Anion‑π Interactions in Computer-Aided Drug Design: Modeling the Inhibition of Malate Synthase by Phenyl-Diketo Acids

Human
infection by Mycobacterium tuberculosis (Mtb)
continues to be a global epidemic. Computer-aided drug design (CADD)
methods are used to accelerate traditional drug discovery efforts.
One noncovalent interaction that is being increasingly identified
in biological systems but is neglected in CADD is the anion-π
interaction. The study reported herein supports the conclusion that
anion-π interactions play a central role in directing the binding
of phenyl-diketo acid (PDKA) inhibitors to malate synthase (GlcB),
an enzyme required for Mycobacterium tuberculosis virulence. Using density functional theory methods (M06-2X/6-31+G­(d)),
a GlcB active site template was developed for a predictive model through
a comparative analysis of PDKA-bound GlcB crystal structures. The
active site model includes the PDKA molecule and the protein determinants
of the electrostatic, hydrogen-bonding, and anion-π interactions
involved in binding. The predictive model accurately determines the
Asp 633-PDKA structural position upon binding and precisely predicts
the relative binding enthalpies of a series of 2-ortho halide-PDKAs
to GlcB. A screening model was also developed to efficiently assess
the propensity of each PDKA analog to participate in an anion-π
interaction; this method is in good agreement with both the predictive
model and the experimental binding enthalpies for the 2-ortho halide-PDKAs.
With the screening and predictive models in hand, we have developed
an efficient method for computationally screening and evaluating the
binding enthalpy of variously substituted PDKA molecules. This study
serves to illustrate the contribution of this overlooked interaction
to binding affinity and demonstrates the importance of integrating
anion-π interactions into structure-based CADD.