5D-QSAR:  The Key for Simulating Induced Fit?

Abstract

In this journal we recently reported the development and the validation of a four-dimensional (4D)-QSAR (quantitative structure-activity relationships) concept, allowing for multiple conformation, orientation, and protonation state representation of ligand molecules. While this approach significantly reduces the bias with selecting a bioactive conformer, orientation, or protonation state, it still requires a "sophisticated guess" about manifestation and magnitude of the associated local induced fit-the adaptation of the receptor binding pocket to the individual ligand topology. We have therefore extended our concept (software Quasar) by an additional degree of freedom--the fifth dimension--allowing for a multiple representation of the topology of the quasi-atomistic receptor surrogate. While this entity may be generated using up to six different induced-fit protocols, we demonstrate that the simulated evolution converges to a single model and that 5D-QSAR--due to the fact that model selection may vary throughout the entire simulation--yields less biased results than 4D-QSAR where only a single induced- fit model can be evaluated at a time. Using two bioregulators (the neurokinin-1 receptor and the aryl hydrocarbon receptor), we compare the results obtained with 4D- and 5D-QSAR. The NK-1 receptor system (represented by a total of 65 antagonist molecules) converges at a cross-validated r2 of 0.870 and a predictive r2 of 0.837; the corresponding values for the Ah receptor system (represented by a total of 131 dibenzodioxins, dibenzofurans, biphenyls, and polyaromatic hydrocarbons) are 0.838 and 0.832, respectively. The results indicate that the formal investment of additional computer time is well-returned both in quantitative and in qualitative values: less-biased boundary conditions, healthier (i.e., less inbred) model populations, and more accurate predictions of new compounds.

Computational Methods in Drug Discovery

“…In a method developed by Vedani and Dobler (2002), induced fit is simulated by mapping a "mean envelope" for all ligands in a training set on to an "inner envelope" for each individual molecule. Their method involves several protocols for evaluating induced-fit models including a linear scale based on the adaptation of topology, adaptations based on property fields, energy minimization, and lipophilicity potential.…”

Employing conformational analysis in the molecular modeling of agrochemicals: insights on QSAR parameters of 2,4-D

“…A similar approach including other indices, like hydrogen bond, was further developed and named Comparative Molecular Similarity Indices Analysis (CoMSIA) (KLEBE et al, 1994), but no significant improvement in comparison to CoMFA was achieved. In order to account for new dimensions, namely conformational and alignment freedom, receptor insight and solvation effects, 4D (HOPFINGER et al, 1997), 5D (VEDANI;DOBLER, 2002) and 6D QSAR (VEDANI et al, 2005) methods have been developed. In all cases, the three dimensional structure of a possible ligand is required and, consequently, several methods to generate and optimize molecular structures should be used to give rise an input geometry for the QSAR calculations.…”

VirtualToxLab ~ in silica prediction of the toxic potential of drugs and environmental chemicals: evaluation status and Internet access protocol

“…Quasar -a receptor-modeling concept developed at the Biographics Laboratory 3R -is based on 6D-QSAR and explicitly allows for the simulation of induced fit (Vedani and Dobler, 2002;Vedani et al, 2005b). Quasar generates a family of quasi-atomistic receptor surrogates that are optimised by means of a genetic algorithm.…”