Modulation of Binding Strength in Several Classes of Active Site Inhibitors of Acetylcholinesterase Studied by Comparative Binding Energy Analysis

Abstract

The comparative binding energy (COMBINE) methodology has been used to identify the key residues that modulate the inhibitory potencies of three structurally different classes of acetylcholinesterase inhibitors (tacrines, huprines, and dihydroquinazolines) targeting the catalytic active site of this enzyme. The extended set of energy descriptors and the partial least-squares methodology used by COMBINE analysis on a unique training set containing all the compounds yielded an interpretable model that was able to fit and predict the activities of the whole series of inhibitors reasonably well (r 2 ) 0.91 and q 2 ) 0.76, 4 principal components). A more robust model (q 2 ) 0.81 and SDEP ) 0.25, 3 principal components) was obtained when the same chemometric analysis was applied to the huprines set alone, but the method was unable to provide predictive models for the other two families when they were treated separately from the rest. This finding appears to indicate that the enrichment in chemical information brought about by the inclusion of different classes of compounds into a single training set can be beneficial when an internally consistent set of pharmacological data can be derived. The COMBINE model was externally validated when it was shown to predict the activity of an additional set of compounds that were not employed in model construction. Remarkably, the differences in inhibitory potency within the whole series were found to be finely tuned by the electrostatic contribution to the desolvation of the binding site and a network of secondary interactions established between the inhibitor and several protein residues that are distinct from those directly involved in the anchoring of the ligand. This information can now be used to advantage in the design of more potent inhibitors.

Binding free energy calculations to rationalize the interactions of huprines with acetylcholinesterase

“…Glu199 is the residue that most contributes to the interaction energy in most huprines (Figure 5), in agreement with previous studies [44]. Glu199 presents several interactions with the surrounding residues mediated by water molecules.…”

Acetylcholinesterase: Molecular Modeling with the Whole Toolkit

“…The residue-specific scoring function is highly amenable to deconvolution, thus permitting the identification and characterization of important ligand-receptor interactions. The resulting list of residues making the most important electrostatic and VDW contributions within the main active site, gorge area, acyl binding pocket, and peripheral site is largely consistent with available X-ray crystallographic and site-directed mutagenesis data and also provides new insight, supporting the concept of a gating role for Tyr337 in huAChE analogous to that of Phe330 in tcAChE as predicted in prior MD calculations [72]. Interestingly, Tyr337 registers within our model not as one of the dominant hydrophobic interactions (as would clearly be expected for Phe330 in tcAChE) but rather as an electrostatic contributor.…”

“…Another recent study employing COMBINE methodology was carried out by Martin-Santamaria and coworkers, exploring ligand binding modes within the tcAChE catalytic active site to identify the key residues that modulate the inhibitive potential of a collection of tacrine-, huprine-, and dihydroquinazoline-based acetylcholinesterase inhibitors [72]. The resulting set of interaction enthalpic descriptors, optimized via partial least-squares fitting across a unique training set containing the full set of compounds, produced a model exhibiting both strong correlation and predictivity (R 2 = 0.91 and Q 2 = 0.76, using 4 principal components) across the entire set of ligands.…”

Exploring the binding of BACE-1 inhibitors using comparative binding energy analysis (COMBINE)

“…In our study, we did not investigate the electrostatic desolvation effects computed with a Poisson-Boltzmann model, which has been proven to yield improved COMBINE models in several previous studies [31,33,54,55]. Nevertheless, our COMBINE models provided useful insights that can be used to design novel BACE-1 inhibitors for the treatment of Alzheimer’s disease.…”