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

现代生活产生了大量的塑料垃圾：全球每年生产3.59亿吨塑料，其中90%来自化石燃料，79%积累在垃圾填埋场或自然环境中。所有这些塑料共同造成环境危害。此外，我们正在失去可以回收的宝贵材料。由于大自然在其进化史的大部分时间里都没有遇到塑料，因此不存在具有代谢作用的塑料降解酶。然而，最近对海洋和废水中细菌群落的研究表明，在过去的50年里，一些细菌已经进化出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PET酶（因为它们降解聚酯，PET），不是很有效，但代表了一个令人兴奋的起点，以发现和工程更有效的酶。此外，国际上的“宏基因组”工作已经从这些自然环境中捕获了大量的细菌基因组数据，这些数据现在可以作为欧洲生物信息学研究所（EBI）的MGnify等资源获得。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获酶，通过将它们分类为具有有用的“混杂”化学活性的功能和结构类别。我们将使用最先进的人工智能（AI）和机器学习（ML）工具来实现这一目标，这些工具已被证明可以对具有高功能相似性的蛋白质家族进行分类。通过这种方法鉴定的推定的新型塑料降解酶将通过ML工具进一步分析，该工具筛选预测的溶解度。我们还将进行化学研究，以评估与现有的低效PETases相比，酶活性的改善。任何假定的塑料降解酶将为定向进化实验提供一个起点，在定向进化实验中，我们选择具有改进特性的酶的新变体。为了更好地探索塑料降解能力的进化，我们将使用我们独特的超高通量颗粒分解试验，每天的通量超过1000万个克隆。因此，我们可以直接评估酶对塑料颗粒（而不是仅模拟塑料的底物）的化学作用能力。这将彻底改变酶促塑料降解领域，因为到目前为止，使用代理底物只能进行边际改进。除了有效的筛选之外，对筛选的输出序列的分析将反馈到我们的生物信息学分析和靶标选择中。我们还将从结构上研究这些酶，以发现它们功能位点的变化如何提高它们结合和消化塑料的能力。这些数据将提供关于蛋白质位点如何发散和进化更好的塑料降解特性的详细见解，从而改进我们的计算机选择方案。我们已经对PETases进行了试点工作，并将在此基础上扩展到其他塑料降解酶（plastizymes）。这种“干”数据科学和“湿”实验工作的紧密结合导致了计算机分析、实验测试和分析工具的改进的强大循环，这些分析工具比目前的小规模蛋白质工程活动更强大。因此，该项目解决了最重要的（也是最困难的）环境挑战之一，但更广泛地说，也提供了一个范例，以证明在没有有效的天然酶的情况下，跨学科的方法可以加速进化。如果成功的话，这种范式将不仅成为“生活规则”（如号召文本中所提到的）的基础，而且成为“超越生活的规则”（如现在所存在的）的基础，旨在解决我们社会的未来需求。

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.