A hybrid strategy for massive acceleration of directed evolution: meeting the need for high-turnover enzymes in industrial biotechnology.

Lead Research Organisation: University of Manchester
Department Name: Chemistry


Enzymes are tiny machines that speed up chemical processes in living organisms; life would not exist without enzymes, because essential chemical reactions would not happen fast enough. Enzymes perform this miraculous function by first attaching to chemicals, changing their shapes temporarily and using this to elicit a chemical reaction, and then releasing the products. Some enzymes break down large chemicals into simpler parts, while some build smaller chemicals into more complex ones. All this happens at atmospheric pressure, room temperature, neutral acidity and all components are biodegradable! In contrast, current industrial chemistry needs high temperatures and pressures, and creates organic waste and pollutants. Consequently, industrial reactions are typically not very efficient and often lead to the formation of unwanted side products. With enzymes there is relatively little energy demand and side products can be eradicated. Enzymes, therefore, represent a huge opportunity in the 21st Century to revolutionise industrial chemical reactions and make them more cost-effective and environmentally friendly.

Unfortunately, there is a catch: naturally occurring enzymes, perhaps unsurprisingly, are not suited to industrial processes. For example, an enzyme may not be naturally available for the chemical reaction at hand, they may be unstable in an industrial setting, or they may be too slow to be cost-effective (metabolic reactions don't demand such speed). Biology does, however, provide a potential solution to these shortcomings. Each enzyme is constructed as a string of 100s of smaller building blocks called amino acids. Furthermore, one enzyme can be converted to another by altering (or mutating) its amino acids. There are 20 possible amino acids and within a string of 100 there are vastly more combinations than stars in the universe and it is very clear that the natural world only uses the tiniest fraction of the possible gamut. The real opportunity is to mutate natural enzymes to make them more stable, faster and tailor their specificity so that they can be used in industry and make chemical processes much more cost-effective and environmentally friendly, which is at the heart of this research proposal.

The active site of an enzyme is a space that only the right chemicals can slot into easily and perfectly, like a key in its lock, and it is the shape of this space and motions within that determine an enzyme's speed and specificity; mutations alter speed and specificity by affecting the active site. Scientists thought, naively, that randomly mutating a few amino acids around the active site would be sufficient to achieve their goals. In such a case there would be, say, only a million combinations to produce and test to identify a suitable one. However, it has now been found that mutations anywhere in an enzyme's string of amino acids may affect the active site and based on random mutations the number of combinations are truly astronomical. Some progress has been made in a process called directed evolution, where random mutations are made in an iterative cycle, but not nearly enough to satisfy industry since for some enzymes the process is predicted to take millennia.

Our vision is to fundamentally change the way that directed evolution is performed, and reduce the timescales, not incrementally, but by potentially millions of times to facilitate rapid production of enzymes with industrially-relevant properties. We plan to use an enzyme, monoamine oxidase, which could be used in the manufacture of almost half of current developmental drugs, to show the validity of our new approach. The idea is to use very fast, but accurate, computer simulations of enzymes, leveraging hardware developed for rendering computer games, to understand how mutations throughout an enzyme affect the active site and use this to predict the optimal mutations for directed evolution, allowing the process to occur in weeks rather than millennia.

