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However, the susceptibility of proteins for targeting by TPD approaches, termed \u201cdegradability\u201d, 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\u2013recall 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.<\/jats:p>","DOI":"10.1016\/j.gpb.2022.11.008","type":"journal-article","created":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T13:18:57Z","timestamp":1670332737000},"page":"882-898","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":34,"title":["Machine Learning Modeling of Protein-Intrinsic Features Predicts Tractability of Targeted Protein Degradation"],"prefix":"10.1093","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2233-299X","authenticated-orcid":false,"given":"Wubing","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Data Science, Dana-Farber Cancer Institute , Boston, MA, 02215 , USA"},{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, MA, 02115 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9274-9104","authenticated-orcid":false,"given":"Shourya S.","family":"Roy Burman","sequence":"additional","affiliation":[{"name":"Department of Cancer Biology, Dana-Farber Cancer Institute , Boston, MA, 02215 , USA"},{"name":"Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School , Boston, MA, 02115 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9654-1968","authenticated-orcid":false,"given":"Jiaye","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Harvard Medical School , Boston, MA, 02115 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8539-5106","authenticated-orcid":false,"given":"Katherine A.","family":"Donovan","sequence":"additional","affiliation":[{"name":"Department of Cancer Biology, Dana-Farber Cancer Institute , Boston, MA, 02215 , USA"},{"name":"Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School , Boston, MA, 02115 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1925-2123","authenticated-orcid":false,"given":"Yang","family":"Cao","sequence":"additional","affiliation":[{"name":"Center of Growth, Metabolism, and Aging, Key Laboratory of Bio-resource and Eco-environment, Ministry of Education, College of Life Sciences, Sichuan University , Chengdu, 610064 , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0463-1809","authenticated-orcid":false,"given":"Chelsea","family":"Shu","sequence":"additional","affiliation":[{"name":"Department of Cancer Biology, Dana-Farber Cancer Institute , Boston, MA, 02215 , USA"},{"name":"Research Scholar Initiative, Graduate School of Arts and Sciences, Harvard University , Cambridge, MA, 02138 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8516-137X","authenticated-orcid":false,"given":"Boning","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Data Science, Dana-Farber Cancer Institute , Boston, MA, 02215 , USA"},{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, MA, 02115 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3905-3244","authenticated-orcid":false,"given":"Zexian","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Data Science, Dana-Farber Cancer Institute , Boston, MA, 02215 , USA"},{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, MA, 02115 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4200-0864","authenticated-orcid":false,"given":"Shengqing","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Data Science, Dana-Farber Cancer Institute , Boston, MA, 02215 , USA"},{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, MA, 02115 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7453-6188","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Data Science, Dana-Farber Cancer Institute , Boston, MA, 02215 , USA"},{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, MA, 02115 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5374-7314","authenticated-orcid":false,"given":"Dian","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Data Science, Dana-Farber Cancer Institute , Boston, MA, 02215 , USA"},{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, MA, 02115 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7337-6306","authenticated-orcid":false,"given":"Eric S.","family":"Fischer","sequence":"additional","affiliation":[{"name":"Department of Cancer Biology, Dana-Farber Cancer Institute , Boston, MA, 02215 , USA"},{"name":"Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School , Boston, MA, 02115 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1395-5378","authenticated-orcid":false,"given":"Collin","family":"Tokheim","sequence":"additional","affiliation":[{"name":"Department of Data Science, Dana-Farber Cancer Institute , Boston, MA, 02215 , USA"},{"name":"Department of Biostatistics, Harvard T.H. 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