Enhancing Specificity and Sensitivity of Pharmacophore-Based Virtual Screening by Incorporating Chemical and Shape Features−A Case Study of HIV Protease Inhibitors

Abstract

Virtual screening (VS), if applied appropriately, could significantly shorten the hit identification and hit-to-lead processes in drug discovery. Recently, the version of VS that is based upon similarity to a pharmacophore has received increased attention. This is due to two major factors: first, the public availability of the ZINC1 conformational database has provided a large selection pool with high-quality and purchasable small molecules; second, new technology has enabled a more accurate and flexible definition of pharmacophore models coupled with an efficient search speed. The major goal of this study was to achieve improved specificity and sensitivity of pharmacophore-based VS by optimizing the variables used to generate conformations of small molecules and those used to construct pharmacophore models from known inhibitors or from inhibitor-protein complex structures. By using human immunodeficiency virus protease and its inhibitors (PIs) as a case study, the impact of the key variables, including the selection of chemical features, involvement of excluded volumes (EV), the tolerance radius of excluded volumes, energy windows, and the maximum number of conformers in conformation generation, was explored. Protein flexibility was simulated by adjusting the sizes of EV. Our best pharmacophore model, combining both chemical features and excluded volumes, was able to correctly identify 60 out of 75 structurally diverse known PIs, while misclassifying only 5 out of 75 similar compounds that are not inhibitors. To evaluate the specificity of the model, 1193 oral drugs on the market were screened, and 25 original hits were identified, including 5 out of 6 known PI drugs.

Pharmacophore-based virtual screening: a review of recent applications

“…Among these, virally encoded enzymes integrase [50][51][52][53] and protease [54,55] have been subjected to extensive pharmacophore modeling for inhibitor screening. In addition, the glycoprotein has been subjected to pharmacophore modeling for successful identification of a receptor antagonist [56].…”

“…HIV protease has also been actively pursued by pharmacophore-based virtual screening as a promising target in antiviral therapy [54,55]. To enhance the specificity and sensitivity of pharmacophore-based virtual screening, Pandit et al [54] optimized the variables for the generation of small-molecule conformation, and for pharmacophore model construction of inhibitors.…”

“…To enhance the specificity and sensitivity of pharmacophore-based virtual screening, Pandit et al [54] optimized the variables for the generation of small-molecule conformation, and for pharmacophore model construction of inhibitors. The key variables explored in their study included the selection of chemical features, involvement of excluded volumes, tolerance radius of excluded volumes, energy windows and maximum number of conformers.…”

Pharmacophore-Based Virtual Screening

“…In HTS, a compound will not be detected as a hit, if it can interact with the target only at its high-energy conformation due to the low apparent concentration. To simulate this situation, some researchers suggested to set a low energy cutoff when generating conformational database [6,7].…”

A self‐organizing algorithm for molecular alignment and pharmacophore development

“…Wang et al 49 postulated a three-point model with one carbonyl H-bond acceptor and two hydroxyl H-bond donors. Pandit et al 74 hypothesized a four-point model with one hydroxyl Hbond donor neighbored by one hydrophobic and two aromatic groups, with excluded volumes from the receptor structure and partial matching of one among four features (two HBAs, one HBD, one hydrophobic) added for specificity. Using multiple protein crystal structures and MD simulation, Meagher et al 75 were able to derive a ''consensus of consensus'' pharmacophore with eight points: two H-bond donors near the catalytic aspartates, four aromatic/hydrophilic groups around the periphery of the active site, and two aromatic/hydrophobic groups near the center of the active site.…”