METALLOCHAPERONES: The partitioning of metals to delivery pathways

金属伴侣：金属到输送途径的分配

• 批准号: BB/R002118/1
• 负责人: Nigel Robinson
• 金额: $ 51.63万
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FR002118%2F1
• 关键词: METALLOCHAPERONES; partitioning; metals; delivery; pathways

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.

生命的大部分反应是由金属催化的。然而，大多数驱动这些反应的酶更愿意与阻止它们活动的金属结合，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，而金属螯合剂和金属离子载体已在生物经济中广泛用作抗菌药物）。由于这些原因，我们通过最近和/或正在进行的合作项目与工业界（龙沙、先正达、宝洁）以及BBSRC金属生物网络在工业生物技术和生物能源领域的480多名成员（约三分之一来自学术界以外）合作。我们的首要目标一直是理解金属的细胞逻辑：也就是说，细胞如何使蛋白质获得正确的金属？这种理解的核心是观察到细胞质溶胶以浓度顺序缓冲金属，这与金属结合偏好相反：因此，紧密结合的金属，如铜、锌和镍，被缓冲到低浓度，而弱结合的金属，如镁、锰和铁，被缓冲到高浓度。金属传感器被调整到这些浓度，以防止缓冲液耗尽或饱和（《自然化学生物学》，目前禁止出版）。然而，这反过来又不可避免地提出了下一个问题，即金属传感器蛋白质本身以及其他金属稳态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一个普通细胞（沙门氏菌）的一组传感器的特性来回答金属传感器的金属特异性问题。简而言之，每种金属的正确传感器只是在该特定金属的集合中最敏感的一个。从这些最近的研究中，我们现在知道了哪些因素决定了最敏感的传感器。我们现在也知道如何计算触发每个沙门氏菌金属传感器的金属浓度，从而对普通细胞质溶胶中每种金属的缓冲浓度有独特的见解。据推测，与金属传感器类似，其他金属稳态蛋白质也被调整到相同的金属浓度。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递送蛋白是如何选择正确的金属的。大约三分之一的金属酶位于特殊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，提供了正确的金属首先分配到递送途径上（否则递送途径将传播错金属化，确实有证据表明这种畸变可能发生）。通过对金属亲和性和金属传递蛋白的丰度进行类似的测量，现在可以确定(1)哪种传递蛋白在每种金属的集合中是最好的；(2)根据触发金属传感器的金属浓度估计，哪种传递蛋白的亲和性高于或低于每种金属的推断缓冲浓度。这些数据将揭示哪些途径可能容易发生金属误用，从而为如何生产颠覆金属处理系统（用作抗菌剂）的配方提供信息。这些数据还将告知如何通过这些途径增强酶金属化，以支持生物技术的合成生物学方法。

项目成果

Metalation: nature's challenge in bioinorganic chemistry.

金属化：生物无机化学中自然的挑战。

• DOI: 10.1007/s00775-020-01790-3
• 发表时间: 2020
• 期刊: a publication of the Society of Biological Inorganic Chemistry
• 作者: Robinson NJ

Protein metalation in a nutshell.

• DOI: 10.1002/1873-3468.14500
• 发表时间: 2023-01
• 期刊: FEBS LETTERS
• 影响因子: 3.5
• 作者: Osman, Deenah;Robinson, Nigel J.
• 通讯作者: Robinson, Nigel J.

Metalation calculators for E. coli strain JM109 (DE3): aerobic, anaerobic, and hydrogen peroxide exposed cells cultured in LB media.

• DOI: 10.1093/mtomcs/mfac058
• 发表时间: 2022-09-01
• 期刊: METALLOMICS
• 影响因子: 3.4
• 作者: Foster, Andrew W.;Clough, Sophie E.;Aki, Zeynep;Young, Tessa R.;Clarke, Alison R.;Robinson, Nigel J.
• 通讯作者: Robinson, Nigel J.

Protein metalation in biology.

• DOI: 10.1016/j.cbpa.2021.102095
• 发表时间: 2022-03
• 期刊: Current opinion in chemical biology
• 影响因子: 7.8
• 作者: Foster AW;Young TR;Chivers PT;Robinson NJ
• 通讯作者: Robinson NJ

Finding the right match

寻找合适的匹配

• DOI: 10.1038/s41570-019-0083-5
• 发表时间: 2019
• 期刊: Nature Reviews Chemistry
• 影响因子: 36.3
• 作者: Schilter D