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基于协同进化的蛋白质组中金属结合位点的机器学习预测。

Co-evolution-based prediction of metal-binding sites in proteomes by machine learning.

机构信息

Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, China.

Department of Chemical Biology, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.

出版信息

Nat Chem Biol. 2023 May;19(5):548-555. doi: 10.1038/s41589-022-01223-z. Epub 2023 Jan 2.

DOI:10.1038/s41589-022-01223-z
PMID:36593274
Abstract

Metal ions have various important biological roles in proteins, including structural maintenance, molecular recognition and catalysis. Previous methods of predicting metal-binding sites in proteomes were based on either sequence or structural motifs. Here we developed a co-evolution-based pipeline named 'MetalNet' to systematically predict metal-binding sites in proteomes. We applied MetalNet to proteomes of four representative prokaryotic species and predicted 4,849 potential metalloproteins, which substantially expands the currently annotated metalloproteomes. We biochemically and structurally validated previously unannotated metal-binding sites in several proteins, including apo-citrate lyase phosphoribosyl-dephospho-CoA transferase citX, an Escherichia coli enzyme lacking structural or sequence homology to any known metalloprotein (Protein Data Bank (PDB) codes: 7DCM and 7DCN ). MetalNet also successfully recapitulated all known zinc-binding sites from the human spliceosome complex. The pipeline of MetalNet provides a unique and enabling tool for interrogating the hidden metalloproteome and studying metal biology.

摘要

金属离子在蛋白质中具有各种重要的生物学作用,包括结构维持、分子识别和催化。以前预测蛋白质组中金属结合位点的方法基于序列或结构基序。在这里,我们开发了一种基于共进化的管道,名为“MetalNet”,用于系统地预测蛋白质组中的金属结合位点。我们将 MetalNet 应用于四种代表性原核生物的蛋白质组,并预测了 4849 个潜在的金属蛋白,这大大扩展了目前注释的金属蛋白组。我们通过生物化学和结构验证了几个以前未注释的金属结合位点,包括无结构或序列同源性的 apo-citrate 裂解酶磷酸核糖-dephospho-CoA 转移酶 citX,一种缺乏结构或序列同源性的大肠杆菌酶已知的金属蛋白(蛋白质数据库 (PDB) 代码:7DCM 和 7DCN)。MetalNet 还成功地从人类剪接体复合物中重现了所有已知的锌结合位点。MetalNet 的流水线为研究隐藏的金属蛋白组和金属生物学提供了一个独特而有效的工具。

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