degron-related

degron-related predicts degrons (short linear motifs) that serve as E3 ubiquitin ligase binding sites from protein sequences to identify sites that regulate protein abundance via the ubiquitin-proteasome system.

Key Features:

• BERT-based deep learning: Uses a BERT-based deep learning model to predict degrons directly from protein sequences.
• Degron definition: Targets short linear motifs that function as E3 ubiquitin ligase binding sites.
• Beyond motif-based approaches: Identifies degrons beyond previously characterized E3 motifs by capturing typical sequence properties associated with degrons.
• Proteome-wide prediction: Performs proteome-wide prediction of potential degron sites across protein substrates.
• E3 association via calculated motifs: Calculates motifs from a curated dataset of E3–substrate interactions to associate predicted degrons with specific E3 ligases.
• Regulatory network construction: Constructs regulatory networks that map protein degradation pathways by linking predicted degrons to E3 ligases.
• Experimental validation: Includes experimentally validated predictions such as a predicted SPOP-binding degron on CBX6 that mediates CBX6 degradation and interaction with SPOP.
• Cancer mutation analysis: Applies predicted degrons to analyze degron-related mutations in tumorigenesis using The Cancer Genome Atlas (TCGA) data.

Scientific Applications:

• Degron discovery: Identification of novel E3 binding sites not captured by known motif lists.
• Proteome annotation: Proteome-wide annotation of potential degrons to inform protein stability and turnover studies.
• Degradation pathway mapping: Construction of E3–substrate regulatory networks to study protein degradation mechanisms.
• Experimental follow-up: Prioritization of predicted degrons for experimental validation, exemplified by SPOP–CBX6 validation.
• Cancer genomics: Investigation of the role of degron-altering mutations in tumorigenesis using TCGA.

Methodology:

A BERT-based deep learning model predicts degrons from protein sequences; motifs are calculated from a curated dataset of E3–substrate interactions and used to associate predicted degrons with specific E3 ligases to build regulatory networks.