7dcn

<StructureSection load='7dcn' size='340' side='right'caption='[[7dcn]], [[Resolution|resolution]] 1.69&Aring;' scene=''>

<table><tr><td colspan='2'>[[7dcn]] is a 1 chain structure with sequence from [https://en.wikipedia.org/wiki/Escherichia_coli Escherichia coli]. Full crystallographic information is available from [http://oca.weizmann.ac.il/oca-bin/ocashort?id=7DCN OCA]. For a <b>guided tour on the structure components</b> use [https://proteopedia.org/fgij/fg.htm?mol=7DCN FirstGlance]. <br>

== Function ==

[https://www.uniprot.org/uniprot/CITX_ECOLI CITX_ECOLI] Transfers 2-(5''-triphosphoribosyl)-3'-dephosphocoenzyme-A on a serine residue to the apo-acyl carrier protein (gamma chain) of the citrate lyase to yield holo-acyl carrier protein.<ref>PMID:10924139</ref> <ref>PMID:11042274</ref>

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== Publication Abstract from PubMed ==

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.

Co-evolution-based prediction of metal-binding sites in proteomes by machine learning.,Cheng Y, Wang H, Xu H, Liu Y, Ma B, Chen X, Zeng X, Wang X, Wang B, Shiau C, Ovchinnikov S, Su XD, Wang C Nat Chem Biol. 2023 May;19(5):548-555. doi: 10.1038/s41589-022-01223-z. Epub 2023 , Jan 2. PMID:36593274<ref>PMID:36593274</ref>

From MEDLINE&reg;/PubMed&reg;, a database of the U.S. National Library of Medicine.<br>

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== References ==

<references/>

[[Category: Escherichia coli]]