A General in Vitro Assay for Studying Enzymatic Activities of the Ubiquitin System
Biochemistry(2020)SCI 3区
Univ Cambridge | Harvard Med Sch
Abstract
The ubiquitin (Ub) system regulates a wide range of cellular signaling pathways. Several hundred E1, E2 and E3 enzymes are together responsible for protein ubiquitination, thereby controlling cellular activities. Due to the numerous enzymes and processes involved, studies on ubiquitination activities have been challenging. We here report a novel FRET-based assay to study the in vitro kinetics of ubiquitination. FRET is established between binding of fluorophore-labeled Ub to eGFP-tagged ZnUBP, a domain that exclusively binds unconjugated Ub. We name this assay the Free Ub Sensor System (FUSS). Using Uba1, UbcH5 and CHIP as model E1, E2 and E3 enzymes, respectively, we demonstrate that ubiquitination results in decreasing FRET efficiency, from which reaction rates can be determined. Further treatment with USP21, a deubiquitinase, leads to increased FRET efficiency, confirming the reversibility of the assay. We subsequently use this assay to show that increasing the concentration of CHIP or UbcH5 but not Uba1 enhances ubiquitination rates, and develop a novel machine learning approach to model ubiquitination. The overall ubiquitination activity is also increased upon incubation with tau, a substrate of CHIP. Our data together demonstrate the versatile applications of a novel ubiquitination assay that does not require labeling of E1, E2, E3 or substrates, and is thus likely compatible with any E1-E2-E3 combinations.
MoreTranslated text
Key words
Deubiquitinating Enzymes
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
Sensitive Detection of Protein Ubiquitylation Using a Protein Fragment Complementation Assay
Journal of Cell Science 2020
被引用4
A Potential Mechanism for Targeting Aggregates With Proteasomes and Disaggregases in Liquid Droplets
FRONTIERS IN AGING NEUROSCIENCE 2022
被引用6
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话