Abstract
The role of virtual ligand screening in modern drug discovery is to mine large chemical collections and to prioritize for experimental testing a comparatively small and diverse set of compounds with expected activity against a target. Several studies have pointed out that the performance of virtual ligand screening can be improved by taking into account receptor flexibility. Here, we systematically assess how multiple crystallographic receptor conformations, a powerful way of discretely representing protein plasticity, can be exploited in screening protocols to separate binders from non-binders. Our analyses encompass 36 targets of pharmaceutical relevance and are based on actual molecules with reported activity against those targets. The results suggest that an ensemble receptor-based protocol displays a stronger discriminating power between active and inactive molecules as compared to its standard single rigid receptor counterpart. Moreover, such a protocol can be engineered not only to enrich a higher number of active compounds, but also to enhance their chemical diversity. Finally, some clear indications can be gathered on how to select a subset of receptor conformations that is most likely to provide the best performance in a real life scenario.
Highlights
For over 20 years, High-Throughput Screening (HTS) has been one of the leading hit identification strategies in drug discovery [1]
Re-ranking according to the best score (MRC-score) was a straightforward procedure: individual scores were merged into one list, which was sorted in ascending order
We used the combination obtained by re-ranking all ligands according to the best rank that each compound obtained across individual runs (MRC-rank)
Summary
For over 20 years, High-Throughput Screening (HTS) has been one of the leading hit identification strategies in drug discovery [1]. HTS has a relatively high rate of false positives and false negatives, and is limited to comparatively small screening libraries. In this regard, Virtual Ligand Screening (VLS) represents a fast and cost-effective alternative, in which much larger libraries are screened by computational means [3,4]. VLS protocols can be devised to improve ‘‘early recognition’’, namely to increase the number of active compounds that are prioritized for testing [5], and to catch the broadest possible chemical diversity Strategies to achieve these improvements may vary depending on the in silico approach to VLS. This is detrimental for early recognition since active compounds will act as true binders only in the presence of the right receptor conformation [16,17]
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