A computational approach to finding novel targets for existing drugs

Abstract

Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects.

Figure 1. The computational molecular-docking pipeline.

Figure 2. Evaluating the known drug-target docking.

1116 (31%) of 3570 known interactions docked with a good score. Two-thirds of the 1116 were ligands docking to non-cognate protein structures, showing that the method could do more than re-dock existing drug-target structures.

Figure 3. Network of known protein-drug interactions.

Proteins are shown as rectangular boxes (nodes), drugs are shown as pink (approved) and blue (experimental) circles, and edges represent known interactions annotated by DrugBank. Edges colored red denote known interactions that were docked with a good icm-score. Here we show only the 252 proteins for which at least one known drug docked well – the ‘reliable-for-docking’ set. The proteins at the bottom of the graph are not connected to other proteins through shared binding drugs.

Figure 4. Score thresholds assessment.

Various combinations of score and rank thresholds were assessed using the positive predictive value (PPV). A) shows the PPVs for thresholds predicting less than 7000 interactions. B) is a zoomed in version showing clearer PPV separation for the top 500 predicted interactions.

Figure 5. A score-plot containing docking ICM- and pmf- scores for 4621 drugs to MAPK14.

Each point represents a drug. The top 5% of the drugs as determined by the consensus scoring threshold are shown as orange dots. These drugs were also docked to the 252 other drug targets in our database, and circles denote the drugs for which this protein was one of the top 5 targets for the drug. The circle colors denote whether the protein rank was based on the ICM score (green) or the pmf score (purple). Finally, drugs that are known to bind MAPK14 are shown in red boxes, and it can be seen than most of these red boxes pass both the consensus and protein rank thresholds.

Figure 6. Testing nilotinib and zafirlukast in ATP-competitive enzymatic assays against MAPK14.

Results are plotted as percent inhibition of activity versus drug concentration. The nilotinib-MAPK14 IC50 was calculated to be 40 nM.

Figure 7. Docking icm- and pmf- scores for BIM-8 docked to 252 reliable-for-docking protein targets.

Each point represents a protein target. Targets for which BIM-8 passed a consensus threshold are shown as orange dots (top 5%) and brown dots (top 1%). Targets with experimental support are enclosed in red colors. Targets that have shown no binding activity with BIM-8 in the literature are shown in shades of green. It can be seen that most of the actual targets of BIM-8 pass stringent consensus score thresholds.

Figure 8. Quantitative interaction map of drugs docked to protein targets, according to their ICM docking score.

Each protein is represented by a column, on which a black cross denotes a known drug docked to the target, a red dot denotes an approved drug docked to the target, and a blue dot denotes an experimental drug docked to the target. Only the top predictions for established drug targets (at least one known approved drug) that docked with a score passing the consensus threshold and had a protein-rank ≤5 are shown.