› 3n86

X-ray diffraction
1.9Å resolution

Crystal structure of 3-dehydroquinate dehydratase from Mycobacterium tuberculosis in complex with inhibitor 4

Released:
DOI: 10.2210/pdb3n86/pdb
PDB entry 3n86 coloured by chain, front view.
PDB entry 3n86 coloured by chain, side view.
PDB entry 3n86 coloured by chain, top view.
Source organism: Mycobacterium tuberculosis
Primary publication:
Structural investigation of inhibitor designs targeting 3-dehydroquinate dehydratase from the shikimate pathway of Mycobacterium tuberculosis.
Dias MV, Snee WC, Bromfield KM, Payne RJ, Palaninathan SK, Ciulli A, Howard NI, Abell C, Sacchettini JC, Blundell TL
Biochem J 436 729-39 (2011)
PMID: 21410435

Function and Biology Details

Reaction catalysed: 
3-dehydroquinate = 3-dehydroshikimate + H(2)O
Biochemical function:
Biological process:
Cellular component:
Sequence domains:

Structure analysis Details

Assembly composition:
homo dodecamer (preferred)
Assembly name:
PDBe Complex ID:
PDB-CPX-162016 (preferred)
Entry contents:
1 distinct polypeptide molecule
Macromolecule:
3-dehydroquinate dehydratase Chains: A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, X
Molecule details ›
Chains: A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, X
Length: 147 amino acids
Theoretical weight: 15.81 KDa
Source organism: Mycobacterium tuberculosis
Expression system: Escherichia coli BL21(DE3)
UniProt:
  • Canonical: P9WPX7 (Residues: 1-147; Coverage: 100%)
Gene names: MTCY159.19, Rv2537c, aroD, aroQ
Sequence domains: Dehydroquinase class II
Structure domains: Dehydroquinase, class II

Ligands and Environments

1 bound ligand:
Ligand RJP 24 x RJP
No modified residues

Experiments and Validation Details

Entry percentile scores
X-ray source: SLS BEAMLINE X06DA
Spacegroup: P21
Unit cell:
a: 91.24Å b: 143.976Å c: 148.628Å
α: 90° β: 90° γ: 90°
R-values:
R R work R free
0.171 0.155 0.212
Expression system: Escherichia coli BL21(DE3)

Quick links

Citations

3 review citations

Structural biology and drug discovery of difficult targets: the limits of ligandability.
Surade et al. (2012)
2 more

3 mentions without citation

DeepBindRG: a deep learning based method for estimating effective protein-ligand affinity.
Zhang et al. (2019)
2 more