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Virtual screening of natural products against 5-enolpyruvylshikimate-3-phosphate synthase using the Anagreen herbicide-like natural compound library.

Maycon Vinicius Damasceno de OliveiraGilson Mateus Bittencourt FernandesKauê Santana da CostaSerhii VakalAnderson Henrique Lima E Lima
Published in: RSC advances (2022)
The shikimate pathway enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) catalyzes a reaction involved in the production of amino acids essential for plant growth and survival. EPSPS is the main target of glyphosate, a broad-spectrum herbicide that acts as a competitive inhibitor concerning phosphoenolpyruvate (PEP), which is the natural substrate of EPSPS. In the present study, we introduce a natural compound library, named Anagreen, which is a compendium of herbicide-like compounds obtained from different natural product databases. Herein, we combined the structure- and ligand-based virtual screening strategies to explore Anagreen against EPSPS using the structure of glyphosate complexed with a T102I/P106S mutant of EPSPS from Eleusine indica ( Ei EPSPS) as a starting point. First, ligand-based pharmacophore screening was performed to select compounds with a similar pharmacophore to glyphosate. Then, structure-based pharmacophore modeling was applied to build a model which represents the molecular features of glyphosate. Then, consensus docking was performed to rank the best poses of the natural compounds against the PEP binding site, and then molecular dynamics simulations were performed to analyze the stability of EPSPS complexed with the selected ligands. Finally, we have investigated the binding affinity of the complexes using free energy calculations. The selected hit compound, namely AG332841, showed a stable conformation and binding affinity to the EPSPS structure and showed no structural similarity to the already known weed EPSPS inhibitors. Our computational study aims to clarify the inhibition of the mutant Ei EPSPS, which is resistant to glyphosate, and identify new potential herbicides from natural products.
Keyphrases
  • molecular dynamics simulations
  • molecular docking
  • molecular dynamics
  • amino acid
  • machine learning
  • dna binding
  • deep learning
  • density functional theory
  • atomic force microscopy
  • free survival