In this article, we describe a computational methodology that analyzes the results generated in ligand docking
and evaluates the correlation between simulation results and intrinsic characteristics present in the crystallographic
structures used in the simulation, such as thermal parameter, resolution, and overall quality of the X-ray diffraction data.
We focus our analysis on molecular docking data obtained from application of differential evolution implemented in the
program Molegro Virtual Docker. As a protein target, we selected the enzyme 3-dehydroquinate dehydratase (DHQD).
This enzyme is part of the shikimate pathway, and a protein target for development of anti-tubercular drugs. We used a set
with 20 DHQD crystallographic structures with ligands bound to the active site. In order to identify the best approach to
molecular docking, we analyzed crystallographic parameters and looked for correlation between the docking results and
structural features present in the protein target. Analysis of docking results helps to identify the best approach to use in
ligand docking and identify structural features important for the success of this methodology. Analysis of results
generated by ligand docking focused on DHQD made possible to assess the best docking protocol for this enzyme and use
this optimized approach in the more computational demanding methodology of virtual screening (VS). We used a data set
of natural products to identify structural features important for ligand-binding affinity.
Keywords: 3-dehydroquinate dehydratase, bioinformatics, data mining, ligand docking, Mycobacterium tuberculosis,
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