Multiple-Conformation and Protonation-State Representation in 4D-QSAR:  The Neurokinin-1 Receptor System

Abstract

Using a 4D-QSAR approach (software Quasar) allowing for multiple-conformation, orientation, and protonation-state ligand representation as well as for the simulation of local induced-fit phenomena, we have validated a family of receptor surrogates for the neurokinin-1 (NK-1) receptor system. The evolution was based on a population of 500 receptor models and simulated during 40 000 crossover steps, corresponding to 80 generations. It yielded a cross-validated r(2) of 0.887 for the 50 ligands of the training set (represented by a total of 218 conformers and protomers) and a predictive r(2) of 0.834 for the 15 ligands of the test set (70 conformers and protomers). A series of five "scramble tests" (with an average predictive r(2) of -0.438) demonstrates the sensitivity of the surrogate toward the biological data, for which it should establish a QSAR. On the basis of this model, the activities of 12 new compounds - four of which have been synthesized and tested in the meantime - are predicted. For most of the NK-1 antagonists, the genetic algorithm selected a single entity - out of the up to 12 conformers or protomers - to preferably bind to the receptor surrogate. Moreover, the evolution converged at an identical protonation scheme for all NK-1 antagonists. This indicates that 4D-QSAR techniques may, indeed, reduce the bias associated with the choice of the bioactive conformation as each ligand molecule can be represented by an ensemble of conformations, orientations, and protonation states.

Molecular Characterization of the Substance P·Neurokinin-1 Receptor Complex

“…Most of these residues are located within the hydrophobic core formed by the seven transmembrane helices (TM), predominantly in the region of TM5 and TM6. Using these putative sites of ligand/receptor interaction and computational models of the receptor, insight into the mode of ligand binding of these antagonists has been developed (7)(8)(9).…”

Induced Fit—The Key for Understanding LSD Activity? A 4D-QSAR Study on the 5-HT2A Receptor System

“…The value obtained for the 4D-QSAR study (À1.05) indicates a strong sensitivity, while the value of the 3D-QSAR experiment (0.14) is just acceptable. Other parameters suggesting that a model based on a single-conformer selection is more dif®cult to obtain include the slope ( These new technologies would also seem less susceptible to the right answer (predictive r 2 value) for the wrong reason (pharmacophore hypothesis, conformer selection) tag, still`a ttached'' to older QSAR concepts as the more recent algorithms allow for a multiple-conformation [25±29], orientation [27], protonation [25], and enantiomer [52] representation of the ligand molecules. In this context, it is worth mentioning that the simulated evolution does not simply select the lowest-energy conformer due to the lowest (i.e.…”

Artificial Intelligence Approaches for Rational Drug Design and Discovery

“…A detailed review on the applications of CoMFA and CoMSIA has been presented by Bordas et al [67]. In 4D-QSAR, the fourth dimension represents an ensemble of conformations, orientations, or protonation states for each molecule [75]. This reduces the bias that may come from the ligand alignment, but requires identification of the most likely bioactive conformation and orientation (or protonation state), frequently obtained using evolutionary algorithms [31].…”