@article {262, title = {Modulation of binding strength in several classes of active site inhibitors of acetylcholinesterase studied by comparative binding energy analysis}, journal = {Journal of medicinal chemistry}, volume = {47}, year = {2004}, month = {2004/08/26/}, pages = {4471 - 4482}, 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 (r2 = 0.91 and q2 = 0.76, 4 principal components). A more robust model (q2 = 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.}, keywords = {50;, Algorithms;, Artificial, Binding;, Cholinesterase, Concentration, Inhibitors/chemistry/pharmacology;, Inhibitory, Intelligence;, Protein, Quantitative, Relationship, Relationship;, Structure-Activity}, author = {Martin-Santamaria,S. and Munoz-Muriedas,J. and F. J. Luque and Gago,F.} }