Adseverin is a Ca2+-dependent, actin severing protein that promotes fusion of osteoclast precursors in osteoclast formation. Currently it is not understood how actin severing activity is regulated in osteoclastogenesis. Mass spectrometry of adseverin immunoprecipitates from osteoclasts showed that adseverin associated with Serpin D1. Purified Serpin D1 interacted with adseverin in vitro in a Ca2+-dependent manner. Ca2+ increased actin severing by ~2-fold. Cells that differentiated into osteoclasts expressed an intracellular, truncated Serpin D1 isoform that lacked exons 1 and 2, and part of exon 3. This truncated Serpin D1 isoform was distinct from the secreted, full-length Serpin D1 protein that inhibits thrombin activity. The expression of the truncated Serpin D1 in RAW264.7 cells was further enhanced by TNF-α during RANKL-induced osteoclastogenesis. CRISPR/Cas9 targeting of exon 3 of Serpin D1 in RAW264.7 cells caused increases of the abundance of subcortical actin filaments. This treatment also altered the spatial distribution of adseverin, inhibited the expression of osteoclast-specific genes and reduced the formation of multinucleated osteoclasts by >90% in vitro. We conclude that a truncated, intracellular variant of Serpin D1 interacts with adseverin to promote actin severing and osteoclast formation.
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.