ArticleGenomics, proteomics & bioinformatics2022
Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation.
Article in Genomics, proteomics & bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- From Ternary Modeling to Predictive PROTAC Design: A Computational Perspective.Journal of chemical information and modeling · 2026Article
- Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi-scenario applications, and translational prospects.Smart molecules : open access · 2026Review
- Article
- Methods to Study the Molecular Mechanism and Drive the Design of Protein Degraders.Chemical reviews · 2026Review
- Bellerophon: An Automated Tool for PROTAC Decomposition.ACS medicinal chemistry letters · 2026Article
- From undruggable to degradable: A deep learning-enabled framework for precision orthopaedic protein degradation.Journal of orthopaedic translation · 2026Review
- Exploring Novel E3 Ligases and Neosubstrates for Molecular Glue Degraders and Therapeutic Applications in Cancer.Oncology research · 2026Review
- Interpretable PROTAC Degradation Prediction With Structure-Informed Deep Ternary Attention Framework.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Advancing PROTAC Discovery Through Artificial Intelligence: Opportunities, Challenges, and Future Directions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Designing the Proteome with Chemical Tools: Degrons and Beyond.Chembiochem : a European journal of chemical biology · 2025Review
- Accurate PROTAC-targeted degradation prediction with DegradeMaster.Bioinformatics (Oxford, England) · 2025Article
- Article
- A Kinetic Scout Approach Accelerates Targeted Protein Degrader Development.Angewandte Chemie (International ed. in English) · 2025Article
- Predicting PROTAC off-target effects via warhead involvement levels in drug-target interactions using graph attention neural networks.Computational and structural biotechnology journal · 2025Article
- Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development.Clinical and translational science · 2024Review
- Mechanism of degrader-targeted protein ubiquitinability.Science advances · 2024Article
- A Kinetic Scout Approach Accelerates Targeted Protein Degrader Development.bioRxiv : the preprint server for biology · 2024Article
- Lysineless HiBiT and NanoLuc Tagging Systems as Alternative Tools for Monitoring Targeted Protein Degradation.ACS medicinal chemistry letters · 2024Article
- P-NADs:Heliyon · 2024Article
- Targeting fusion oncoproteins in childhood cancers: challenges and future opportunities for developing therapeutics.Journal of the National Cancer Institute · 2024Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
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 TPD approaches, termed "degradability", is largely unknown. Here, we developed a machine learning model, model-free analysis of protein degradability (MAPD), to predict degradability from features intrinsic to protein targets. MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds [with an area under the precision-recall curve (AUPRC) of 0.759 and an area under the receiver operating characteristic curve (AUROC) of 0.775] and is likely generalizable to independent non-kinase proteins. We found five features with statistical significance to achieve optimal prediction, with ubiquitination potential being the most predictive. By structural modeling, we found that E2-accessible ubiquitination sites, but not lysine residues in general, are particularly associated with kinase degradability. Finally, we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins (including proteins encoded by 278 cancer genes) that may be tractable to TPD drug development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.