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© Research
Publication : Journal of chemical information and modeling

Functional motions modulating VanA ligand binding unraveled by self-organizing maps

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in Journal of chemical information and modeling - 15 Jan 2014

Bouvier G, Duclert-Savatier N, Desdouits N, Meziane-Cherif D, Blondel A, Courvalin P, Nilges M, Malliavin TE

Link to Pubmed [PMID] – 24397493

J Chem Inf Model 2014 Jan;54(1):289-301

The VanA D-Ala:D-Lac ligase is a key enzyme in the emergence of high level resistance to vancomycin in Enterococcus species and methicillin-resistant Staphylococcus aureus. It catalyzes the formation of D-Ala-D-Lac instead of the vancomycin target, D-Ala-D-Ala, leading to the production of modified, low vancomycin binding affinity peptidoglycan precursors. Therefore, VanA appears as an attractive target for the design of new antibacterials to overcome resistance. The catalytic site of VanA is delimited by three domains and closed by an ω-loop upon enzymatic reaction. The aim of the present work was (i) to investigate the conformational transition of VanA associated with the opening of its ω-loop and of a part of its central domain and (ii) to relate this transition with the substrate or product binding propensities. Molecular dynamics trajectories of the VanA ligase of Enterococcus faecium with or without a disulfide bridge distant from the catalytic site revealed differences in the catalytic site conformations with a slight opening. Conformations were clustered with an original machine learning method, based on self-organizing maps (SOM), which revealed four distinct conformational basins. Several ligands related to substrates, intermediates, or products were docked to SOM representative conformations with the DOCK 6.5 program. Classification of ligand docking poses, also performed with SOM, clearly distinguished ligand functional classes: substrates, reaction intermediates, and product. This result illustrates the acuity of the SOM classification and supports the quality of the DOCK program poses. The protein-ligand interaction features for the different classes of poses will guide the search and design of novel inhibitors.

http://www.ncbi.nlm.nih.gov/pubmed/24397493