Technical Summary

Cost-effective and environmentally-sustainable catalytic methods for the separation into enantiomerically pure chiral molecules are urgently needed (almost half of drug candidates have a chiral amine). Directed evolution (DE) aims to meet this need by re-engineering enzyme specificities and turnover rate by repeated laboratory mutagenesis and selection. However, DE struggles to produce mutant enzymes with sufficiently high turnover rates for industrial use. The problem is that beneficial mutations occur not just at active sites but all over enzymes and the number of possible mutants to search is astronomical. Specifically, finding the most important 5 amino acids in a protein of 495 gives 2 trillion possibilities, but exhaustive search of those 5 is 3 million; knowing the best 5 gives a million-fold speed-up (a week versus 20,000 years). However, it is not understood how to identify mutations far from active sites that confer improved catalytic properties. Recent microsecond molecular dynamics (MD) simulations have shown that catalytically competent conformations are progressively stabilized, and appear with increasing frequency, due to the dynamic effects of remote mutations on the active site. Thus, we aim to integrate accurate hardware-accelerated microsecond MD simulations into the predict-design-test-build-learn cycle of DE to allow the crucial few beneficial mutations to be found at each iterative step. Our vision is to fundamentally change how DE is performed, and reduce the optimisation time, not incrementally, but by millions of times to facilitate rapid evolution of enzymes with high turnover rates. We will show proof-of-principle for monoamine oxidase, for which we already have extensive experience. The overall outcomes will be an enzyme that is massively faster than our present best, and our aim is to achieve improvements of 100 times, a mutant enzyme that will be of interest to industry, and a methodology that can be generalised to other enzymes.

Planned Impact

Major beneficiaries will be the fine chemicals and pharmaceutical manufacturing industries (e.g., BASF, Evonik, Lonza, DSM, Bayer, Novartis, Pfizer). They would derive significant benefit from a high turnover monoamine oxidase mutant enzyme, which could be used as a stalwart biocatalyst to replace inefficient processes, such as diasteromeric crystallization, chromatographic resolution and chiral pool separation. Even now, relatively few catalytic enantio-selective processes are operated on a commercial scale and new approaches are urgently required to meet current and anticipated demand: between 2001-11, the Federal Drug Administration (FDA) approved 9 single-enantiomer products and during that period US Medicaid programs spent over $6 billion on these 9 drugs. The FDA now reports that almost half of all developmental drugs contain a chiral amine. It is therefore evident that our research will have significant societal healthcare benefits in terms of bringing new products to the market earlier and with reduced manufacturing costs and environmental impact, hopefully with concomitant reduced costs for consumers and health services. Other beneficiaries include manufacturers employing industrial fermentation, for the production of enzymes, antibodies or vaccines, who would gain from the know-how and methods used for the rapid evolution of proteins using synthetic biotechnology and modern predictive computer simulations.

All pertinent data will be made available via the World Wide Web and we endeavour to make all our publications open access as soon as possible. However, we do anticipate that our newly designed biocatalytic mutants and technology will constitute valuable intellectual property (IP). Our strategy for translating the IP is to establish protection prior to disclosure through the University of Manchester's technology transfer office (UMI3) and work with them as partners during early exploitation. Depending on the value of opportunity, we will either seek to out-license the use of our mutant strains and/or use of our platform technology, or consider creating a University spin-out, which the applicants already have significant experience of. We will actively pursue UMI3 proof-of-principle funding, BBSRC follow on funding, knowledge transfer partnership (KTP) and venture capital funding, as required to support technology transfer.

We have established collaborations with biocatalysis companies with whom we will explore licensing agreements. Furthermore, many pharmaceutical companies (e.g., GSK, Merck, AstraZeneca, Pfizer) have their own relevant research groups, with whom we are in contact. The continued transfer of responsible synthetic biology technologies to industry will support cleaner production processes (e.g., green chemistry), more sustainable manufacturing and energy sources (e.g., biofuels), and contribute to job creation and wealth within the UK chemicals and pharmaceuticals industries and wider economy. The multidisciplinary interaction across scientific disciplines (biology, chemistry and computational science) will further strengthen the UK research base in synthetic biology.

We will use press releases and networking events to communicate with external stakeholders, the latter through structured workshops and showcase events within the Manchester Institute of Biotechnology (MIB) and associated centres, partly looking to leverage the MIB's reputation as a national hub for integration of synthetic biology approaches in fine and speciality chemicals production in terms of technology infrastructure and scientific expertise. We will also interface with relevant industrial partners both directly and via the meetings of the Bioprocess Research Industry Club (BRIC) and other IB NIBS. These activities will extend the outreach of our work and will demonstrate the utility of our new directed evolution design platform and the general impact it will have in the creation of novel industrially-useful biocatalysts.


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