Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise

We describe a general methodology for designing an empirical scoring function and provide smina, a version of AutoDock Vina specially optimized to support high-throughput scoring and user-specified custom scoring functions. Using our general method, the unique capabilities of smina, a set of default interaction terms from AutoDock Vina, and the CSAR (Community Structure–Activity Resource) 2010 data set, we created a custom scoring function and evaluated it in the context of the CSAR 2011 benchmarking exercise. We find that our custom scoring function does a better job sampling low RMSD poses when crossdocking compared to the default AutoDock Vina scoring function. The design and application of our method and scoring function reveal several insights into possible improvements and the remaining challenges when scoring and ranking putative ligands

Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise

We describe a general methodology for designing an empirical scoring function and provide smina, a version of AutoDock Vina specially optimized to support high-throughput scoring and user-specified custom scoring functions. Using our general method, the unique capabilities of smina, a set of default interaction terms from AutoDock Vina, and the CSAR (Community Structure–Activity Resource) 2010 data set, we created a custom scoring function and evaluated it in the context of the CSAR 2011 benchmarking exercise. We find that our custom scoring function does a better job sampling low RMSD poses when crossdocking compared to the default AutoDock Vina scoring function. The design and application of our method and scoring function reveal several insights into possible improvements and the remaining challenges when scoring and ranking putative ligands

Query results for top four- and five-feature pharmacophore models.

<p>Query results for top four- and five-feature pharmacophore models.</p

The ZINCPharmer based interactive pharmacophore modelling interface used by the students to competitively develop an informative pharmacophore model.

<p>The ZINCPharmer based interactive pharmacophore modelling interface used by the students to competitively develop an informative pharmacophore model.</p

Chemical structure of DHODH inhibitor 5-methyl-7-(naphthalene-2-yl-amino)-1H-[1,2,4]triazolo[1,5-a]pyrimidine-3,8-diium.

<p>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.</p

The distribution of tested and active compounds within the submitted (a) Vina ranked and (b) custom ranked compounds.

<p>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.</p

The number of compounds found to demonstrate inhibition at 10μM overall and with respect to the two different methods of ranking compounds.

<p>The number of compounds found to demonstrate inhibition at 10μM overall and with respect to the two different methods of ranking compounds.</p

Pose prediction of compound 6.

<p>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 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134697#pone.0134697.g001" target="_blank">Fig 1</a>. 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.</p

A pharmacophore derived by a student from a structure of DHODH bound to an inhibitor (PDB 3I65).

<p>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.</p

Pose prediction results.

<p>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 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134697#pone.0134697.ref022" target="_blank">22</a>]. (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.</p