Comparative study on the use of docking and Bayesian categorization to predict ligand binding to nicotinic acetylcholine receptors (nAChRs) subtypes.

We have carried out a comparative study between docking into homology models and Bayesian categorization, as applied to virtual screening of nicotinic ligands for binding at various nAChRs subtypes (human and rat α4β2, α7, α3β4, and α6β2β3). We found that although results vary with receptor subtype, Bayesian categorization exhibits higher accuracy and enrichment than unconstrained docking into homology models. However, docking accuracy is improved when one sets up a hydrogen-bond (HB) constraint between the cationic center of the ligand and the main-chain carbonyl group of the conserved Trp-149 or its homologue (a residue involved in cation-π interactions with the ligand basic nitrogen atom). This finding suggests that this HB is a hallmark of nicotinic ligands binding to nAChRs. Best predictions using either docking or Bayesian were obtained with the human α7 nAChR, when 100 nM was used as cutoff for biological activity. We also found that ligand-based Bayesian-derived enrichment factors and structure-based docking-derived enrichment factors highly correlate to each other. Moreover, they correlate with the mean molecular fractional polar surface area of actives ligands and the fractional hydrophobic/hydrophilic surface area of the binding site, respectively. This result is in agreement with the fact that hydrophobicity strongly contributes in promoting nicotinic ligands binding to their cognate nAChRs.