A Combination of Pharmacophore and Docking-based Virtual Screening to Discover new Tyrosinase Inhibitors.

Melanogenesis controls the formation of melanin pigment whose overproduction is related to various hyperpigmentary disorders in humans. Tyrosinase is a type-3 copper enzyme involved in the rate limiting step of melanin synthesis, therefore its inhibition could represent an efficient way for the development of depigmenting agents. In this work, a combination of pharmacophore and docking-based studies has been employed to screen two in-house 3D compound databases containing about 2,000 molecules from natural and synthetic sources. As result we selected two "hit compounds" which proved to inhibit tyrosinase activity showing IC50 values in the micromolar range.

Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations.

We present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure-based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high number of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and rescored to generate a single hit-list. The score for a particular molecule is calculated based on the number of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. We test the performance of CHA for virtual screening using screening databases with active and inactive compounds for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the experimental structure in the PDB, and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availabilithy of diverse sets of different pharmacophore models is utilized to analyze some additional questions of interest in 3D pharmacophore-based virtual screening.