Evidence map›Paper›PMID 38797898›Full record

ArticleCurrent computer-aided drug design2025

Structural Insight into the Binding Pattern and Interaction Mechanism of Antagonist MCC950 and Agonist BMS986299 with NLRP3 by Molecular Dynamics Simulation.

Ruifeng Zhang, Xin Xiong, Zhenli Min

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Article in Current computer-aided drug design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 authors.

Ruifeng ZhangInstitute of Advanced Pharmaceutical Technology, Department of Pharmacy, College of Medicine, Wuhan University of Science and Technology, Wuhan, 430081, China.
Xin XiongInstitute of Advanced Pharmaceutical Technology, Department of Pharmacy, College of Medicine, Wuhan University of Science and Technology, Wuhan, 430081, China.
Zhenli MinInstitute of Advanced Pharmaceutical Technology, Department of Pharmacy, College of Medicine, Wuhan University of Science and Technology, Wuhan, 430081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe NLRP3 inflammasome mediates a range of inflammatory responses that are associated with an increasing number of pathological mechanisms. Over-activation of NLRP3 can exacerbate many diseases. However, NLRP3 antagonists have significant therapeutic potential. Moreover, NLRP3 plays an important role in limiting the growth and spread of some tumors, and NLRP3 agonists also have clinical value. MCC950 and BMS986299 are an antagonist and agonist of NLRP3, respectively. In light of the important clinical applications of NLRP3, especially for NLRP3 inhibitors, a computational method was used to investigate the interaction modes of MCC950 and BMS986299 with NLRP3 in order to design and develop more potent NLRP3 regulators.

methodsIn this study, the conformational behaviors of NLRP3 bound to the antagonist MCC950 in an inactive state and the agonist BMS986299 in an active state were investigated using 200 ns equilibrium all-atom molecular dynamics (MD) simulations, and then the analyses of the MD trajectories (RMSD, Rg, RMSF, SASA, PCA, and DCCM) were carried out to explore the mechanism of the antagonist and agonist on NLRP3 in the two different states.

resultsThe RMSD, RMSF, Rg, SASA, and PCA analyses indicated that NLRP3 was more dispersive and less energetically stable in the active state than in the inactive state and that MCC950 significantly reduced the fluctuations of the interactive residues while BMS986299 did not. The antagonist MCC950 interacted with residues mainly in the NBD, HD1, WHD, and HD2 domains of NLRP3, whereas the agonist BMS986299 mainly in the NBD and WHD of NLRP3. Additionally, both compounds did not interact with residues located in the FISNA domain. The conformation of the FISNA domain appeared to change significantly when NLRP3 was translated from an inactive state to an active state.

conclusionThe antagonist may interact with residues mainly in the NBD, HD1, WHD, and HD2 domains, and the agonist may interact in the NBD and WHD domains. Our study provided new insights into the development of NLRP3 regulators.

Indexed as

FuransIndenesNLR Family, Pyrin Domain-Containing 3 ProteinSulfonamidesSulfonesCyclopropanesHumansInflammasomesMolecular Docking SimulationMolecular Dynamics SimulationProtein BindingCyclopropanesFuransIndenesInflammasomesN-(1,2,3,5,6,7-hexahydro-S-indacen-4-ylcarbamoyl)-4-(2-hydroxy-2-propanyl)-2-furansulfonamideNLR Family, Pyrin Domain-Containing 3 ProteinNLRP3 protein, humanSulfonamidesSulfonesBMS986299inflammationMCC950molecular dynamics simulationNLRP3NLRP3.

Identifiers

PMID38797898

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