bioRxivBiophysicsPreprint
Structural Basis for the Assembly of Amyloid Fibrils by the Master Cell-Signaling Regulator Human Receptor-Interacting Protein Kinase 1
Paula PolonioiD, Jorge Pedro López-AlonsoiD, Hanxing JiangiD, Fátima C. Escobedo‐González, Gustavo A. Titaux‐DelgadoiD, Iban Ubarretxena‐BelandiaiD, Miguel MompeániD
Abstract
Amyloid fibrils, typically associated with neurodegenerative diseases, also play critical roles as functional assemblies in biological processes. The RIP homotypic interaction motifs (RHIMs) in receptor-interacting protein kinases 1 and 3 (RIPK1 and RIPK3) are essential for necroptosis, orchestrating the formation of amyloid-like fibrils that assemble into necrosomes. These supramolecular complexes propagate cell death signals and activate effectors like MLKL. While the structures of human RIPK3 (hRIPK3) homomeric fibrils and RIPK1-RIPK3 heteromeric fibrils have been resolved, the atomic structure of human RIPK1 (hRIPK1) homomeric fibrils has remained elusive. Here, we present a high-resolution structure of hRIPK1 RHIM-mediated amyloid fibrils, determined using an integrative approach combining cryoprobe-detected solid-state nuclear magnetic resonance spectroscopy and cryo-electron microscopy. The fibrils adopt an N-shaped amyloid fold consisting of three {beta}-sheets stabilized by the conserved IQIG RHIM motif through hydrophobic interactions and hydrogen bonding. A key hydrogen bond between N545 and G542 closes the {beta}2-{beta}3 loop, resulting in denser side-chain packing compared to hRIPK3 homomeric fibrils. This structural feature likely contributes to the compact architecture of hRIPK1 fibrils, in contrast to the more relaxed S-shaped fold observed in hRIPK3. These findings provide structural insights into how hRIPK1 homomeric fibrils nucleate hRIPK3 recruitment and fibrillization during necroptosis, offering broader perspectives on the molecular principles governing RHIM-mediated amyloid assembly and functional amyloids.
- organism: human
- assay: structural biology
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
- Citations
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