7SQX

Crystal Structure of Pseudomonas aeruginosa lytic polysaccharide monooxygenase CbpD

Summary for 7SQX

• Entry DOI: 10.2210/pdb7sqx/pdb
• Descriptor: Chitin-binding protein CbpD, AMMONIUM ION (3 entities in total)
• Functional Keywords: lpmo virulence factor, type ii secretion, oxidoreductase
• Biological source: Pseudomonas aeruginosa (strain ATCC 15692 / DSM 22644 / CIP 104116 / JCM 14847 / LMG 12228 / 1C / PRS 101 / PAO1)
• Total number of polymer chains: 1
• Total formula weight: 40713.11

Authors

Dade, C.,Douzi, B.,Ball, G.,Voulhoux, R.,Forest, K.T. (deposition date: 2021-11-07, release date: 2022-07-20, Last modification date: 2024-10-23)

Primary citation

Dade, C.M.,Douzi, B.,Cambillau, C.,Ball, G.,Voulhoux, R.,Forest, K.T.
The crystal structure of CbpD clarifies substrate-specificity motifs in chitin-active lytic polysaccharide monooxygenases.
Acta Crystallogr D Struct Biol, 78:1064-1078, 2022

PubMed Abstract: Pseudomonas aeruginosa secretes diverse proteins via its type 2 secretion system, including a 39 kDa chitin-binding protein, CbpD. CbpD has recently been shown to be a lytic polysaccharide monooxygenase active on chitin and to contribute substantially to virulence. To date, no structure of this virulence factor has been reported. Its first two domains are homologous to those found in the crystal structure of Vibrio cholerae GbpA, while the third domain is homologous to the NMR structure of the CBM73 domain of Cellvibrio japonicus CjLPMO10A. Here, the 3.0 Å resolution crystal structure of CbpD solved by molecular replacement is reported, which required ab initio models of each CbpD domain generated by the artificial intelligence deep-learning structure-prediction algorithm RoseTTAFold. The structure of CbpD confirms some previously reported substrate-specificity motifs among LPMOAA10s, while challenging the predictive power of others. Additionally, the structure of CbpD shows that post-translational modifications occur on the chitin-binding surface. Moreover, the structure raises interesting possibilities about how type 2 secretion-system substrates may interact with the secretion machinery and demonstrates the utility of new artificial intelligence protein structure-prediction algorithms in making challenging structural targets tractable.

PubMed: 35916229
DOI: 10.1107/S2059798322007033

Experimental method

X-RAY DIFFRACTION (3 Å)