AIMC Topic: Ubiquitin-Protein Ligases

Clear Filters Showing 11 to 20 of 25 articles

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) ...

DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs.

Nature communications
The rational design of PROTACs is difficult due to their obscure structure-activity relationship. This study introduces a deep neural network model - DeepPROTACs to help design potent PROTACs molecules. It can predict the degradation capacity of a pr...

Systematic prediction of degrons and E3 ubiquitin ligase binding via deep learning.

BMC biology
BACKGROUND: Degrons are short linear motifs, bound by E3 ubiquitin ligase to target protein substrates to be degraded by the ubiquitin-proteasome system. Mutations leading to deregulation of degron functionality disrupt control of protein abundance d...

A representation and deep learning model for annotating ubiquitylation sentences stating E3 ligase - substrate interaction.

BMC bioinformatics
BACKGROUND: Ubiquitylation is an important post-translational modification of proteins that not only plays a central role in cellular coding, but is also closely associated with the development of a variety of diseases. The specific selection of subs...

Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients.

Nature cancer
Cell-line screens create expansive datasets for learning predictive markers of drug response, but these models do not readily translate to the clinic with its diverse contexts and limited data. In the present study, we apply a recently developed tech...

DeepciRGO: functional prediction of circular RNAs through hierarchical deep neural networks using heterogeneous network features.

BMC bioinformatics
BACKGROUND: Circular RNAs (circRNAs) are special noncoding RNA molecules with closed loop structures. Compared with the traditional linear RNA, circRNA is more stable and not easily degraded. Many studies have shown that circRNAs are involved in the ...

Differentiating molecular etiologies of Angelman syndrome through facial phenotyping using deep learning.

American journal of medical genetics. Part A
Angelman syndrome (AS) is caused by several genetic mechanisms that impair the expression of maternally-inherited UBE3A through deletions, paternal uniparental disomy (UPD), UBE3A pathogenic variants, or imprinting defects. Current methods of differe...

Analysis of differentially expressed genes in rheumatoid arthritis and osteoarthritis by integrated microarray analysis.

Journal of cellular biochemistry
BACKGROUND: Rheumatoid arthritis (RA) and osteoarthritis (OA) were two major types of joint diseases. This study aimed to explore the mechanism underlying OA and RA and analyze their difference by integrated analysis of multiple gene expression data ...