Jul 2026· International Journal of Biological Macromolecules· pp.
153824
· 0 citations· 27 references
Medicine
TL;DR
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.
Abstract
This study integrates long-timescale all-atom molecular dynamics (MD) simulations with multidimensional drug discovery strategies to identify potential allosteric sites of the GIPR and screen for allosteric modulators. Based on the constructed GIPR-GIP complex, we performed conformational sampling and combined dynamic pocket detection algorithms, MDpocket and FTMove, to identify six characteristic cryptic pockets within the dynamic trajectories. Subsequently, using representative conformations as templates, a structure-based virtual screening of 1.6 million compounds from the ChemDiv database was conducted, yielding 30 candidate compounds. Surface plasmon resonance (SPR) experiments showed binding responses. The cAMP accumulation assay demonstrated that compound C30 could dose-dependently antagonize GIP-induced receptor activation, displaying negative allosteric modulator (NAM) activity. MD simulations revealed that C30 primarily restricts the outward movement of the transmembrane helix TM6. This study provides potential lead compounds for the design of small-molecule allosteric drugs targeting class B1 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.
S. Peter, G. Chalhoub, Peter J. McCormick et al.· Journal of Chemical Informat...· 0 citations
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.
T.D. Thompson, Yinglong Miao· bioRxiv· 0 citations
Current rational drug design relies predominantly on computational (CADD/AIDD) methods that model binding thermodynamics and static conformations of target proteins, primarily in their inactive states. However, the kinetic parameters that govern experimental efficacy—such as catalytic turnover and signaling potency—are determined by molecular interactions with transition states (TS), intermediate states (IS), and the entire continuum of conformations along the least free-energy activation pathway. The absence of this dynamic dimension has fundamentally limited the predictive power and success rate of conventional structure-based approaches. Here, we present a structural database that systematically maps the complete activation trajectories of pharmaceutically relevant targets, encompassing TS, IS, and all connecting conformational ensembles. This resource offers multiple strategic advantages for drug discovery: enabling rational targeting of previously “undruggable” proteins, facilitating biased agonism/antagonism design, revealing cryptic allosteric sites in inactive conformations, identifying novel transient pockets along the activation route, rationalizing the mechanisms of existing drugs, predicting mutational effects on activation barriers, and prospectively forecasting drug resistance and off-target liabilities. We demonstrate the utility of this database through representative case studies and provide implementation guidelines for integration into existing discovery pipelines. More detailed information can be found at our website: https://www.momedpamdb.com/en. Terminology The following terms are clarified in this document: Stable state (SS): In this document, this term refers exclusively to, and is synonymous with, the protein’s inactive state (IAS). Note that other states may also be stabilized into meta-stable states by certain means. Unstable state (US): This term encompasses all states other than SS, even if they appear computationally meta-stable on the free energy surface. Activated state (AS): The meta-stable working state of the protein. Transition state (TS): The state with the highest free energy along the least-energy pathway on the free energy surface that connects the inactive state to the activated state of the target protein. Intermediate state (IS): The state(s) located at a local minimum along the least-energy pathway, excluding SS and AS.
Allosteric modulation of Abl kinase via its myristoyl binding pocket is a promising strategy for drug discovery, either aiming at inhibition to treat Chronic Myeloid Leukemia, or activation, under investigation for breast cancer therapy. Although activators and inhibitors can be differentiated by the induced α-helix-I conformation, in silico classification has been proven challenging. Our study distinguishes these classes effectively by integrating multiple computational approaches that account for the conformational plasticity of the regulatory α-helix-I. By evaluating traditional molecular docking, co-folding, and molecular dynamics simulations with methodology benchmarking, we use these methods to uncover mechanistic principles of allosteric modulation. Docking highlighted potentially stabilizing C–F interactions with deep-pocket residues, while co-folding correctly predicted helix bending for inhibitors and outperformed docking in virtual screening. Molecular dynamics revealed that the α-helix-I samples multiple possible conformations and uncovered motions consistent with dynamic coupling between helix bending and long-range restraint of the activation loop, a nuanced mechanism that static models could not elucidate. This study provides a validated framework that combines efficient classification with mechanistic analysis by combining molecular docking, co-folding, and molecular dynamics. Our approach aids in identifying myristoyl pocket ligands, differentiating inhibitors from activators, and suggesting allosteric principles that govern their function, paving the way for more rational design of next-generation, function-specific Abl modulators.
P. T. T. F. Leite, P. O. Fernandes, D. M. Martins et al.· Journal of Chemical Informat...· 0 citations
Blockade of signaling through the angiotensin II type 1 receptor (AT1R), a prototypical G protein-coupled receptor (GPCR), by angiotensin receptor blockers (ARBs) is a major therapeutic approach to treating a wide variety of cardiovascular and renal diseases1. Like most GPCRs, the AT1R signals through two transducers, G proteins and β-arrestins2,3. Previous reports have described β-arrestin-biased peptide orthosteric agonists for the AT1R with potential therapeutic advantages over currently available unbiased ARBs4–6. Here we report the DNA- encoded library screening-guided isolation and pharmacological characterization of the first small molecule AT1R allosteric ligands. We use cryo-electron microscopy, double electron- electron resonance spectroscopy, molecular dynamics simulations, and targeted mutagenesis to determine their binding sites, binding modes and conformational mechanisms driving their unique and divergent modulatory effects on G protein and β-arrestin pathways. Our findings uncover new mechanisms for precisely controlling the dynamic behavior of the AT1R with implications for drug development targeting this pathophysiologically important receptor family.
Samuel Liu, Peng Xiao, M. Elgeti et al.· bioRxiv· 0 citations
Pathological mitochondrial fission driven by hyperactivation of the Drp1-MiD49 interaction contributes majorly to several cardiovascular diseases. Existing synthetic Drp1 inhibitors often lack selectivity between pathological and physiological mitochondrial fission, leading to poor bioavailability and off-target effects. Allosteric modulation therefore represents a more promising strategy to fine-tune Drp1 activity than complete inhibition and our study investigated whether natural antioxidants could function as selective allosteric modulators of Drp1 by targeting a characterized allosteric site at the dimer interface using molecular docking, long-timescale (1000 ns) molecular dynamics simulations and MMPBSA analysis. Docking analysis demonstrated favourable binding of the selected phytochemicals at the Drp1 allosteric interface, with Astaxanthin exhibiting the highest docking score, while Baicalin, Luteolin, and Resveratrol formed stable interactions with critical interface residues. Molecular dynamics simulations revealed that these compounds remained consistently associated with the allosteric site and promoted compact conformations of Drp1 characterized by reduced RMSD, radius of gyration (Rg) and solvent-accessible surface area (SASA) relative to apo-Drp1. RMSF, secondary structure and principal component analyses further demonstrated ligand-induced conformational transitions in functionally important regions including the G1/P-loop, G3/Switch II, G5/G-cap and the 80-loop and progressive restriction of conformational space for the Baicalin-, Luteolin-, and Resveratrol-bound complexes. MM-PBSA calculations identified Baicalin as the most energetically favourable complex, followed by Resveratrol. Independent replica simulations of the Baicalin-, Resveratrol-, and Mdivi-1-bound complexes further supported the reproducibility of the observed molecular dynamics trends. Collectively, these findings highlight the potential of Baicalin, Resveratrol, and Luteolin as promising Drp1 allosteric modulators, supporting future development of mitochondrial fission-targeted cardiac therapeutics.
B. Gangadharappa, Keerthana Ramji· Journal of Biomolecular Stru...· 0 citations
