The sigma (σ) receptor system consists of at least two major receptor subtypes: σ₁ and σ₂. Several potential therapeutic applications would benefit from structural knowledge of the σ₂ receptor but gaining this knowledge has been hampered by the difficulties associated with its isolation and, thus, characterization. Here, a ligand based approach has been adopted using the program PHASE® and a group of 41 potent and structurally diverse σ₂ ligands to develop several pharmacophore models for different families of σ₂ ligands. These pharmacophores were analyzed to identify the different binding modes to the receptor and were combined together to construct a comprehensive pharmacophore that was used to develop a structural model for the σ₂ binding pocket. A total of six binding modes were identified and could be classified as neutral or charged modes. The results presented here also indicate the significance of hydrophobic interactions to σ₂ binding and the requirement of hydrogen bonding interactions to increase the affinity for this receptor subtype. This work adds breadth to our knowledge of this receptor's binding site, and should contribute significantly to the development of novel selective σ₂ ligands.
The human dopamine transporter (hDAT) plays many vital functions within the central nervous system and is thus targeted by many pharmaceutical agents. Dopamine‐related therapies are in current development for individuals with dopamine‐related disorders including depression, Parkinson's disease, and psychostimulant addictions such as cocaine abuse. Yet, most efforts to develop new dopamine therapies are within costly structure–activity relationship studies. Through structure‐based drug design techniques, the binding site of hDAT can be utilized to develop novel selective and potent dopamine therapies at reduced costs. However, no structural models of hDAT specifically validated for rational drug design purposes currently exist. Here, using the Drosophila dopamine transporter as a template, a homology model for the hDAT was developed and validated. The model was able to reproduce experimental binding modes with great accuracy, was able to rank inhibitors in the correct order of increasing potency with an R2 value of 0.81 for the test set, and it also outperformed other published hDAT models. Thus, the model can be used reliably in structure‐based drug design projects.
Xanthine oxidase (XO) is an enzyme that converts hypoxanthine to xanthine and xanthine to uric acid, the last two steps in purine metabolism. XO is a molybdenum enzyme, which contains reductive and oxidative half reactions and uses oxygen gas as its final electron acceptor. Because O2 is the final electron receptor, XO generates reducing equivalents as a product of reaction. During an ischemic condition such as a stroke or a heart attack, the oxidative half reaction does not occur, leading to a buildup of reducing equivalents. Upon reintroduction of oxygen, these reducing equivalents rapidly convert oxygen to H2O2 and O2 ? at a rate that cannot be broken down by other enzymes, leading to oxidative tissue damage. Therefore, the overall goal of this project is to find an inhibitor which slows release of electrons to oxygen. Using computer modeling and docking programs various inhibitors were docked into XO active site and their docking energies estimated. Glide® was used to dock the inhibitors and investigate their different binding modes. Docking results were compared to enzyme assay results to validate our computational approach. Enzyme assays were performed using XO purified from cow's milk. Enzyme concentration was measured by examining the absorbance at 450 nm and calculated using the extinction coefficient 37.8 mM/cm AU. Enzyme activity was estimated by measuring the uric acid production at 295nm upon addition of xanthine. Binding of inhibitors to two mutants of Xanthine Dehydrogenase was also investigated computationally. The results showed that 1H‐Benzotriazole‐5‐Carboxylic, Benzotriazole‐1‐methanol and Benzotriazole has the most favorable conformation out of all the inhibitors and these results were used to prepare the enzyme assay. Glide docking result matches the experimental data and we can use these data in future for more experiments.
Regulators of G‐protein signaling (RGS) is a family of approximately 30 proteins that bind to the alpha subunits of G‐proteins (Galpha) and accelerate their GTP hydrolysis rates. This deactivates the G‐protein and terminates G‐Protein Coupled Receptors (GPCRs) signaling. RGS proteins, therefore, play important roles in regulating GPCR signaling and most members are implicated in human diseases such as hypertension, depression, and others. Regulator of G‐protein Signaling 2 (RGS2) is a regulator of angiotensin‐II receptor signaling and a modulator of oxytocin receptor signaling as well. More importantly, RGS2 was reported to be over expressed in the majority of solid breast cancers and in metastatic prostate cancer. For this reason, we sought to develop RGS2 inhibitors as potential chemotherapeutic agents. We utilized structure‐based drug design approaches to develop inhibitors of RGS2‐Galpha‐q interactions. Available structures of the RGS2‐Galpha complex were used to extract a pharmacophore model for searching of commercially available chemical databases. The identified hits were docked to different RGS structures to screen for compounds with the highest binding potential and most selectivity towards RGS2. We report the first group of inhibitors of RGS2‐Galpha‐q interactions developed through rational drug design that interfered with RGS2 signaling in cell‐based assays. In addition, inhibitor AJ‐3 inhibited the growth of MCF‐7 breast cancer cells in cell culture assays, which suggests that RGS2 inhibitors may have a potential to be a new class of chemotherapeutic agents.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.