A calculator for metalation inside a cell

生物技术在很大程度上依赖于含有金属的酶，因为据估计，几乎一半的酶需要一种或多种结合金属。有时，这些金属嵌入到辅因子中，如维生素B12中的钴、血红素中的铁或辅因子F430中的镍。用正确的金属装载金属酶，或装载为辅因子提供金属的蛋白质，是所有活细胞的非凡成就。我们的长期目标是了解细胞内的金属化作用，并使这一过程可预测和可利用。一种易于使用的、基于计算机的体内金属化计算器将被生产和测试。细胞内金属的可利用性是决定哪些金属与哪些蛋白质结合的主要因素(Nature 2008 455：1138-1142)。细胞内环境缓冲了一些金属，使其可利用性低于其他金属，DNA结合金属传感器可根据这些可利用性进行调整(自然化学生物学2017 13：409-417)。在BBSRC(加上宝洁)的支持下，在七年的时间里，为一套DNA结合的金属传感器收集了一系列热力学数值，使计算细菌中金属的细胞内可利用性成为可能(自然化学生物学2019 15：241-249)。通过参考这些细胞内金属可利用性的值，可以对金属蛋白的金属化做出粗略的预测，特别是确定适合蛋白质的正确金属，而不仅仅是结合最紧密的金属。现在我们将改进(并测试)这些预测，以了解金属化和错金属化是如何随着培养条件的变化而变化的。做出这种预测的能力与合成生物学中金属化的最优化有关，对工业生物技术很有价值，但也与系统生物学和更广泛的细胞的基本理解有关。细胞内金属可利用性的计算将利用三个概念上的进展：第一，金属的细胞生物学是快速的，并通过不稳定的缓冲液关联；第二，如果金属被缓冲，金属传感器的热力学循环在数学上是容易处理的(因此从质量平衡计算中排除)；第三，参考可用金属的这些值可以计算蛋白质(或其他分子)的体内金属化状态，这可以解释金属间的竞争。Pi和COI Warren指导了第一阶段，现在是第二阶段，BBSRC工业生物技术和生物能源网络，目的是加速利用生物研究中的金属进展，以支持生物制造和生物技术的其他方面。这项提案中工作的一个目的是使行业平台以及学者能够接触到金属化的优化，他们不一定是该分支学科的专家。例如，应用可以包括优化生物过程中的金属化或优化抗微生物离子载体的错误金属化。我们将使用三种不同的系统来测试计算器的三种不同类型的预测。我们以前与所有系统合作过，并获得了支持他们选择这项工作的先前数据。其中一个系统是由沃伦设计的以异源方式产生非天然四吡咯的大肠杆菌：这些四吡咯包括辅因子F430和维生素B12。后者具有直接的实际意义。我们有证据(未发表)表明，在合成的大肠杆菌系统中，B12的钴输送蛋白与锌发生错误金属化，但当细胞补充多余的钴时，这一问题可以克服。这项工作的第二个目的是操纵这种菌株，以增加维生素B12的供应，以满足越来越多转向低肉类饮食的个人(植物既不制造也不需要维生素B12)，从而例证了金属化计算器的优点。

Biotechnology is heavily dependent upon metal-containing enzymes since almost a half of all enzymes are estimated to require one or more bound metal. Sometimes the metals are embedded within cofactors, such as cobalt in vitamin B12, iron in heme or nickel in cofactor F430. Loading metalloenzymes, or loading the proteins that supply metals to cofactors, with the correct metal is a remarkable achievement of all living cells. Our long-term goal has been to understand intracellular metalation and to make this process predictable and exploitable. An easy-to-use, computer-based, calculator of metalation in vivo will be produced and tested.The availabilities of metals inside cells is a dominant factor in determining which metals bind to which proteins (Nature 2008 455: 1138-1142). The intracellular milieu buffers some metals to lower availabilities than other metals and DNA-binding metal sensors are tuned to these availabilities (Nature Chemical Biology 2017 13: 409-417). Over the course of seven years, supported by the BBSRC (plus Procter and Gamble), a series of thermodynamic values were collected for a set of DNA-binding metal sensors, making it possible to calculate the intracellular availabilities of metals in a bacterium (Nature Chemical Biology 2019 15: 241-249). By reference to these values for intracellular metal-availability, it has become possible to make coarse-grain predictions about the metalation of metalloproteins, specifically identifying the right metal for a protein and not merely the tightest-binding metal. Now we will refine (and test) these predictions to understand how metalation and mis-metalation changes as a function of culture conditions. An ability to make such predictions is relevant to the optimisation of metalation in synthetic biology, is of value to industrial biotechnology, but also relevant to basic understanding of cells in systems biology and more generally.Calculation of intracellular metal availabilities will exploit three conceptual advances: First that the cell biology of metals is rapid and associative via a labile buffer; second that the thermodynamic cycles of metal-sensors are mathematically tractable if metals are buffered (hence excluded from mass balance calculations); third that reference to these values for available metal enables calculation of the in vivo metalation state of a protein (or other molecule) that can account for inter-metal competition. The PI and CoI Warren directed a phase I, and now phase II, BBSRC Network in Industrial Biotechnology and Bioenergy with the purpose of accelerating the exploitation of advances in metals in biology research to support bio-manufacturing and other facets of biotechnology. A purpose of the work in this proposal is to make the optimisation of metalation accessible to a platform of industries, as well as to academics, who need not be experts in the sub-discipline. Applications could include the optimisation of metalation in bioprocesses or the optimisation of mis-metalation by antimicrobial ionophores, as examples. We will use three different systems to test three distinct types of predictions of the calculator. We have worked with all of the systems previously and obtained prior data supporting their choice for this work. One of the systems is E. coli that has been engineered by Warren to heterologously produce non-native tetrapyrroles: These tetrapyrroles include cofactor F430 and vitamin B12. The latter has immediate practical relevance. We have evidence (unpublished) that in the synthetic E. coli system the cobalt delivery protein for B12 becomes mis-metalated with zinc but that this can be overcome when cells are supplemented with surplus cobalt. A secondary purpose of this work is to manipulate this strain to enhance the supply of vitamin B12 for the increasing number of individuals turning to low meat diets (plants neither make nor require vitamin B12), in turn exemplifying the merit of the metalation calculator.