AIMC Topic: Ubiquitination

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Therapeutic potential of allosteric HECT E3 ligase inhibition.

Cell
Targeting ubiquitin E3 ligases is therapeutically attractive; however, the absence of an active-site pocket impedes computational approaches for identifying inhibitors. In a large, unbiased biochemical screen, we discover inhibitors that bind a crypt...

Identification of WDR74 and TNFRSF12A as biomarkers for early osteoarthritis using machine learning and immunohistochemistry.

Frontiers in immunology
BACKGROUND: Osteoarthritis (OA) is a chronic joint condition that causes pain, limited mobility, and reduced quality of life, posing a threat to healthy aging. Early detection is crucial for improving prognosis. Recent research has focused on the rol...

Identification of ubiquitination-related key biomarkers and immune infiltration in Crohn's disease by bioinformatics analysis and machine learning.

Scientific reports
Crohn's disease (CD) is a chronic inflammatory bowel disease with an unknown etiology. Ubiquitination plays a significant role in the pathogenesis of CD. This study aimed to explore the functional roles of ubiquitination-related genes in CD. Differen...

Enhancing Arabidopsis thaliana ubiquitination site prediction through knowledge distillation and natural language processing.

Methods (San Diego, Calif.)
Protein ubiquitination is a critical post-translational modification (PTM) involved in diverse biological processes and plays a pivotal role in regulating physiological mechanisms and disease states. Despite various efforts to develop ubiquitination ...

Machine learning-based approaches for ubiquitination site prediction in human proteins.

BMC bioinformatics
Protein ubiquitination is a critical post-translational modification (PTMs) involved in numerous cellular processes. Identifying ubiquitination sites (Ubi-sites) on proteins offers valuable insights into their function and regulatory mechanisms. Due ...

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence.

Journal of the American Chemical Society
Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) ...

Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation.

Genomics, proteomics & bioinformatics
Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell's endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by...

Multi-dimensional feature recognition model based on capsule network for ubiquitination site prediction.

PeerJ
Ubiquitination is an important post-translational modification of proteins that regulates many cellular activities. Traditional experimental methods for identification are costly and time-consuming, so many researchers have proposed computational met...

PrUb-EL: A hybrid framework based on deep learning for identifying ubiquitination sites in Arabidopsis thaliana using ensemble learning strategy.

Analytical biochemistry
Identification of ubiquitination sites is central to many biological experiments. Ubiquitination is a kind of post-translational protein modification (PTM). It is a key mechanism for increasing protein diversity and plays a vital role in regulating c...

An Ensemble Deep Learning based Predictor for Simultaneously Identifying Protein Ubiquitylation and SUMOylation Sites.

BMC bioinformatics
BACKGROUND: Several computational tools for predicting protein Ubiquitylation and SUMOylation sites have been proposed to study their regulatory roles in gene location, gene expression, and genome replication. However, existing methods generally rely...