Evaluation of Amides, Carbamates, Sulfonamides, and Ureas of 4-Prop-2-ynylidenecycloalkylamine as Potent, Selective, and Bioavailable Negative Allosteric Modulators of Metabotropic Glutamate Receptor 5

Novel substituted 4-(Arylethynyl)-Pyrrolo[2,3-d]pyrimidines negative allosteric modulators (NAMs) of the metabotropic glutamate receptor subtype 5 (mGlu5) Treat depressive disorder in mice

In view of the fact that the G-protein-coupled receptors (GPCRs) sit at the top of the signaling pathways triggering a diverse range of signaling cascades towards a cellular event, GPCRs are regarded as central drug targets. mGlu5, a type of classical GPCRs, is highly expressed in the central nervous system (CNS) and responds to the neurotransmitter glutamate. Researches show that mGlu5 is a potential drug target for the treatment of depression. Up to now, multiple mGlu5 negative allosteric modulators (NAMs) have entered clinical trials, but no small molecule mGlu5 NAM has yet to reach market. Herein, we report the structural optimization and structure-activity relationship studies of a series of novel mGlu5 NAMs. Among them, the novel compound 10b is a high-affinity mGluR5 antagonist, with an IC50 value of 11.5 nM. Besides, we evaluated the anti-depressant effect of compound 10b using the chronic unpredictable mild stress (CUMS)-induced depression model. The data showed that the mice in CUMS group were featured by decreased level of serum 5-HT and increased level of serum CORT, and the expression of synaptic proteins were reduced, including GluA1, GluA2, p-PKA, BDNF and TrkB. However, those factors for identifying sensitivity to depression-like behaviors could be improved by compound 10b treatment. The preliminary toxicology evaluations indicated that compound 10b had a good safety profile in vivo. Collectively, the compound 10b represents a promising lead compound for the treatment of depressive disorder.

In Silico End-to-End Protein-Ligand Interaction Characterization Pipeline: The Case of SARS-CoV-2

SARS-CoV-2 triggered a worldwide pandemic disease, COVID-19, for which an effective treatment has not yet been settled. Among the most promising targets to fight this disease is SARS-CoV-2 main protease (Mpro), which has been extensively studied in the last few months. There is an urgency for developing effective computational protocols that can help us tackle these key viral proteins. Hence, we have put together a robust and thorough pipeline of in silico protein-ligand characterization methods to address one of the biggest biological problems currently plaguing our world. These methodologies were used to characterize the interaction of SARS-CoV-2 Mpro with an α-ketoamide inhibitor and include details on how to upload, visualize, and manage the three-dimensional structure of the complex and acquire high-quality figures for scientific publications using PyMOL (Protocol 1); perform homology modeling with MODELLER (Protocol 2); perform protein-ligand docking calculations using HADDOCK (Protocol 3); run a virtual screening protocol of a small compound database of SARS-CoV-2 candidate inhibitors with AutoDock 4 and AutoDock Vina (Protocol 4); and, finally, sample the conformational space at the atomic level between SARS-CoV-2 Mpro and the α-ketoamide inhibitor with Molecular Dynamics simulations using GROMACS (Protocol 5). Guidelines for careful data analysis and interpretation are also provided for each Protocol.

Subtype-selective mechanisms of negative allosteric modulators binding to group I metabotropic glutamate receptors

Group I metabotropic glutamate receptors (mGlu₁ and mGlu₅) are promising targets for multiple psychiatric and neurodegenerative disorders. Understanding the subtype selectivity of mGlu₁ and mGlu₅ allosteric sites is essential for the rational design of novel modulators with single- or dual-target mechanism of action. In this study, starting from the deposited mGlu₁ and mGlu₅ crystal structures, we utilized computational modeling approaches integrating docking, molecular dynamics simulation, and efficient post-trajectory analysis to reveal the subtype-selective mechanism of mGlu₁ and mGlu₅ to 10 diverse drug scaffolds representing known negative allosteric modulators (NAMs) in the literature. The results of modeling identified six pairs of non-conserved residues and four pairs of conserved ones as critical features to distinguish the selective NAMs binding to the corresponding receptors. In addition, nine pairs of residues are beneficial to the development of novel dual-target NAMs of group I metabotropic glutamate receptors. Furthermore, the binding modes of a reported dual-target NAM (VU0467558) in mGlu₁ and mGlu₅ were predicted to verify the identified residues that play key roles in the receptor selectivity and the dual-target binding. The results of this study can guide rational structure-based design of novel NAMs, and the approach can be generally applicable to characterize the features of selectivity for other G-protein-coupled receptors.

Molecular Docking: Shifting Paradigms in Drug Discovery

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.