Decode protein-metabolite regulatory network: one MIDAS at a time.
Decode protein-metabolite regulatory network: one MIDAS at a time.
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DOI：
10.1038/s41392-023-01566-6
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发表时间：
2023-08-23
影响因子：
39.3
通讯作者：
Gao, Chen
中科院分区：
文献类型：
--
作者：
Liu, Tian;Gao, Chen
在最近发表在《科学》杂志上的一项研究中，希克斯等人。利用质谱学结合平衡透析发现变构系统(MIDAS)来探索蛋白质-代谢物相互作用组，并揭示了先前未知的乳酸脱氢酶的调节。1这份报告提出了一种令人兴奋的新方法来解决生物学中长期存在的挑战。细胞功能是由分子组成的交互网络协调的，分子组成是蛋白质与其他分子实体(如DNA、RNA和代谢物)之间的物理相互作用。就像生命之书的基本词汇一样，这些相互作用是复杂生理和疾病的基本机制。在过去的二十年里，高通量测序和基于质谱仪的分析工具的发展帮助解开了分子身份及其相互作用的史无前例的复杂程度，产生了对蛋白质-蛋白质、蛋白质-DNA和蛋白质-RNA相互作用的迅速深入的理解。相比之下，蛋白质-代谢物相互作用(PMI)仍然没有明确的定义。越来越多的证据支持代谢物的重要性，不仅作为代谢反应的底物，而且通过它们相互作用的蛋白质作为具有强大功能影响的信号分子。因此，为了填补这一重要空白，迫切需要开发无偏见的方法来系统和全面地确定PMI网络。一个最新的发展是LIP-SMAP(LIP-小分子相互作用映射)平台。这是一种基于质谱学的技术，利用与代谢物结合时改变的蛋白质对蛋白质分解的敏感性。在未变性的条件下提取的全细胞裂解物加或不加代谢物处理。代谢物的结合改变了其同源结合蛋白的蛋白分解敏感性，使其与非结合蛋白区分开来。使用LiPSMap，Piazza等人。通过揭示已知和新的PMI在大肠杆菌中的相互作用，展示了它的效力，并揭示了代谢物-蛋白质通讯的功能和结构原理。2 LIP-SMAP方法的一个独特特征是，它不仅将绘制蛋白质与代谢物的相互作用图，而且还提供目标蛋白质上潜在配体结合位点的信息。另一个大规模的作图工具是PROMIS(蛋白质-代谢物相互作用)平台，它结合了大小排除层析与蛋白质组和代谢组学方法，根据未标记的蛋白质和代谢物之间的共分离曲线来检测它们之间的相互作用。在Luzarowski等人最近发表的一篇文章中，酵母细胞分裂被用作内源蛋白质-蛋白质和蛋白质-代谢物复合体的来源。用体积排阻色谱分离络合物，然后用液-质联用检测同一组分中的蛋白质和代谢物。建立了基于层析共分离的酿酒酵母蛋白质组和代谢组的PMI数据集。3本报告展示了PROMIS以无偏见的方式全面分析PMI的优势，然而，检测可能受到代谢物及其目标蛋白天然丰度的限制。最近加入的是Midas Platform，这是一种基于质谱学的方法，利用未标记代谢物的平衡透析。这项新技术已经产生了第一个关于特定代谢物和碳水化合物代谢中目标蛋白质的人类PMI的详细图谱，展示了一条在哺乳动物中建立细胞PMI图景的潜在途径。1《The…》
In a recent study published in Science, Hicks et al. utilized Mass spectrometry integrated with equilibrium dialysis for the discovery of allostery systematically (MIDAS) to explore protein-metabolite interactome and have revealed previously unknown regulation for lactate dehydrogenase. 1 This report brings an exciting new approach to address a long-standing challenge in biology. Cellular functions are orchestrated by an interactive network of molecular constituents, the physical interactions between proteins and other molecular entities, such as DNA, RNA, and metabolites. Like basic vocabulary for the book of life, these interactions are the fundamental mechanisms for complex physiology and diseases. Over the past two decades, the development of high-throughput sequencing and mass-spec based analysis tools helped to unlock the molecular identities and their interactions at unprecedented scales of complexity, yielding rapidly advanced understanding to protein-protein, protein-DNA and protein-RNA interactions. In contrast, the protein-metabolite interaction (PMI) remains poorly defined. Increasing evidence supports the importance of metabolites not just as substrates for metabolic reactions, but also as signaling molecules with potent functional impact through their interacting proteins. Therefore, developing unbiased approaches to systematically and comprehensively determine PMI network is critically needed to fill this important gap. One recent development is LiP-SMap (Lip-small molecule interactions mapping) platform. It is a mass-spectrometry based technology taking advantage of altered protein sensitivity to proteolysis upon binding with metabolites. Whole cell lysates extracted under undenatured conditions are treated with or without a metabolite. Binding of the metabolite changes the proteolytic susceptibility of its cognate binding protein, allowing it to be differentiated from the non-binding proteins. Using LiPSMap, Piazza et al. showed its potency by uncovering known and novel PMI interactions in Escherichia coli and revealed functional and structural principles of metabolite-protein communication. 