Machine Learning Modeling of Protein-Intrinsic Features Predicts Tractability of Targeted Protein Degradation
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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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Cites
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Bai, N., Riching, K. M., Makaju, A., Wu, H., Acker, T. M., Ou, S.-C., Zhang, Y., Shen, X., Bulloch, D. N., Rui, H., et al. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.101653 · Cited by 77 · see citing works
Self-citation
- Authors
- Zhang, W., Roy Burman, S. S., Chen, J., Donovan, K. A., Cao, Y., Shu, C., Zhang, B., Zeng, Z., Gu, S., Zhang, Y., Li, D., Fischer, E. S., Tokheim, C., & Shirley Liu, X.
- Affiliations
- Harvard University (US)
- Department of Cancer Biology, Dana-Farber Cancer Institute , Boston, MA, 02215 , USA
- Sichuan University (CN)
- Department of Data Science, Dana-Farber Cancer Institute , Boston, MA, 02215 , USA
- Corresponding author
- Fischer, Eric S.
- Tokheim, Collin
- ORCID
- Venue
- Genomics, Proteomics & Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-10-01
- Volume
- 20
- Issue
- 5
- Pages
- 882-898
- ISSN
- 1672-0229 (ISSN-L)
- 2210-3244
- PMC
- PMC10025769
- OpenAlex
- W4310760182
- Cited by
- 51
- References
- 16
- Citations by year
- 2026: 15 · 2025: 10 · 2024: 16 · 2023: 4 · 2022: 6
- FWCI
- 2.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Genomics Proteomics & Bioinformatics (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Digital Commons-TMC (Texas Medical Center) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Breast Cancer Research Foundation (BCRF-19-100)
- Mark Foundation for Cancer Research (19-001-ELA)
- National Institutes of Health (R01CA218278, R01CA214608)
- Damon Runyon Cancer Research Foundation (DRQ-04-20)
- Harvard Medical School
- Dana-Farber Cancer Institute
- Cancer Research Institute
- German Cancer Research Center (CRI 3442)
- National Institute of Health
- Astellas Pharma US
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- targeted protein degradation, kinases, undruggable proteins, cancer genes
- MeSH terms
- Machine Learning, Lysine, Proteome, Ubiquitination, Proteolysis
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