Fig 1.
A pharmacophore derived by a student from a structure of DHODH bound to an inhibitor (PDB 3I65).
The pharmacophore consists of hydrophobic features (green spheres) and a hydrogen donor feature (white sphere). This pharmacophore was used as part of a virtual screen for novel inhibitors.
Fig 2.
The ZINCPharmer based interactive pharmacophore modelling interface used by the students to competitively develop an informative pharmacophore model.
Fig 3.
Chemical structure of DHODH inhibitor 5-methyl-7-(naphthalene-2-yl-amino)-1H-[1,2,4]triazolo[1,5-a]pyrimidine-3,8-diium.
The six pharmacophore features used to define the sparse model search space are labeled. Green labels refer to hydrophobic groups, blue labels refer to hydrogen bond features.
Table 1.
Query results for the top five sparse pharmacophore models.
Table 2.
Query results for top four- and five-feature pharmacophore models.
Fig 4.
Pose prediction of compound 6.
Receptor structure and binding site residues of 3I65 are shown in blue. Compound 6 is shown in magenta sticks. (a) Compound 6 aligned to the pharmacophore of Fig 1. The compound makes a hydrogen bond with HIS-185. (b) After minimization, the pose has twisted so that the hydrogen bond to HIS-185 is broken. (c) When the pharmacophore aligned posed is minimized with a flexible ARG-265, which sterically clashes with the initial pose, a less dramatic movement is observed and the hydrogen bond to HIS-185 is maintained.
Fig 5.
The crystal structure of compound 6 (pink sticks) bound to its receptor (silver) is compared to the predicted poses. Pose alignments were obtained by aligning the crystal receptor with 3I65 using PyMOL [22]. (a) The pharmacophore-aligned pose has a heavy-atom RMSD to the crystal pose of 1.77Å while (b) the pose minimized with a flexible ARG-265 has an RMSD of 1.18Å.
Fig 6.
The distribution of tested and active compounds within the submitted (a) Vina ranked and (b) custom ranked compounds.
For each ranking 1000 compounds were submitted from which the TDT organizers sampled a total of 167 compounds for testing. Within the Vina ranking, better scoring compounds are more likely to be active. No such enrichment is observed for the custom ranking.
Table 3.
The number of compounds found to demonstrate inhibition at 10μM overall and with respect to the two different methods of ranking compounds.
Fig 7.
The nine active compounds with unpublished chemical scaffolds identified by the exercise.
Compounds are shown with their measured IC50 and their rank within the Vina ranked list and the custom scored list (italic). The one compound present in both lists was the only novel compound to demonstrate sub-micromolar affinity. All compounds have a Tanimoto similarity coefficient of less than 0.16 with respect to the ligand used to define the pharmacophore as computed by OpenBabel with FP2 fingerprints.