Centromeres link chromosomes to spindle microtubules to direct their segregation during mitosis. The vertebrate centromere is built on two critical DNA-binding proteins, the histone H3-variant CENP-A and histone fold protein CENP-T. CENP-T is a potent scaffold for kinetochore assembly, such that concentrating it at ectopic sites is sufficient to build kinetochore-like structures. How cells prevent excess CENP-T from acting at non-centromere sites remains unclear. Here, using optical pooled screening to systematically define the requirements for CENP-A and CENP-T localization, we recover the established CENP-A deposition machinery and reveal a specific role for the proteasome in controlling CENP-T levels. We find that non-centromere CENP-T is degraded in a ubiquitin- and proteasome-dependent manner. Whereas centromere-incorporated CENP-T is stable, CENP-T that is not bound to centromeres is rapidly turned over, with the CENP-T histone fold region sufficient to confer proteasome sensitivity. Quiescent cells, which disassemble their centromeres, continue to express CENP-T but eliminate it through continuous degradation. Thus, whereas CENP-A is controlled primarily through regulated deposition, cells additionally control CENP-T by selectively degrading the unincorporated protein.
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.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
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Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
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...
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.