Lim, V. J. Y. | bioRxiv Science

bioRxiv2021

Differential recognition of opioid analgesics by μ opioid receptors: Predicted interaction patterns correlate with ligand-specific voltage sensitivity

The {micro} opioid receptor (MOR) is the key target for analgesia, but the application of opioids is accompanied by several issues. There is a wide range of opioid analgesics, differing in their chemical structure and their properties in receptor activation and subsequent effects. A better understanding of ligand-receptor interactions and resulting effects is important. Here, we calculated the respective binding modes for several opioids and analyzed fingerprints of ligand-receptor interactions. We further corroborated the binding modes experimentally by cellular assays. As ligand-induced modulation of activity due to changes in membrane potential was displayed by MOR, we further analyzed the effects of voltage sensitivity of this receptor. With a combined in silico and in vitro approach, we defined discriminating interaction patterns for the ligand-specific voltage sensitivity. With this, we present new insights for interactions likely in ligand recognition and their specific effects on activation of the MOR.

pharmacology and toxicology↗

bioRxiv2018

A benchmarking study on virtual ligand screening against homology models of human GPCRs

G-protein-coupled receptor (GPCR) is an important target class of proteins for drug discovery, with over 27% of FDA-approved drugs targeting GPCRs. However, being a membrane protein, it is difficult to obtain the 3D crystal structures of GPCRs for virtual screening of ligands by molecular docking. Thus, we evaluated the virtual screening performance of homology models of human GPCRs with respect to the corresponding crystal structures. Among the 19 GPCRs involved in this study, we observed that 10 GPCRs have homology models that have better or comparable performance with respect to the corresponding X-ray structures, making homology models a viable choice for virtual screening. For a small subset of GPCRs, we also explored how certain methods like consensus enrichment and sidechain perturbation affect the utility of homology models in virtual screening, as well as the selectivity between agonists and antagonists. Most notably, consensus enrichment across multiple homology models often yields results comparable to the best performing model, suggesting that ligand candidates predicted with consensus scores from multiple models can be the optimal option in practical applications where the performance of each model cannot be estimated.

bioinformatics↗