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

蛋白质是自然界的万能功能分子，在温和的条件下以无与伦比的精度工作：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-紧密结合和有效的催化转换-使它们成为可以捕获靶分子并中和，切割或处理它们的试剂。能够模仿大自然在实验室中创造定制分子的能力，将为我们的生活方式带来变革性的变化：例如通过“绿色”工业生产线，资源节约型生物加工或更具选择性的治疗干预。然而，尽管在基础和应用研究方面进行了大量的研究工作，但对酶催化的理解仍然是一个艰巨的挑战。我们的理解当然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：我们收集分子并测试它们中的每一个，看看这个集合中是否有任何一个是谚语中的“大海捞针”。我们做的测试越多，找到有用催化剂的机会就越大：这就是自然界逐渐进化出新分子的方式。我们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：在微流体设备中，我们可以在一天内测试超过1000万个突变体。这给了我们一个技术优势，我们希望在定向进化中更快，并得到更好的催化剂。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）中我们可以去的地方。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），该技术允许我们在每轮进化中获得> 10，000个序列的全长序列（每个序列的价格低于1美分）。这种映射将帮助我们看到我们在序列空间中的位置，并为我们理解进化提供计算帮助（使用相关分析和机器学习），以理解抑制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（通过多轮易错PCR）或使用破坏性但功能上创新的插入-缺失（InDel）文库（由我们的方法TRIAD制作：Nat Commun 2020，11（1），3469 & Proc Natl Acad Sci U S A 2020，117（44），27307-27318）来探测效率和特异性的成功进化的决定因素。具体来说，我们对混杂酶（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，因此跟踪它们的出现有望对酶如何在进化中改变其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之外，我们希望证明对进化的理解可以通过定向进化来告知蛋白质工程。

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.