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Selective Activation of GPCRs: Molecular Dynamics Shows Siponimod Binds but Fails To Activate S1PR2, Unlike S1PR1

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NIAID Data Ecosystem2026-05-10 收录
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https://figshare.com/articles/dataset/Selective_Activation_of_GPCRs_Molecular_Dynamics_Shows_Siponimod_Binds_but_Fails_To_Activate_S1PR2_Unlike_S1PR1/30788898
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G protein-coupled receptors (GPCRs) are central to drug discovery, accounting for nearly 40% of approved pharmaceuticals due to their regulatory role in diverse physiological processes. Given the high structural similarity among homologues, achieving receptor selectivity while minimizing off-target effects remains a major challenge in designing drugs that target GPCRs. Sphingosine-1-phosphate receptors (S1PRs), comprising five subtypes, are therapeutically important GPCRs critical for immune and cardiovascular functions. Siponimod, an FDA-approved drug for multiple sclerosis, selectively modulates S1PR1 over S1PR2, unlike earlier S1PR modulators. However, the molecular basis for this selectivity is unclear, as cellular and biochemical assays provide limited insights. In this study, we used long-time-scale molecular dynamics simulations to investigate how S1P and Siponimod binding affects the structural dynamics of S1PR1 and S1PR2. Both ligands exhibited strong active site binding in both receptors. Crucially, while S1P and Siponimod induced similar activation-linked conformational changes in S1PR1, Siponimod failed to trigger these rearrangements in S1PR2. Specifically, Siponimod binding to S1PR2 led to altered side-chain dynamics of key TM7 residues (viz., Y7.37, F7.38, F7.39) and a drift of transmembrane helix 6 (TM6) toward orientations observed in the inactive state. These unique structural features differentiate Siponimod’s behavior from S1P and explain its inability to modulate S1PR2. Our findings elucidate molecular determinants of Siponimod’s selectivity toward S1PR1 and highlight these residues as potential differentiators for selective modulator design. This study demonstrates how structural and dynamic insights from atomistic simulations aid rational drug design for targets with high homology.
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2025-12-04
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