Caenorhabditis elegans has served as a model for the unfolded protein response of the endoplasmic reticulum (UPR-ER) and, separately, the oxidative stress response coordinated by SKN-1/Nrf. The oxidative protein folding in the ER generates reactive oxygen species, which is one reason why these systems are frequently studied together. Additionally, numerous stressors activate both pathways simultaneously. Although these molecular chaperones are critical to both processes, current research rarely considers them as an integrative concept, instead categorizing the interaction strictly within different pathway mechanisms. This review focuses on the chaperone systems themselves, including the HSP-4 and protein disulfide isomerases that are located in the ER, the HSP-70 family that resides in the cytoplasm and is controlled by HSF-1, and HSP-6 and HSP-60 that function in the mitochondria and are regulated by ATFS-1. Oxidative stress interacts with each compartment in a manner that is mechanistically distinct. This corresponds to direct redox chemistry in the ER, transcriptional coupling by SKN-1, and compartment-specific ROS-sensing evidence in the mitochondria and cytoplasm. Oxidative stress and chaperone induction converge through multiple independent pathways rather than a single sequential pathway, and this convergence deteriorates with age in a pathway- and tissue-specific manner, as shown by studies focusing on translational chaperone loss, particulate matter exposure, and proteasome-mediated degradation. Finally, we address some outstanding issues, such as the translational relevance to human diseases associated with ER stress and the functioning of redox-regulated chaperone mechanisms in C. elegans.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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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 sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.