GREET: Generative Recombinant Enzyme Engineering for Therapeutics

Enzymes are proteins catalysing almost all reactions required for cellular life and, when defective, they can cause severe pathologies. For example, in humans, alpha-galactosidase (a-GAL) deficiency, a condition affecting up to 1 in 3000 newborn known as Fabry's disease (FD), causes life threatening damage to heart and kidneys. Since these diseases are usually caused by inherited genomic mutations, they cannot be cured, but they can be treated using Enzyme Replacement Therapies (ERTs), which consist of the injection of a recombinant version of the affected enzymes into patients.
Unfortunately, ERTs have limitations; recombinant enzymes have lower enzymatic activity compared to the human wild-type versions, are unstable in blood, are poorly absorbed by human cells, and often trigger an immune response. Moreover, manufacturing therapeutic enzymes is extremely expensive because standard mammalian cell-based expression systems have low yield.

Developing effective therapeutic enzymes requires design methods able to discover new amino acid sequences that can encode the same catalytic function, while optimising the therapeutic properties of the molecule. Then, these enzymes must be converted into highly optimised DNA triplets, called codons, to maximise expression and yield in host organisms that can grow in inexpensive media. With the increasing incidence of enzymatic deficiencies and current treatments costing up to £400K per year per patient, it is crucial to establish effective methods to perform these tasks and implement a platform for effective and sustainable production of therapeutic enzymes.

Through the EPSRC fellowship, I will develop the computational and experimental methods required for engineering and manufacturing designer enzymes. I will use deep generative machine learning (ML) to design and codon optimise new enzymes, which will then be rapidly built and tested at scale using the lab automation platform available at the University of Edinburgh (UoE). As a proof of concept, I will build a library of designer human a-GAL enzymes using P. pastoris, a high-yield expression system used in the pharmaceutical industry.

To deliver this ambitious project, I have set four objectives over the 4 years of my fellowship :
1. Developing deep generative learning models for enzyme design.
2. Developing deep generative learning models for codon optimisation.
3. Building a library of designer human a-GAL enzymes in P. pastoris.
4. Developing a computer aided design (CAD) software for enzyme engineering.

Each objective addresses current limitations in enzyme engineering and manufacturing. ML avoids the need for accurate biophysical models by learning design rules directly from existing enzymes. Thus, by reverse engineering Nature's design principles, it will be possible to engineer functional designer enzymes at unprecedented scale. Coupling in-silico design with a robotic platform will allow building and testing thousands of different variants, thus minimising the time required for identifying a functional enzyme. Here I will test this new approach by engineering the human a-GAL enzyme, which is currently difficult to manufacture and optimise for therapeutic treatment; this effort will not only provide experimental evidence for the effectiveness of my platform but could also identify new potential treatments for FD.

The project is supported by a strong network of experts in synthetic biology and machine learning, in the UK and the US, industrial biopharmaceutical and biotechnology partners, such as Fujifilm Diosynth Biotechnologies UK (FDBK) and the Industrial Biotechnology Innovation Centre (IBioIC), and unique research facilities available at UoE, such as the Edinburgh Genome Foundry.

With this fellowship, I will lay the foundation for data-driven biological engineering and deliver enabling computational and experimental technologies to rapidly design, build and test new therapeutic molecules.