Jul 2026· Journal of Chemical Information and Modeling· Vol 66, pp. 9089 - 9098· 0 citations· 25 references
Computer ScienceMedicine
TL;DR
A dynamics-aware molecular descriptor, LDR, is developed that can be a key factor in quantifying partial agonism arising from small-molecule ligand drift away from the primary orthosteric site and it is found that adding constraints on pose diversity and allowing receptor flexibility can improve LDR’s ability to distinguish full from partial agonists.
Abstract
The recent discovery that inverse agonists bind to a secondary orthosteric site in PPARγ reveals unexpected structural complexity in this nuclear receptor. To differentiate between full and partial agonists, we hypothesize that partial agonists exhibit dynamic positional behavior, moving among alternative binding sites at equilibrium when saturated, while full agonists bind directly and consistently to the primary orthosteric site. Using a two-state model derived from the Boltzmann distribution and molecular docking (MODO) sampling, we developed a computational metric called the Ligand Drift Rate (LDR) Descriptor to differentiate full agonists (Emax ≥ 90%) from partial agonists (Emax ≤ 45%) of PPARγ by simulating ligand positional instability within the primary orthosteric binding pocket. We combined molecular docking and hydrogen bond fluctuation analysis across 28 PPARγ-ligand complexes (18 full agonists and 10 partial agonists). MODO sampling was performed with several algorithms, each tested on five carefully selected receptor-binding pockets from 356 crystal complexes. Hydrogen bonds between ligands and key residues (His323, His449, Tyr473) were analyzed at various hydrogen-bond displacement (HBD) thresholds (6–12 Å). The LDR was calculated as the percentage of the top 20-scoring poses that lacked hydrogen bonds. We found that adding constraints on pose diversity and allowing receptor flexibility can improve LDR’s ability to distinguish full from partial agonists. Finally, MODO-sampling-based LDR calculations were conducted on 1079 ChEMBL ligands (393 full agonists and 686 partial agonists). In this validation set, LDR successfully differentiated partial from full agonists (t-test, p = 0.00043) using the genetic algorithm with the 3B1M-KRC binding pocket and a 10 Å hydrogen-bond threshold. In QSAR applications for drug discovery, we have developed a dynamics-aware molecular descriptor, LDR, that can be a key factor in quantifying partial agonism arising from small-molecule ligand drift away from the primary orthosteric site.
The third-generation antipsychotic cariprazine is a low-efficacy partial agonist of the dopamine D3 receptor (D3R). Here, we report the cryo–electron microscopy structure of cariprazine bound to D3R, establishing a framework for understanding ligand recognition in this receptor. We further determine structures of D3R in complex with a series of cariprazine derivatives spanning inverse agonists to high-efficacy partial agonists. Integration of structural data with pharmacological profiling and molecular dynamics simulations reveals how subtle chemical modifications translate into distinct functional outcomes. Determinants distinguishing agonism from inverse agonism are well defined, whereas differences among partial agonists arise from small positional shifts of the ligand within the orthosteric binding site. In contrast, the extended binding site primarily modulates ligand stability, affinity, and receptor selectivity. These findings establish a mechanistic link between bitopic ligand architecture and receptor activation, providing a “ligand-centric” view of D3R signaling. Leveraging these principles, we designed and validated cariprazine derivatives with enhanced D3/D2 selectivity and partial agonist activity. Together, this work provides a structural and pharmacological blueprint for the rational design of D3R-targeting ligands with tailored efficacy and therapeutic profiles.
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
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.
Zhi Dong, Long Cheng, Qingxin Shi et al.· International Journal of Bio...· 0 citations
These results provide the first atomistic model of SPM binding to GPR101 and establish an RBFE-guided framework for designing next-generation pro-resolving mediator analogs with enhanced pro-resolving effects and stability.
D. Hasselstrøm, Majd Awad, T. Hansen et al.· ACS Omega· 0 citations
This work proposes a method to compute accurate kinetics for general ligand-unbinding problems at modest computational expense and minimal fine tuning, building on the AIMMD path sampling framework and opting for modelling the committor with a single descriptor-free, equivariant graph neural network shared across all systems.
Drug resistance in epidermal growth factor receptor (EGFR)-mutant cancers commonly arises from kinase-domain substitutions that remodel the adenosine triphosphate binding pocket and reduce complementarity to orthosteric inhibitors, with the T790M gatekeeper mutation posing a major challenge. This study evaluated whether pre-occupying a proximal allosteric pocket with selected phytochemicals could bias mutant EGFR toward drug-compatible conformations and improve inhibitor binding. A two-phase computational workflow was employed: (i) molecular docking of gefitinib and erlotinib to wild-type and mutant EGFR; and (ii) allosteric pre-docking of phytochemicals followed by redocking of the ATP-site inhibitor. Top-ranked complexes were advanced to 200-nanosecond all-atom molecular dynamics simulations in explicit solvent and end-state binding free-energy estimation using Molecular Mechanics Generalized Born Surface Area (MM/GBSA). Docking predicted stronger binding to wild-type EGFR and reduced affinity for the T790M mutant, whereas co-binding produced compound-dependent improvements. Simulations suggested partial stabilization of the protein-ligand complexes, characterized by reduced root mean square deviation, damped hinge and αC-helix motions, reduced solvent exposure, and radii of gyration approaching wild-type behavior. Binding free energies improved from -12.6 to -17.6 kcal mol-1 (Genistein) and -19.58 kcal mol-1 (Tupichinols C) for gefitinib, and from -13.4 to -18.1 and -20.1 kcal mol-1, respectively, for erlotinib. Absorption, distribution, metabolism, excretion, and toxicity screening supported the developability of the leading candidates. This integrated framework provides structural, dynamic, and energetic criteria for prioritize cooperative allosteric-orthosteric co-binding chemotypes for experimental validation.
A. Sindi· Journal of Biomolecular Stru...· 0 citations
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