to determine the success criteria for achieving the project’s goals. Once the goals have been defined, the next step is to draw up a detailed design brief specifying the areas of improvement required – for example, a certain percentage increase in thermal tolerance under normal operating conditions – and how they can be achieved. This is the first, and arguably easiest, step of the DBTL cycle.
Build
The next step is to build the required protein variants, which can be achieved by a variety of approaches, depending on the aim and the degree of structural and functional information available about the target enzyme. For example, if increasing enzyme stability is the intent, using error-prone PCR (epPCR) or chemical mutagens such as nitrous acid will very effectively introduce random DNA mutations to generate a library of variants for testing.2,3 This scattergun approach can – with a little luck – allow rapid progress in the absence of extensive knowledge of the tertiary (and quaternary) structure of the enzyme. Alternatively, a more methodical, focused approach can be used, especially if individual key amino acids are known to influence the enzyme attribute of interest. This tactic is well suited to altering the specificity of an enzyme to metabolise a new target substrate, for example. By identifying the key residues around the active site that determine enzyme specificity, site saturation mutagenesis can be used to systematically introduce all possible amino acid substitutions at each of those positions to assess the impact on enzyme specificity. However, with 20 amino acids available, the potential number of combinations rapidly multiplies with each new position tested. This not only creates a logistical problem for the ‘build’ phase of the cycle – mitigated to some extent by increasingly high throughput commercial DNA synthesis – but also significantly increases the scope of the subsequent ‘test’ step.
Test
Automation has rendered the design and build aspects simpler than in the past, this has inevitably made testing even more challenging – and arguably the most important component for success – as large numbers of variants can be generated more quickly and (relatively) easily than ever before. The type of screen required will vary on a case-by-case basis, and may require the development of new assays to detect the product of interest while still achieving an appropriate throughput.
Auxotrophic screening offers a high throughput, easy to interpret approach if the enzyme product is a nutrient essential for host survival. Ingenza has a library of gene knockout hosts, each deficient in the production of a critical nutritional requirement. For example, if the target enzyme is part of the isoleucine biosynthesis pathway, we can deploy a host microbe deficient in the native activity of that enzyme. The variant library of the enzyme target is then introduced to the deficient host, and those variants able to restore isoleucine production under selective conditions – such as elevated temperature or pH – are identified. This simple ‘life-death’ screening approach is very high throughput, allowing rapid assessment of many variants. Tens, or even hundreds, of thousands of individual variants can be quickly screened on solid phase agar to identify those isolates capable of growth. These isolates can then be readily recovered, and their DNA sequenced to identify the key mutation(s) responsible for the successful enzyme adaptation. This ‘recruitment’ of nature’s immense selective power can be augmented by including chemical reagents (e.g. analogues of essential nutrients) whose toxicity to the host organism can only be overcome by additional adaptation of the target enzyme, permitting further gains in performance.2
Another approach is solid phase, high throughput colorimetric screening, where either the reaction product itself is coloured, or the initial product undergoes a second chemical transformation to yield a coloured compound. For example, the enzyme amine oxidase – useful in the preparation of chiral amines in high enantiomeric purity – generates the oxidant hydrogen peroxide. This can be detected visually by the addition of diaminobenzidine and horse radish peroxidase. In situ oxidation of diaminobenzidine forms an insoluble red/purple precipitant that permits semi-quantitative visual detection of individual colonies harbouring superior enzyme variants when screened directly for activity on an amine of interest.4
Flow cytometry methods can also be beneficial for enzyme expression projects, by coupling enzymes that are difficult to express to a fluorogenic fusion peptide for detection. This enables very high throughput libraries – 108 to 109 – to be screened quickly. Lower throughput methods – such as HPLC and LC-MS – can also be used when no auxotrophic, colorimetric, fluorometric or similar approach is viable. These methods are more universally applicable, but they are restricted by the instrument’s throughput rate, typically limiting screening to hundreds – rather than tens or hundreds of thousands – of variants.
Learn
Versatility and rigour in both designing the screening process and understanding the outcomes are crucial to further refine the enzyme or bioprocess. These are key strengths of Ingenza and are underpinned by a history of effectively integrating chemistry and biology disciplines. Colorimetric and fluorometric approaches are qualitative – or at best semi-quantitative – in the first instance, providing limited information on the engineered enzyme’s performance compared to the wild-type. In comparison, HPLC and LC-MS allow more in-depth interrogation of the engineered enzyme, but at a lower throughput. A combination of these approaches is often the most effective strategy, rapidly triaging the candidate enzymes with a high throughput test method, then evaluating selected hits with a lower throughput approach that offers greater kinetic and overall performance characterisation. This allows us to drill down to the enzymes that will deliver the greatest benefit for our clients.
Going beyond the basics
Creating a novel enzyme at the bench scale is obviously the primary focus of any enzyme evolution approach, but it is equally important to keep the final target bioprocess and end use in mind. A majority of enzymes engineered are intended for industrial bioprocessing, so it is critical to confirm that the final application delivers beyond the scope of lab-scale bioreactors. Our in-house fermentation scale-up capability allows us to assess the enhanced enzyme within the target bioprocess, generating data on productivity, yield and titre that is directly relevant to the intended industrial application. In-house validation of the engineered enzyme’s performance throughout the entire bioprocess at the pilot scale provides high predictability of its effectiveness in delivering the expected benefits. This strength in depth allows us to provide an end-to-end service, from feasibility, proof of concept and bioprocess optimisation to final technology transfer and manufacturing, reducing risks, costs and development timelines for our customers.
Ingenza has the expertise to provide optimised enzyme evolution, engineering novel strains with enhanced capabilities for any bioprocess of interest. Contact us to learn more about how our approach to enzyme evolution could benefit your bioprocess.
References
- Opgenorth P, Costello Z, Okada T, et al. Lessons from Two Design-Build-Test-Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning. ACS Synth Biol. 2019;8(6):1337-1351. doi:10.1021/acssynbio.9b00020
- McCullum EO, Williams BA, Zhang J, Chaput JC. Random mutagenesis by error-prone PCR. Methods Mol Biol. 2010;634:103-109. doi:10.1007/978-1-60761-652-8_7
- Nelms J, Edwards RM, Warwick J, Fotheringham I. Novel mutations in the pheA gene of Escherichia coli K-12 which result in highly feedback inhibition-resistant variants of chorismate mutase/prephenate dehydratase. Appl Environ Microbiol. 1992;58(8):2592-2598. doi:10.1128/aem.58.8.2592-2598
- Carr R, Alexeeva M, Enright A, Eve TSC, Dawson MJ, Turner NJ. (2003), Directed Evolution of an Amine Oxidase Possessing both Broad Substrate Specificity and High Enantioselectivity. Angew Chem Int Ed, 2003;42:4807-4810. doi.org/10.1002/anie.200352100