Journal of Medicinal Chemistry
5D-QSAR: The key for simulating induced fit ?Authors: Angelo Vedani and Max Dobler
Journal: Journal of Medicinal Chemistry
Biographics Laboratory 3R, Missionsstr. 60, 4055 Basel, Switzerland.
In this journal, we recently reported the development and the validation of a 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 qualitative values: less biased boundary conditions, healthier (i.e. less inbred) model populations and more accurate predictions of new compounds.
Left: Aryl hydrocarbon (Ah) receptor (green/red/blue = training/test/prediction set)
Right: Neurokinin-1 receptor system (green/red/blue = training/test/prediction set)