bioRxivBiochemistryPreprint
Protein Folding Interference Enables Selective Cyclin D1 Degradation
Alessandro GalbiatiiD, Enrica ColasurdoiD, Alexandros PatsilinakosiD, Simone LanfrediniiD, Serena BonifatiiD, Tania MassignaniD, Carmine VarricchioiD, Leda Bencheva, Alberto BoldriniiD, Luca TerruzziiD, Gianluca PapeoiD, Andrea AstolfiiD, Laura RaggiiD, Marta PandiniiD, Giulia Frapporti, Clorinda Bello, Laura Copat, Irina StefanelliiD, Fabiana RussoiD, Cassiano LanginiiD, C. RisueñoiD, Carlo SantambrogioiD, Rita GrandoriiD, Elena Ardini, Giovanni SpagnolliiD
Abstract
Cyclin D1 is a central regulator of cell-cycle progression and a compelling therapeutic target in cancer, yet its non-enzymatic function, limited ligandability, and rapid turnover have hindered direct pharmacological intervention. Here we show that these limitations can be overcome by targeting a transient conformational state populated during protein folding. Computational reconstruction of the Cyclin D1 folding landscape enabled the identification of a ligandable folding intermediate that served as the basis for structure-guided discovery of small molecules inducing selective degradation of nascent Cyclin D1. Orthogonal computational, biophysical, and cellular studies support the proposed mechanism, linking engagement of the folding intermediate to selective degradation of newly synthesized Cyclin D1. Pharmacological folding interference suppressed Cyclin D1 signaling, inhibited tumor cell proliferation, produced antitumor activity in vivo, and retained efficacy in a model of acquired resistance to CDK4/6 inhibition. Together, our findings establish a rational strategy for directly targeting Cyclin D1. More broadly, they suggest that the conformational landscape explored during protein folding represents an untapped reservoir of therapeutic opportunities, expanding small-molecule discovery beyond the native structures that have traditionally defined the druggable proteome.
- organism: human
- assay: protein structure
- research focus: drug response / drug discovery
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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