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Structure-based virtual screening of hypothetical inhibitors of the enzyme longiborneol synthase-a potential target to reduce Fusarium head blight disease.

Authors :
Bresso E
Leroux V
Urban M
Hammond-Kosack KE
Maigret B
Martins NF
Source :
Journal of molecular modeling [J Mol Model] 2016 Jul; Vol. 22 (7), pp. 163. Date of Electronic Publication: 2016 Jun 21.
Publication Year :
2016

Abstract

Fusarium head blight (FHB) is one of the most destructive diseases of wheat and other cereals worldwide. During infection, the Fusarium fungi produce mycotoxins that represent a high risk to human and animal health. Developing small-molecule inhibitors to specifically reduce mycotoxin levels would be highly beneficial since current treatments unspecifically target the Fusarium pathogen. Culmorin possesses a well-known important synergistically virulence role among mycotoxins, and longiborneol synthase appears to be a key enzyme for its synthesis, thus making longiborneol synthase a particularly interesting target. This study aims to discover potent and less toxic agrochemicals against FHB. These compounds would hamper culmorin synthesis by inhibiting longiborneol synthase. In order to select starting molecules for further investigation, we have conducted a structure-based virtual screening investigation. A longiborneol synthase structural model is first built using homology modeling, followed by molecular dynamics simulations that provided the required input for a protein-ligand ensemble docking procedure. From this strategy, the three most interesting compounds (hits) were selected among the 25 top-ranked docked compounds from a library of 15,000 drug-like compounds. These putative inhibitors of longiborneol synthase provide a sound starting point for further studies involving molecular modeling coupled to biochemical experiments. This process could eventually lead to the development of novel approaches to reduce mycotoxin contamination in harvested grain.

Details

Language :
English
ISSN :
0948-5023
Volume :
22
Issue :
7
Database :
MEDLINE
Journal :
Journal of molecular modeling
Publication Type :
Academic Journal
Accession number :
27324634
Full Text :
https://doi.org/10.1007/s00894-016-3021-1