Targeted protein degradation represents a novel but promising therapeutic modality. The interactions between E3 ubiquitin ligase and degradation signal (degron) determine the degradation specificity and maintain cellular homeostasis. Although the human genome encodes over 600 E3 ubiquitin ligases, only a small number of targeted degron instances are identified. Here, we build a user-friendly web service, named MetaDegron (Multimodal feature integrated Transformer for E3 degron) binding prediction. The built-in MetaDegron model shows excellent performance by integrating comprehensive featurization strategies and large protein language models. MetaDegron will serve the community for exploring biological mechanisms and implications of protein degradation, as well as drug discovery and design on degrons.

MetaDegron workflow

MetaDegron allows batch prediction of targeted degrons of 21 E3 ligases, providing visualization and functional annotation of multiple degron-related structural and physicochemical features. The built-in models of MetaDegron integrates comprehensive featurization strategies and large protein language model, showing great performance in both hybrid- and pure- deep leaning modes.

Updates
9/17/2023: Some bugs have been fixed for the MetaDegron server.

7/15/2023: MetaDegron 1.0 was released for prediction of E3 degron binding by deep learning.

3/18/2023: The protein language-based model was implemented and evaluated through 10 fold cross-validation and independent test.

10/05/2022: Comprehensive featurization strategies were implemented and evaluated for degron predicion.

8/17/2022: Dmultimodal features were evaluated by a XGBoost classifier.

6/21/2022: We calculated the enrichment of important PTMs around degrons and their structural properties.

4/13/2022: Degron motifs were collected from ELM database and literature.

Developers


Citation

♦ Zheng M, Lin S, Chen K, Hu R, Wang L, Zhao Z, Xu H. MetaDegron: multimodal feature-integrated protein language model for predicting E3 ligase targeted degrons. Brief Bioinform. 2024 Sep 23;25(6):bbae519. [PMID: 39431517]

♦ Xu H, Hu R, Zhao Z. DegronMD: Leveraging Evolutionary and Structural Features for Deciphering Protein-Targeted Degradation, Mutations, and Drug Response to Degrons. Mol Biol Evol. 2023;40(12):msad253. [PMID: 37992195]

* MetaDegron is free and open to all users and there is no login requirement.