Novel Plastizymes: discovery and improvement of plastic-degrading enzymes by integrated cycles of computational and experimental approaches

新型塑料酶：通过计算和实验方法的综合循环发现和改进塑料降解酶

• 批准号: BB/X00306X/1
• 负责人: Florian Hollfelder
• 金额: $ 385.37万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX00306X%2F1
• 关键词: Novel; Plastizymes; discovery; improvement; plastic

Modern life generates enormous amounts of plastic waste: 359 million tons of plastics are produced annually worldwide, of which 90% is produced from fossil fuels and 79% accumulates in landfill or in the natural environment. Collectively all these plastics create an environmental hazard. Furthermore, we are losing valuable materials that could be recycled. As Nature did not encounter plastics for most of its evolutionary history, plastic-degrading enzymes with a metabolic role did not exist. However, recent research into communities of bacteria from oceans and wastewater has shown that over the last 50 years some bacteria have evolved enzymes that can exploit this new nutrient. These plastic degrading enzymes, some of which are known as PETases (as they degrade polyesters, PETs), are not very efficient but represent an exciting starting point to discover and engineer more effective enzymes. Furthermore, international 'metagenome' efforts have been capturing vast amounts of bacterial genomic data from these natural environments, which are now available as resources like MGnify at the European Bioinformatics Institute (EBI). In this project we will use bioinformatics to harvest enzymes from these massive metagenomic databases, by classifying them into functional and structural classes with useful 'promiscuous' chemical activities. We will use state-of-the-art artificial intelligence (AI) and machine learning (ML) tools to do this, proven to classify families of proteins with high functional similarity. Putative novel plastic-degrading enzymes identified by this approach will be further analysed by ML tools which screen for predicted solubility. We will also perform chemical studies to assess improvements in enzyme activity, compared to the existing, inefficient, PETases. Any putative plastic-degrading enzymes will then provide a starting point for directed evolution experiments where we select new variants of the enzymes with improved properties. To best explore evolution of plastic degrading ability we will use our unique ultrahigh-throughput assay for particle breakdown, with a throughput of over 10 million clones per day. We can thus directly assess the ability of enzymes to chemically act on plastic particles (rather than substrates that only mimic plastics). This will revolutionise the field of enzymatic plastic degradation, because so far only marginal improvements have been possible using proxy substrates. In addition to efficient screening, the analysis of the output sequences of screening will be fed back into our bioinformatic analyses and target selection. We will also structurally characterise these enzymes to discover how changes in their functional sites have improved their ability to bind and digest plastics. This data will provide detailed insights on how protein sites can diverge and evolve better plastic degrading properties, thus improving our in silico selection protocol. We have performed pilot work on PETases and will build on this and extend to other plastic degrading enzymes (plastizymes). This close integration of 'dry' data science and 'wet' experimental work results in powerful cycles of in silico analysis, experimental tests and refinement of analysis tools that are more powerful than current small scale protein engineering campaigns. The project thus addresses one of the most important (and also most difficult) environmental challenges, but more generally, also provides a paradigm to demonstrate how an interdisciplinary approach can accelerate evolution in cases where no effective natural enzyme is available. If successful, this paradigm would form the basis not just for the 'rules of life' (as mentioned in the call text), but for 'rules beyond life' (as it exists now), targeted to address the future needs of our society.

现代生命产生了大量的塑料废物：每年在全球每年生产3.59亿吨塑料，其中90％是由化石燃料生产的，有79％的塑料在垃圾填埋场或自然环境中累积。总体上所有这些塑料造成了环境危害。此外，我们正在丢失可以回收的有价值的材料。由于自然在其大多数进化史上都没有遇到塑料，因此不存在具有代谢作用的塑料降解酶。但是，最近对来自海洋和废水细菌群落的研究表明，在过去的50年中，一些细菌进化了可以利用这种新营养素的酶。这些塑料降解酶，其中一些被称为小叶酶（它们降解了宠物，宠物），不是很有效，而是令人兴奋的起点，可以发现和设计更有效的酶。此外，国际“元基因组”的努力一直在捕获这些自然环境中的大量细菌基因组数据，这些数据现在可以作为欧洲生物信息学研究所（EBI）的MGNIFY等资源提供。在这个项目中，我们将使用生物信息学从这些大规模的宏基因组数据库中收集酶，并通过将其分类为具有有用的“混杂”化学活动的功能和结构类别。我们将使用最先进的人工智能（AI）和机器学习（ML）工具来做到这一点，被证明可以对具有高功能相似性的蛋白质家族进行分类。通过筛选预测溶解度的ML工具将进一步分析假定的新型降解酶。我们还将进行化学研究，以评估与现有的，效率低下的小同生酶相比，酶活性的改进。然后，任何推定的塑料降解酶都将为有向进化实验提供一个起点，在该实验中，我们选择具有改进特性的酶的新变体。为了最好地探索塑料降解能力的演变，我们将使用我们独特的超高通量测定法进行颗粒分解，每天的吞吐量超过1000万克隆。因此，我们可以直接评估酶对化学作用于塑料颗粒的能力（而不是仅模拟塑料的底物）。这将彻底改变酶促塑性降解的领域，因为到目前为止，使用代理底物只能进行边缘改进。除了有效的筛选外，对筛选的输出序列的分析还将被反馈到我们的生物信息学分析和目标选择中。我们还将在结构上表征这些酶，以发现其功能部位的变化如何提高其结合和消化塑料的能力。这些数据将提供有关蛋白质位点如何分歧和发展更好的塑料降解特性的详细见解，从而改善了我们的计算机选择方案。我们已经在石4上进行了试点工作，并将基于此基础，并延伸到其他塑料降解酶（塑料）。 “干燥”数据科学和“湿”实验工作的这种紧密整合导致在计算机分析，实验测试和分析工具的完善中，这些周期比当前的小规模蛋白质工程运动更强大。因此，该项目解决了最重要的（也是最困难的）环境挑战之一，但更普遍地，也提供了一个范式来证明在没有有效的天然酶的情况下，跨学科方法如何加速进化。如果成功的话，这种范式将不仅构成“生命规则”的基础（如呼叫文本中所述），而是针对“超越生命的规则”（如今存在），旨在满足我们社会的未来需求。

项目成果

Chemoenzymatic Photoreforming: A Sustainable Approach for Solar Fuel Generation from Plastic Feedstocks.

• DOI: 10.1021/jacs.3c05486
• 发表时间: 2023-09-20
• 期刊: JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
• 影响因子: 15
• 作者: Bhattacharjee, Subhajit;Guo, Chengzhi;Lam, Erwin;Holstein, Josephin M.;Rangel Pereira, Mariana;Pichler, Christian M.;Pornrungroj, Chanon;Rahaman, Motiar;Uekert, Taylor;Hollfelder, Florian;Reisner, Erwin
• 通讯作者: Reisner, Erwin