› 7dcm

X-ray diffraction
2.5Å resolution

Crystal structure of CITX

Released:
DOI: 10.2210/pdb7dcm/pdb
PDB entry 7dcm coloured by chain, front view.
PDB entry 7dcm coloured by chain, side view.
PDB entry 7dcm coloured by chain, top view.
Source organism: Escherichia coli
Primary publication:
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 19 548-555 (2023)
PMID: 36593274

Function and Biology Details

Reaction catalysed: 
2'-(5-triphosphoribosyl)-3'-dephospho-CoA + citrate lyase apo-[acyl-carrier protein] = citrate lyase holo-[acyl-carrier protein] + diphosphate
Biochemical function:
Biological process:
Cellular component:
  • not assigned
Sequence domain:

Structure analysis Details

Assembly composition:
monomeric (preferred)
Assembly name:
PDBe Complex ID:
PDB-CPX-215845 (preferred)
Entry contents:
1 distinct polypeptide molecule
Macromolecule:
Apo-citrate lyase phosphoribosyl-dephospho-CoA transferase Chain: A
Molecule details ›
Chain: A
Length: 186 amino acids
Theoretical weight: 20.72 KDa
Source organism: Escherichia coli
Expression system: Escherichia coli K-12
UniProt:
  • Canonical: P0A6G5 (Residues: 1-183; Coverage: 100%)
Gene names: JW0606, b0614, citX, ybdU
Sequence domains: Apo-citrate lyase phosphoribosyl-dephospho-CoA transferase

Ligands and Environments

1 bound ligand:
Ligand ZN 1 x ZN
1 modified residue:
Ligand MSE 3 x MSE

Experiments and Validation Details

Entry percentile scores
X-ray source: SSRF BEAMLINE BL17U1
Spacegroup: P21
Unit cell:
a: 43.017Å b: 36.784Å c: 48.265Å
α: 90° β: 92.13° γ: 90°
R-values:
R R work R free
0.18 0.171 0.257
Expression system: Escherichia coli K-12

Quick links

Citations

1 review citation

AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design.
Casadevall et al. (2023)

1 mention without citation

Co-evolution-based prediction of metal-binding sites in proteomes by machine learning.
Cheng et al. (2023)