2 One unique feature of LiP-SMap approach is that it will not only map protein-metabolite interactions, but also provides information on the potential ligand-binding sites on the targeted proteins. Another large-scale mapping tool is PROMIS (Protein-Metabolite Interactions) platform which combines size-exclusion chromatography with proteomic and metabolomic methods to detect interactions between untagged proteins and metabolites based on their co-segregation profiles. In a recent publication by Luzarowski et al., dividing yeast cells were used as a source for endogenous protein-protein and protein-metabolite complexes. The complexes were fractionated using size exclusion chromatography followed by liquid chromatography-mass spectrometry to detect both proteins and metabolites from the same fraction. A dataset for PMI based on co-segregation from chromatography was established for the entire proteome and metabolome in Saccharomyces cerevisiae. 3 This report showcases the advantage of PROMIS for comprehensive profiling of PMIs in an unbiased fashion, however, the detection may be limited by the native abundance of the metabolites and their target proteins. The most recent entry is MIDAS platform which is a mass-spec based approach utilizing equilibrium dialysis of untagged metabolites. This new technology has yielded the first detailed map of human PMI for specific metabolites and targeted proteins in carbohydrate metabolism, demonstrating a potential path to establish cellular PMI landscape in mammals. 1 The …
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影响因子：
56.9
作者：
Hicks, Kevin G.;Cluntun, Ahmad A.;Schubert, Heidi L.;Hackett, Sean R.;Berg, Jordan A.;Leonard, Paul G.;Aleixo, Mariana A. Ajalla;Zhou, Youjia;Bott, Alex J.;Salvatore, Sonia R.;Chang, Fei;Blevins, Aubrie;Barta, Paige;Tilley, Samantha;Leifer, Aaron;Guzman, Andrea;Arok, Ajak;Fogarty, Sarah;Winter, Jacob M.;Ahn, Hee-Chul;Allen, Karen N.;Block, Samuel;Cardoso, Iara A.;Ding, Jianping;Dreveny, Ingrid;Gasper, William C.;Ho, Quinn;Matsuura, Atsushi;Palladino, Michael J.;Prajapati, Sabin;Sun, Pengkai;Tittmann, Kai;Tolan, Dean R.;Unterlass, Judith;VanDemark, Andrew P.;Vander Heiden, Matthew G.;Webb, Bradley A.;Yun, Cai-Hong;Zhao, Pengkai;Wang, Bei;Schopter, Francisco J.;Hill, Christopher P.;Nonato, Maria Cristina;Muller, Florian L.;Cox, James E.;Rutter, Jared
通讯作者：
Rutter, Jared
影响因子：
5.9
作者：
Luzarowski M;Vicente R;Kiselev A;Wagner M;Schlossarek D;Erban A;de Souza LP;Childs D;Wojciechowska I;Luzarowska U;Górka M;Sokołowska EM;Kosmacz M;Moreno JC;Brzezińska A;Vegesna B;Kopka J;Fernie AR;Willmitzer L;Ewald JC;Skirycz A
通讯作者：
Skirycz A
影响因子：
64.5
作者：
Piazza, Ilaria;Kochanowski, Karl;Picotti, Paola
通讯作者：
Picotti, Paola
影响因子：
20.1
作者：
Lopaschuk GD;Karwi QG;Tian R;Wende AR;Abel ED
通讯作者：
Abel ED