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

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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 fitthe adaptation of the receptor binding pocket to the individual ligand topology. We have therefore extended our concept (software Quasar) by an additional degree of freedomthe fifth dimensionallowing 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-QSARdue to the fact that model selection may vary throughout the entire simulationyields 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.