A framework for machine learning assisted directed evolution of plastic-degrading enzymes

全球每年生产的塑料超过3.81亿吨。英国回收行业的价值超过270亿英镑，预计每年将以5.8%的速度增长。聚对苯二甲酸乙二醇酯塑料（PET）是一种高度可回收的塑料，在某些国家有很高的回收水平用于加工（英国53%的瓶子和38%的其他包装）。尽管如此，塑料的机械回收是一个昂贵的过程，由于污染物的积累，不能无限期地使用。标准普尔全球普氏能源资讯估计，每吨再生塑料的制造成本比原始塑料高出57英镑。生物制造提出了一个新的和可持续的替代成本效益，低能源和碳中性塑料回收的问题。塑料降解酶作为一种可持续塑料回收过程的解决方案，越来越受到人们的关注。然而，由于热稳定性和催化活性的限制，天然存在的酶不适合工业塑料降解应用。Entropix旨在克服这些限制。该项目将展示现代生物制造技术如何与工业人工智能相结合，为塑料的原始原料解聚提供新的回收技术。基于Entropix现有的技术栈，并与利物浦大学的虚拟工程中心合作，该项目将提供一种新的为制造而制造的机器学习方法，以生产定向进化工业酶，旨在为塑料解聚提供更好的催化效率和稳定性，用于利物浦和其他地方的工业和回收中心。这一创新发展将应用来自不同技术领域的知识，并将人工智能应用于定向进化过程。过去的研究表明，机器学习在识别DNA序列模式方面特别有效。机器学习工具似乎很适合解决这类实际问题。定向进化通过突变和选择的迭代来识别具有所需特性和功能的酶变体。它已经被成功地部署，但受到特定蛋白质的大量潜在突变的限制。该项目将部署一个新的机器学习（ML）定向进化框架，该框架使用ML方法在每个进化循环中仔细指导候选突变体的选择。工程塑料降解酶的目标是提高成本效益和可行的生产。它将与塑料处理和回收工业市场整合，并支持利物浦地区及其他地区可持续塑料价值链的发展。

Globally over 381 million tonnes of plastic are produced every year. The UK recycling industry is valued at over £27bn and is expected to grow at 5.8% per annum. Polyethylene terephthalate plastics (PET) are a highly recyclable form of plastics with high levels of collection for processing in certain countries (53% of all bottles and 38% of other packaging in the UK). Despite this, mechanical recycling of plastics is an expensive process and cannot be used indefinitely owing to accumulation of contaminants. S&P Global Platts estimates that recycled plastics cost an extra £57 per tonne than virgin plastic cost to manufacture.Biomanufacturing presents a new and sustainable alternative to the problem of cost-effective, low-energy and carbon-neutral plastic recycling. Plastic-degrading enzymes have gained increasing attention as a solution to a sustainable plastic recycling process. However, naturally occurring enzymes are not well-suited for industrial plastic degradation applications due to the limitations on thermostability and catalytic activity.Entropix aims to overcome these limitations. The project will demonstrate how modern biomanufacturing technologies paired with industrial AI can deliver new recycling technologies for the depolymerization of plastic to its original raw materials.Building on Entropix's existing technology stack and working with the University of Liverpool's Virtual Engineering Centre the project will deliver a new made-for-manufacturing machine learning approach to produce directed evolution industrial enzymes engineered to produce better catalytic efficiency and stability for plastic depolymerization, for use in industry and recycling centres in Liverpool and elsewhere.This innovative development will apply knowledge from different technology sectors, with the application of AI to the process of directed evolution. Past work has shown that machine learning is especially effective at identifying patterns in DNA sequences. Machine learning tools seem well-placed to contribute to this type of practical problem.Directed evolution identifies the enzyme variants with desired properties and functionalities using iterations of mutation and selection. It has been successfully deployed but is limited by the enormous number of potential mutations for a given protein.This project will deploy a novel machine learning (ML) directed evolution framework that uses ML approach to carefully guide the selection of candidate mutants at each evolution loop.The engineered plastic-degrading enzymes are aimed at better cost efficiency and feasible manufacturing. It will integrate with the plastic treatment and recycling industrial market and support the development of a sustainable plastic value chain in the Liverpool region and beyond.