METALLOCHAPERONES: The partitioning of metals to delivery pathways

生命中很大一部分反应是由金属催化的。然而，推动这些反应的大多数酶更喜欢与阻止其活性的金属结合，而不是与正确的金属结合。因此，细胞必须帮助酶获得正确的金属。许多金属催化的反应对生物技术具有很高的价值和/或是抗菌治疗的目标(免疫系统已经进化到利用金属来控制所谓的营养免疫中的微生物，而根据经验，金属螯合剂和金属离子载体在生物经济中被广泛用作抗菌剂)。出于这些原因，我们通过最近和/或正在进行的合作项目与Industry(Lonza，Syngenta，Procter and Gamble)合作，以及我们工业生物技术和生物能源领域BBSRC金属生物学网络的480多名成员(约三分之一来自学术界)。我们的首要目标一直是了解金属的细胞逻辑：即细胞如何使蛋白质能够获得正确的金属？这种理解的核心是观察到，胞质按浓度顺序缓冲金属，这与金属结合偏好相反：因此，铜、锌和镍等紧密结合的金属被缓冲到低浓度，而镁、锰和亚铁等弱结合金属被缓冲到较高浓度。金属传感器被调整到这些浓度，以防止缓冲区变得枯竭或饱和(《自然化学生物学》，目前被禁运，正在出版中)。然而，这反过来又提出了一个不可避免的下一个问题，即金属传感器蛋白本身以及其他金属动态平衡的蛋白质如何选择正确的金属。最近，我们已经能够通过比较来自普通细胞(沙门氏菌)的一组传感器的性质来回答金属传感器的金属特异性问题。简而言之，对于每种金属，正确的传感器只是一组中对该特定金属最敏感的那个。从这些最近的研究中，我们现在知道是什么因素决定了集合中最灵敏的传感器。我们现在还知道如何计算触发每个沙门氏菌金属传感器的金属浓度，从而对公共胞质中每种金属的缓冲浓度有独特的见解。它假设，类似于金属传感器，其他金属动态平衡的蛋白质也会调整到这些相同的金属浓度。这项研究现在将抓住机会，应用类似的方法来了解一组金属输送蛋白如何选择正确的金属。大约三分之一的金属酶处于特殊的金属输送途径的末端。这解决了这些金属酶的金属选择性的挑战，前提是首先将正确的金属分配到传递途径上(否则传递途径将传播错误的金属化，确实有证据表明这种异常可能发生)。通过对金属传递蛋白的金属亲和力和丰度进行类似的测量，现在可以识别(1)对于每种金属来说，哪种传递蛋白在集合中是最好的，以及(2)根据触发金属传感器的金属浓度估计，对于每种金属，哪些传递蛋白的亲和力高于或低于推断的缓冲浓度。这些数据将揭示哪些途径可能容易发生错误金属化，从而了解如何产生颠覆金属处理系统(用作抗菌剂)的配方。这些数据还将介绍如何通过这些途径加强酶的金属化，以支持生物技术的合成生物学方法。

A large proportion of the reactions of life are catalysed by metals. Yet most of the enzymes driving these reactions prefer to associate with metals that prevent their activity rather than with the correct metals. Cells must therefore help enzymes to acquire the correct metals. Many metal-catalysed reactions are of high value to biotechnology and/or are the targets of antimicrobial treatments (immune systems have evolved to exploit metals to control microbes in so-called nutritional immunity, while metal-chelants and metal-ionophores have empirically been used as antimicrobials widely across the bioeconomy). For these reasons we have worked with Industry (Lonza, Syngenta, Procter and Gamble) via recent and/or on-going collaborative projects, plus with more than 480 members (about a third from outside academia) of our BBSRC Metals in Biology Network in Industrial Biotechnology and Bioenergy.Our overarching goal has always been to understand the cellular logic for metals: That is, how do cells enable proteins to acquire the correct metals? Central to this understanding is an observation that the cytosol buffers metals in an order of concentrations which is the inverse of metal-binding preferences: Thus tight-binding metals such as copper, zinc and nickel are buffered to low concentrations while weak-binding metals like magnesium, manganese and ferrous-iron, are buffered to higher concentrations. Metal-sensors are tuned to these concentrations to prevent the buffers from becoming depleted or saturated (Nature Chemical Biology, currently embargoed and in press). However this, in turn, raises an inevitable next question as to how the metal-sensor proteins themselves, along with other proteins of metal homeostasis, select the correct metals.Recently we have been able to answer the question of metal-specificity of metal-sensors by comparing properties within a set of sensors from a common cell (Salmonella). In short, the correct sensor for each metal is simply whichever one is the most sensitive in the set for that particular metal. From these recent studies we now know what factors determine the most sensitive sensor in the set. We also now know how to calculate the metal concentration that triggers each Salmonella metal sensor, and thus have unique insight into the buffered concentrations of each metal in a common cytosol. It is hypothesised that, akin to the metal-sensors, other proteins of metal-homeostasis are also tuned to these same metal concentrations. This study will now seize the opportunity to apply similar approaches to understand how a set of metal-delivery proteins select the correct metals. About one third of metalloenzymes are at the end of specialised metal delivery pathways. This solves the challenge of metal-selectivity for these metalloenzymes provided the correct metals partition onto the delivery pathways in the first place (otherwise the delivery-pathways will propagate mismetalation, and indeed there is evidence that such aberrations can occur). By making similar measurements of the metal-affinities and abundances of metal delivery proteins, it will now become possible to identify (1) which delivery protein is the best in the set for each metal and (2) which delivery proteins have affinities above or below the inferred buffered concentrations for each metal, as estimated from the metal-concentrations which trigger the metal-sensors. These data will reveal which pathways may be vulnerable to mismetalation and hence inform upon how to generate formulations which subvert the metal-handling systems (for use as antimicrobials). These data will also inform on how to enhance enzyme metalation via these pathways in support of synthetic biological approaches to biotechnology.