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.

蛋白质是在轻度条件下具有无与伦比精度的大自然的全用功能分子：它们的选择性使它们能够从细胞中的数千个中识别一个分子。它们的功效 - 紧密的结合和有效的催化转换 - 使它们可以捕获目标分子并中和，切割或处理它们。能够模仿自然创建量身定制分子的能力，在实验室中，将使我们的生活方式变革变革：例如通过“绿色”工业生产线，资源有效的生物处理或更具选择性的治疗干预措施。然而，尽管基础研究和应用研究的强烈研究工作，对酶催化的理解仍然是一个艰巨的挑战。我们的理解肯定未能通过最严重的测试 - 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：我们制作分子的集合并测试它们中的每一个，以查看该系列中的任何一个人是否是众所周知的“干草中的针头”。我们进行的测试越多，找到有用的催化剂的机会就越好：这就是自然逐渐进化的新分子的机会。我们已经开发了一个测试系统，该系统可以在实验室中进行通常进行的更多测试：在微流体设备中，我们一天可以测试超过1000万个突变体。这为我们带来了技术优势，我们希望在定向进化中更快地变得更快，并促进更好的催化剂。但是我们还必须选择“序列空间”（蛋白质的所有可能氨基酸随机分析的函数）中的位置。为了进行探究，我们使用了最近开发的技术（“ umic-seq”：Nat Commun 2020，11（1），6023），该技术使我们能够获得每轮进化的全长序列> 10,000序列（以每序列小于1便士的价格）。这种映射将帮助我们查看我们在序列空间中的去向，并为我们提供计算帮助，以理解进化（使用相关分析和机器学习），以了解表征基因内部epitisasis的合作相互作用模式。进化将进行缓慢而稳定（通过多个容易出错的PCR）或功能性但功能性创新的插入插入（Indel）库（由我们的方法三合会制造：Nat Commun 2020，11（1），3469，3469，3469＆Proc Natl Acad Sci Sci Sci Sci us a 2020，117（44），27307-273307-273318）特异性。具体而言，我们对滥交酶的Follwing进化轨迹感兴趣（具有多个功能的酶），因为它们被认为是进化的弹簧板，因此跟踪其出现的承诺可以使酶在进化中如何改变其功能有特别有用的见解。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直接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