Highly SpecIfic and Sensitive Pharmacophore Model for Identifying CXCR4 Antagonists. Comparison with Docking and Shape-Matching Virtual Screening Performance

Abstract

HIV infection is initiated by fusion of the virus with the target cell through binding of the viral gp120 protein with the CD4 cell surface receptor protein and the CXCR4 or CCR5 coreceptors. There is currently considerable interest in developing novel ligands that can modulate the conformations of these coreceptors and, hence, ultimately block virus−cell fusion. Herein, we present a highly specific and sensitive pharmacophore model for identifying CXCR4 antagonists that could potentially serve as HIV entry inhibitors. Its performance was compared with docking and shapematching virtual screening approaches using 3OE6 CXCR4 crystal structure and high-affinity ligands as query molecules, respectively. The performance of these methods was compared by virtually screening a library assembled by us, consisting of 228 high affinity known CXCR4 inhibitors from 20 different chemotype families and 4696 similar presumed inactive molecules. The area under the ROC plot (AUC), enrichment factors, and diversity of the resulting virtual hit lists was analyzed. Results show that our pharmacophore model achieves the highest VS performance among all the docking and shape-based scoring functions used. Its high selectivity and sensitivity makes our pharmacophore a very good filter for identifying CXCR4 antagonists.

Partial Least Squares Discriminant Analysis and 3D Similarity Perspective Applied to Analyze Comprehensively the Selectivity of Glycogen Synthase Kinase 3 Inhibitors

“…We noted that the best AUC rankings were obtained with ST (0.904, query 1 ) for CDK‐2/GSK‐3 dataset, and shape and physico‐chemical‐based coefficients, e. g. CoT which display an AUC of 0.986 for CDK‐4/GSK‐3 dataset, while FTvC yields an AUC of 0.957 for PKC\GSK‐3 dataset. This observation is consistent with previous remarks, which affirm that selectivity is determined by both shape and pharmacophore features . The most evident variance concerning AUC for selective queries was observed for FTv coefficient (Figure ).…”

Anti-HIV Drug Development Through Computational Methods

“…Recently, a highly specific and sensitive pharmacophore model was reported for identifying CXCR4 antagonists that could potentially serve as HIV entry inhibitors (51). It achieved the best performance when compared with docking and shapematching virtual screening approaches using the 3OE6 CXCR4 crystal structure and high-affinity ligands as query molecules, respectively (51).…”

“…It achieved the best performance when compared with docking and shapematching virtual screening approaches using the 3OE6 CXCR4 crystal structure and high-affinity ligands as query molecules, respectively (51). Although no CXCR4 antagonist has been approved for the clinical treatment of HIV infection, numerous candidates are under investigation, and several are in clinical trials (41-43, 46, 52, 53).…”

Selective DNA Gyrase Inhibitors: Multi-Target in Silico Profiling with 3D-Pharmacophores

“…In addition, 3D-pharmacophores are highly useful and efficient tools for data mining (hit finding) and medicinal chemistry decision support in drug discovery research [ 31 , 32 ]. Furthermore, they have been reported to be highly efficient and accurate virtual screening (VS) filters when compared to docking and shape-based approaches [ 33 ]. In relevant studies, cyclothialidines were utilized to identify GyrB subunit inhibitors using 3D-pharmacophore, docking, and VS approaches [ 34 ].…”