Multiple Fragment Docking and Linking in Primary and Secondary Pockets of Dopamine Receptors

A sequential docking methodology was applied to computationally predict starting points for fragment linking using the human dopamine D₃ receptor crystal structure and a human dopamine D₂ receptor homology model. Two focused fragment libraries were docked in the primary and secondary binding sites, and best fragment combinations were enumerated. Similar top scoring fragments were found for the primary site, while secondary site fragments were predicted to convey selectivity. Three linked compounds were synthesized that had 9-, 39-, and 55-fold selectivity in favor of D₃ and the subtype selectivity of the compounds was assessed on a structural basis

The Impact of Molecular Dynamics Sampling on the Performance of Virtual Screening against GPCRs

The formation of ligand–protein complexes requires simultaneous adaptation of the binding partners. In structure based virtual screening, high throughput docking approaches typically consider the ligand flexibility, but the conformational freedom of the protein is usually taken into account in a limited way. The goal of this study is to elaborate a methodology for incorporating protein flexibility to improve the virtual screening enrichments on GPCRs. Explicit-solvated molecular dynamics simulations (MD) were carried out in lipid bilayers to generate an ensemble of protein conformations for the X-ray structures and homology models of both aminergic and peptidergic GPCRs including the chemokine CXCR₄, dopamine D₃, histamine H₄, and serotonin 5HT₆ <i>holo</i> receptor complexes. The quality of the receptor models was assessed by enrichment studies to compare X-ray structures, homology models, and snapshots from the MD trajectory. According to our results, selected frames from the MD trajectory can outperform X-ray structures and homology models in terms of enrichment factor and AUC values. Significant changes were observed considering EF1% values: comparing the original CXCR₄, D₃, and H₄ targets and the additional 5HT₆ initial models to that of the best MD frame resulted in 0 to 6.7, 0.32 to 3.5 (10×), 13.3 to 26.7 (2×), and 0 to 14.1 improvements, respectively. It is worth noting that rank-average based ensemble evaluation calculated for different ensemble sizes could not improve the results further. We propose here that MD simulation can capture protein conformations representing the key interacting points of the receptor but less biased toward one specific chemotype. These conformations are useful for the identification of a “consensus” binding site with improved performance in virtual screening

The Impact of Molecular Dynamics Sampling on the Performance of Virtual Screening against GPCRs

The formation of ligand–protein complexes requires simultaneous adaptation of the binding partners. In structure based virtual screening, high throughput docking approaches typically consider the ligand flexibility, but the conformational freedom of the protein is usually taken into account in a limited way. The goal of this study is to elaborate a methodology for incorporating protein flexibility to improve the virtual screening enrichments on GPCRs. Explicit-solvated molecular dynamics simulations (MD) were carried out in lipid bilayers to generate an ensemble of protein conformations for the X-ray structures and homology models of both aminergic and peptidergic GPCRs including the chemokine CXCR₄, dopamine D₃, histamine H₄, and serotonin 5HT₆ <i>holo</i> receptor complexes. The quality of the receptor models was assessed by enrichment studies to compare X-ray structures, homology models, and snapshots from the MD trajectory. According to our results, selected frames from the MD trajectory can outperform X-ray structures and homology models in terms of enrichment factor and AUC values. Significant changes were observed considering EF1% values: comparing the original CXCR₄, D₃, and H₄ targets and the additional 5HT₆ initial models to that of the best MD frame resulted in 0 to 6.7, 0.32 to 3.5 (10×), 13.3 to 26.7 (2×), and 0 to 14.1 improvements, respectively. It is worth noting that rank-average based ensemble evaluation calculated for different ensemble sizes could not improve the results further. We propose here that MD simulation can capture protein conformations representing the key interacting points of the receptor but less biased toward one specific chemotype. These conformations are useful for the identification of a “consensus” binding site with improved performance in virtual screening