Mapping the overlapping fitness landscapes of a superfamily of promiscuous enzymes: strategies for directed evolution?

绘制混杂酶超家族的重叠适应度景观：定向进化策略？

• 批准号: BB/W000504/1
• 负责人: Florian Hollfelder
• 金额: $ 76.96万
• 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: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FW000504%2F1
• 关键词: Mapping; overlapping; fitness; landscapes; superfamily

Proteins are Nature's all-purpose functional molecules that work with unsurpassed precision under mild conditions: their selectivity allows them to recognise one molecule out of thousands in a cell. Their efficacy - tight binding and efficient catalytic turnover - makes them reagents that can catch onto target molecules and neutralize, cleave or process them. Being able to emulate Nature's ability to create tailor-made molecules, in the laboratory would bring transformational change to the way we live: e.g. via 'green' industrial production lines, resource-efficient bioprocessing or more selective therapeutic intervention. However, understanding of enzyme catalysis remains a daunting challenge, despite intense research efforts in basic and applied research. Our understanding certainly fails the most severe test - that of making catalysts that meet the efficiency of natural enzymes. Directed evolution is a new approach to this problem: we make collections of molecules and test each of them to see whether any one in this collection is the proverbial 'needle in a haystack'. The more tests we do, the better are the chances of finding useful catalysts: this is how Nature has gradually evolved new molecules. We have developed a testing system that can do more tests normally carried out in a lab: in microfluidic devices we can test more than 10 million mutants in a day. This gives us a technological advantage and we hope to be faster in directed evolution and get better catalysts out. But we also have to choose where in 'sequence space' (a function of all possible amino acid randomisations of a protein) we can go. To probe this, we use a technology we have recently developed ('UMIC-Seq': Nat Commun 2020, 11 (1), 6023) that allows us to obtain a full-length sequence of > 10,000 sequence per round of evolution (at a price of less than 1 penny per sequence). This kind of mapping will help us to see where we are going in sequence space and sets us up for computational help in understanding evolution (using correlation analysis and machine learning), to understand the cooperative interaction patterns that characterise intra-gene epistasis. Evolution will be carried out slow and steady (via multiple rounds of error-prone PCR) or with dispruptive yet functionally innovative insertion-deletion (InDel) libraries (made by our method TRIAD: Nat Commun 2020, 11 (1), 3469 & Proc Natl Acad Sci U S A 2020, 117 (44), 27307-27318) to probe the determinants of successful evolution of efficiency and specificity. Specifically we are interested in follwing evolutionary trajectories of promiscuous enzymes (enzymes with multiple functions), because they are beieved to be springboards of evolution, so tracking their emergence promises to yield particularly useful insights into how enzymes change their function in evolution. In addition to a fundamental interest in a mechanism fundamental to life, we hope to demonstrate that an understanding of evolution can inform protein engineering by directed evolution.

蛋白质是自然界万能的功能分子，在温和的条件下具有无与伦比的精确度：它们的选择性使它们能够识别细胞中数千个分子中的一个。它们的功效--紧密的结合和高效的催化周转--使它们成为能够捕捉目标分子并中和、切割或加工它们的试剂。如果能够在实验室中模仿大自然制造定制分子的能力，将给我们的生活方式带来革命性的变化：例如，通过“绿色”工业生产线、资源高效的生物处理或更有选择性的治疗干预。然而，尽管在基础和应用研究方面做出了密集的研究努力，但对酶催化的理解仍然是一个艰巨的挑战。我们的理解肯定没有通过最严格的测试--即制造符合天然酶效率的催化剂。定向进化是解决这个问题的一种新方法：我们收集分子，并对每个分子进行测试，看看这个集合中是否有任何一个是谚语中的大海捞针。我们做的试验越多，找到有用催化剂的机会就越大：这就是大自然如何逐渐进化出新分子的。我们已经开发了一种测试系统，可以进行更多通常在实验室进行的测试：在微流控设备中，我们一天可以测试1000多万个突变基因。这为我们提供了技术优势，我们希望在定向进化方面速度更快，并推出更好的催化剂。但我们也必须选择在“序列空间”(蛋白质所有可能的氨基酸随机化的函数)中我们可以到达的位置。为了探索这一点，我们使用了我们最近开发的一种技术(‘UMIC-Seq’：NAT Commun 2020，11(1)，6023)，它允许我们在每一轮进化中获得&gt；10,000序列的全长序列(每个序列的价格不到1便士)。这种映射将帮助我们看到我们在序列空间中的位置，并为我们在理解进化方面提供计算帮助(使用相关分析和机器学习)，理解表征基因内上位性的合作相互作用模式。进化将缓慢而稳定地进行(通过多轮容易出错的聚合酶链式反应)或使用分散但功能创新的插入-缺失(INDELL)文库(由我们的方法Triad：NAT Commun 2020，11(1)，3469&Proc Natl Acad Sci U S A2020,117(44)，27307-27318建立)，以探索效率和特异性成功进化的决定因素。具体地说，我们对杂交酶(具有多种功能的酶)的进化轨迹感兴趣，因为它们被认为是进化的跳板，所以跟踪它们的出现有望对酶如何改变其在进化中的功能产生特别有用的见解。除了对生命基本机制的基本兴趣外，我们还希望证明，对进化的理解可以通过定向进化为蛋白质工程提供信息。

项目成果

Selection of a Promiscuous Minimalist cAMP Phosphodiesterase from a Library of De Novo Designed Proteins

• DOI: 10.1101/2023.02.13.528392
• 发表时间: 2023-02
• 期刊: bioRxiv
• 作者: J. Schnettler;Michael S. Wang;Maximilian Gantz;Christina Karas;F. Hollfelder;M. Hecht

Ultrahigh-throughput directed evolution of a metal-free a/ß-hydrolase with a Cys-His-Asp triad into an efficient phosphotriesterase

具有 Cys-His-Asp 三联体的无金属 a/α-水解酶超高通量定向进化为高效磷酸三酯酶

• DOI: 10.1101/2022.02.14.480337
• 发表时间: 2022
• 作者: Schnettler Fernández D

Evolution of protease activation and specificity via alpha-2-macroglobulin-mediated covalent capture.

• DOI: 10.1038/s41467-023-36099-7
• 发表时间: 2023-02-11
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Knyphausen, Philipp;Rangel Pereira, Mariana;Brear, Paul;Hyvonen, Marko;Jermutus, Lutz;Hollfelder, Florian
• 通讯作者: Hollfelder, Florian