Golgins are widely described as long coiled-coil proteins that contribute to the structural organisation and trafficking functions of the Golgi apparatus. Although experimental structures have been determined for a limited number of golgin regions, atomic-level information on their extended coiled-coil segments remains scarce, and the oligomeric state, topology, and register of most predicted regions remain unestablished. Here, we characterise a predicted coiled-coil region of the yeast golgin Bug1 (BUG1cc) using structural, biophysical, and computational approaches. X-ray crystallography revealed a parallel, in-register dimeric coiled-coil containing ten heptad repeats and a predominantly hydrophobic core, with specific polar interactions contributing to dimer stabilisation. In solution, BUG1cc was dimeric under SEC-MALS conditions and remained highly α-helical across the pH and ionic strength conditions examined. CD measurements revealed pronounced scan-rate-dependent hysteresis, while DSC independently confirmed an asymmetry between heating and cooling transitions. Increasing protein concentration shifted both apparent transition temperatures while preserving thermal hysteresis, supporting chain association and conformational rearrangements in structural recovery. Structure-based simulations indicated that interface contacts and intra-chain helicity are thermodynamically coupled and melt as a single cooperative unit, and that the monomer released on dissociation is compact and only partially helical, so that reassociation proceeds through a coupled folding-binding mechanism whose rate-limiting step is conformational rather than bimolecular. Together, these results establish the molecular architecture of a predicted coiled-coil region of Bug1 and reveal a complex folding landscape in which oligomerisation, secondary-structure recovery, and kinetic barriers are tightly coupled.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
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