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- W4310760182 endingPage "898" @default.
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- W4310760182 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 precision recall curve (AUPRC) of 0.759 and 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 278 cancer genes, that may be tractable to TPD drug development. Running title: Zhang W et al / Protein-intrinsic Features Predict Degradability" @default.
- W4310760182 created "2022-12-17" @default.
- W4310760182 creator A5001830101 @default.
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- W4310760182 creator A5066853006 @default.
- W4310760182 creator A5079301959 @default.
- W4310760182 creator A5081967923 @default.
- W4310760182 creator A5090099671 @default.
- W4310760182 date "2022-10-01" @default.
- W4310760182 modified "2023-10-14" @default.
- W4310760182 title "Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation" @default.
- W4310760182 cites W1967618958 @default.
- W4310760182 cites W1984754431 @default.
- W4310760182 cites W1991930087 @default.
- W4310760182 cites W1997952190 @default.
- W4310760182 cites W2001297798 @default.
- W4310760182 cites W2008708467 @default.
- W4310760182 cites W2015345028 @default.
- W4310760182 cites W2024543003 @default.
- W4310760182 cites W2043274355 @default.
- W4310760182 cites W2048611034 @default.
- W4310760182 cites W2048643046 @default.
- W4310760182 cites W2100547839 @default.
- W4310760182 cites W2110780888 @default.
- W4310760182 cites W2113904137 @default.
- W4310760182 cites W2128551987 @default.
- W4310760182 cites W2131736388 @default.
- W4310760182 cites W2134428146 @default.
- W4310760182 cites W2148043260 @default.
- W4310760182 cites W2150123812 @default.
- W4310760182 cites W2151683271 @default.
- W4310760182 cites W2171071516 @default.
- W4310760182 cites W2283442479 @default.
- W4310760182 cites W2344185956 @default.
- W4310760182 cites W2406250479 @default.
- W4310760182 cites W2568182039 @default.
- W4310760182 cites W2597078857 @default.
- W4310760182 cites W2611953519 @default.
- W4310760182 cites W2614443510 @default.
- W4310760182 cites W2733066269 @default.
- W4310760182 cites W2743800043 @default.
- W4310760182 cites W2767251210 @default.
- W4310760182 cites W2767426216 @default.
- W4310760182 cites W2767856718 @default.
- W4310760182 cites W2767891136 @default.
- W4310760182 cites W2774864460 @default.
- W4310760182 cites W2778809918 @default.
- W4310760182 cites W2785792383 @default.
- W4310760182 cites W2789810162 @default.
- W4310760182 cites W2804822363 @default.
- W4310760182 cites W2806543441 @default.
- W4310760182 cites W2806672448 @default.
- W4310760182 cites W2890974236 @default.
- W4310760182 cites W2896054460 @default.
- W4310760182 cites W2899147964 @default.
- W4310760182 cites W2900090807 @default.
- W4310760182 cites W2900569176 @default.
- W4310760182 cites W2905558559 @default.
- W4310760182 cites W2913446986 @default.
- W4310760182 cites W2914587883 @default.
- W4310760182 cites W2944245644 @default.
- W4310760182 cites W2944317821 @default.
- W4310760182 cites W2948220248 @default.
- W4310760182 cites W2972872742 @default.
- W4310760182 cites W2990435446 @default.
- W4310760182 cites W2992546470 @default.
- W4310760182 cites W3001475835 @default.
- W4310760182 cites W3010457103 @default.
- W4310760182 cites W3022943641 @default.
- W4310760182 cites W3031756382 @default.
- W4310760182 cites W3043399847 @default.
- W4310760182 cites W3044562385 @default.
- W4310760182 cites W3082280319 @default.
- W4310760182 cites W3088870471 @default.
- W4310760182 cites W3090754737 @default.
- W4310760182 cites W3091242795 @default.
- W4310760182 cites W3107242996 @default.
- W4310760182 cites W3110571152 @default.
- W4310760182 cites W3120197834 @default.
- W4310760182 cites W3120879514 @default.
- W4310760182 cites W3130894852 @default.
- W4310760182 cites W3136448722 @default.
- W4310760182 cites W3161432498 @default.
- W4310760182 cites W3165703330 @default.
- W4310760182 cites W3183782996 @default.
- W4310760182 cites W3186493235 @default.
- W4310760182 cites W3202924278 @default.
- W4310760182 cites W3205926919 @default.
- W4310760182 cites W3211270181 @default.
- W4310760182 cites W3211795435 @default.