Allosteric Cooperativity Mechanism Investigation of Orthosteric and Allosteric Ligands in Modulating AR Activity: A Molecular Dynamics Study

Identification of allosteric sites and allosteric inhibitors in the glucose-dependent insulinotropic polypeptide receptor via molecular simulations.

This study provides potential lead compounds for the design of small-molecule allosteric drugs targeting class B1 GPCRs and performs conformational sampling and combined dynamic pocket detection algorithms, MDpocket and FTMove, to identify six characteristic cryptic pockets within the dynamic trajectories.

Residue Interaction Network Reveals Allosteric Pathways Linking Orthosteric and Intracellular Sites in Class A GPCRs

Understanding how allosteric modulators influence protein dynamics is essential for guiding drug design. This work analyses a total of 45 μs of classical molecular dynamics simulations for four class A G-protein-coupled receptors (GPCRs), namely the Complement C5a receptor (C5AR1), the Purinergic Receptor P2Y (P2RY1), and the Cannabinoid Receptors 1 and 2 (CNR1/CNR2). Protein dynamics is essential to detect the shallow extrahelical binding sites, such as the one found in P2RY1. Current methods for computing Allosteric Communication Networks (ACNs) produce complex outputs requiring expert interpretation. To address this, we focus on the shortest paths of information transfer between the orthosteric and G-protein binding sites in Class A GPCRs. Our retrospective analysis reveals state- and bias ligand-dependent residue interactions along these communication pathways. Furthermore, focusing on the predicted binding site of allosteric modulator EC21a at cannabinoid receptors, the ACN framework was used to prioritize two residues for mutational analysis that may contribute to allosteric communication.

Benchmarking Docking Protocols for GPCR Allosteric Modulators

GaMD ensemble docking improved early AM enrichment across all four targets under at least one program, and the Boltz-2 deep-learning program showed minimal sensitivity to GaMD templates and underperformed conventional docking, suggesting its affinity predictions complement rather than replace physics- and empirical-based docking approaches for GPCR AM screening.

Structure-based elucidation of thiazine inhibitors targeting estrogen receptors alpha: pharmacophore modeling, virtual screening, molecular docking, MD, and DFT approach.

This research developed a promising lead molecule (HIT1) as a therapeutic approach for ER-positive breast cancer through pharmacophore model-based drug design of thiazine derivative targeting ERα via pharmacophore model-based drug design.

Structure-based discovery of novel ROCK2 inhibitors using integrated virtual screening, molecular dynamics, metadynamics and absolute binding free energy calculations

Computer-aided drug design of quinoxaline based selective oestrogen receptor α modulators for breast cancer therapy: molecular docking, MD simulation, MM-PBSA and ADME/T analysis.

Oestrogen receptor alpha (ERα) is a driver of hormone-dependent breast cancer, yet current therapies are often hindered by drug resistance and adverse effects. In this study, we developed a structure-based virtual screening workflow to identify novel quinoxaline derivatives as computationally prioritized potential ERα modulators. The quinoxaline analogues obtained from PubChem are screened through validated docking model of ERα. The top-ranked candidates were further refined by MD simulations in GROMACS for 300 ns to assess complex stability and interaction persistence, followed by MM-PBSA binding free energy calculations to quantify binding energetics. The prioritized quinoxaline hits exhibited more favourable predicted docking scores than reference ligand, tamoxifen, while MD trajectory analyses indicated stable complex formation with sustained predicted interactions involving key ERα binding site residues. MM-PBSA highlights LIG3 as the most promising lead with a favourable energy (ΔG = -34.15 kcal/mol). Complementary ADMET profiling predicted favourable pharmacokinetic behaviour and low toxicity for the prioritized compounds. This integrated in silico strategy demonstrates that quinoxaline scaffolds, specifically LIG3, represent promising computationally prioritized templates for the further development and experimental evaluation of ERα targeted ligands. These findings provide a reliable computational framework for the prioritization and structural optimization of next-generation anti-breast cancer agents.