GREET: Generative Recombinant Enzyme Engineering for Therapeutics

酶是一种蛋白质，催化细胞生命所需的几乎所有反应，当有缺陷时，它们可能会导致严重的病理。例如，在人类中，α-半乳糖苷酶(α-GAL)缺乏症是一种被称为法布里病(FD)的疾病，每3000名新生儿中就有一人患有这种疾病，会对心脏和肾脏造成危及生命的损害。由于这些疾病通常是由遗传的基因组突变引起的，它们无法治愈，但可以使用酶替代疗法(ERT)治疗，这种疗法包括将受影响的酶的重组版本注射到患者体内。不幸的是，ERT有局限性；与人类野生型版本相比，重组酶的酶活性较低，在血液中不稳定，不易被人类细胞吸收，经常引发免疫反应。此外，由于标准的哺乳动物细胞表达系统产量较低，制造治疗性酶的成本极其高昂。开发有效的治疗性酶需要设计方法，能够发现能够编码相同催化功能的新氨基酸序列，同时优化分子的治疗特性。然后，这些酶必须被转化为高度优化的DNA三联体，称为密码子，以最大限度地提高寄主生物体的表达和产量，这些寄主生物体可以在廉价的介质中生长。随着酶缺乏的发生率不断增加，以及目前每个患者每年花费高达400K GB的治疗方法，建立有效的方法来执行这些任务并实现一个有效和可持续生产治疗用酶的平台是至关重要的。通过EPSRC奖学金，我将开发工程和制造设计酶所需的计算和实验方法。我将使用深度生成性机器学习(ML)来设计和密码子优化新的酶，然后使用爱丁堡大学(UoE)提供的实验室自动化平台快速构建和大规模测试。作为概念的证明，我将使用P.Pastoris建立一个设计人类α-半乳糖酶的库，这是一种用于制药业的高产表达系统。为了实现这个雄心勃勃的项目，我在4年的研究员生涯中设定了四个目标：1.为酶设计开发深度生成性学习模型。开发用于密码子优化的深度生成性学习模型。在巴斯德毕赤酵母中构建设计的人α-半乳糖酶文库。开发一种用于酶工程的计算机辅助设计(CAD)软件。每个目标都针对目前酶工程和制造中的局限性。ML通过直接从现有的酶中学习设计规则，避免了对准确的生物物理模型的需要。因此，通过逆向工程自然的设计原则，将有可能以前所未有的规模设计功能设计者酶。将硅内设计与机器人平台相结合，将允许构建和测试数千种不同的变体，从而最大限度地减少鉴定功能酶所需的时间。在这里，我将通过设计人类α-半乳糖酶来测试这一新方法，目前这种酶很难制造和优化用于治疗；这一努力不仅将为我的平台的有效性提供实验证据，还可以发现治疗功能性消化不良的新的潜在疗法。该项目得到了英国和美国合成生物学和机器学习方面的强大专家网络、工业生物制药和生物技术合作伙伴(如英国富士胶片DiSynth BioTechnologies UK(FDBK)和工业生物技术创新中心(IBioIC))以及爱丁堡大学独特的研究设施(如爱丁堡基因组基金会)的支持。有了这笔奖学金，我将为数据驱动的生物工程奠定基础，并提供能够快速设计、构建和测试新的治疗分子的计算和实验技术。

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.