Structural Bioinformatics and QSAR Analysis Applied to the Acetylcholinesterase and Bispyridinium Aldoximes

Abstract

The methods of bioinformatics, molecular modelling, and quantitative structure-activity relationships (QSARs) using regression and artificial neural network (ANN) analyses were applied to develop safer aldoxime antidotes against poisoning by organophosphorus (OP) agents with high, mean, and low aging rates. We start here from a molecular modelling of the mouse AChE at an atomistic level. Aim is to predict qualitatively the structural requirements of an aldoxime that shows an unique reactivating activity against the three classes of OPs. An antidotal action should occur by a three-site mechanism: the aldoxime groups of the first pyridinium ring should point towards the catalytic site, and the second pyridinium ring and its substituents should be anchored at the peripherical and anionic subsites. Based on this model, it is predicted that a suitable substituent is based on an arginine-like moiety. Then, an ANN-based QSAR analysis using a training set of aldoximes with known structure and activities was applied. Its input layer consisted of seven nodes: the group-membership descriptors that parameterize the type of the OP, the logarithms of the distribution coefficients at pH 7.4 and their squared term, the lowest unoccupied molecular orbital (LUMO) energies, the scaled molar refractions of the substituents, and their squared term. It was shown that the qualitative prediction made by molecular modelling can be quantified by an ANN prediction.