The crystal structure of CbpD clarifies substrate-specificity motifs in chitin-active lytic polysaccharide monooxygenases

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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jul 27, 2022
Source ID
10.1107/s2059798322007033

Entities

People

  • Badreddine Douzi
  • Christian Cambillau
  • Christopher M. Dade
  • Genevieve Ball
  • Katrina T. Forest
  • Romé Voulhoux

Organizations

  • Agence Nationale de la Recherche
  • Agricultural Research Service
  • Aix-Marseille University
  • University of Wisconsin–Madison

Tags

Fields of Study

  • Chemistry

Readers

  • Microbial Pathology
  • Molecular Biology and Genetics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks