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Machine learning modeling of protein-intrinsic features predicts tractability of targeted protein degradation

Wubing Zhang, Shourya S. Roy Burman, Jiaye Chen, Katherine A. Donovan, Yang Cao, Boning Zhang, Zexian Zeng, Yi Zhang, Dian Li, Eric S. Fischer, Collin Tokheim, View ORCID ProfileX. Shirley Liu
doi: https://doi.org/10.1101/2021.09.27.462040
Wubing Zhang
1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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Shourya S. Roy Burman
3Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
4Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
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Jiaye Chen
5Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
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Katherine A. Donovan
3Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
4Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
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Yang Cao
6College of Life Sciences, Sichuan University, Chengdu, China
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Boning Zhang
1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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Zexian Zeng
1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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Yi Zhang
1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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Dian Li
1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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Eric S. Fischer
3Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
4Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
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  • For correspondence: xsliu@ds.dfci.harvard.edu
Collin Tokheim
1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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  • For correspondence: xsliu@ds.dfci.harvard.edu
X. Shirley Liu
1Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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  • ORCID record for X. Shirley Liu
  • For correspondence: xsliu@ds.dfci.harvard.edu
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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. Recent systematic studies to map the degradable kinome have shown differences in degradation between kinases with similar drug-target engagement, suggesting yet unknown factors influencing degradability. We therefore developed a machine learning model, MAPD (Model-based Analysis of Protein Degradability), to predict degradability from protein features that encompass post-translational modifications, protein stability, protein expression and protein-protein interactions. MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds (auPRC=0.759) 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.

Competing Interest Statement

X.S.L. is a cofounder, board member, SAB member, and consultant of GV20 Oncotherapy and its subsidiaries; stockholder of BMY, TMO, WBA, ABT, ABBV, and JNJ; and received research funding from Takeda, Sanofi, Bristol Myers Squibb, and Novartis. E.S.F. is a founder, science advisory board (SAB) member, and equity holder in Civetta Therapeutics, Jengu Therapeutics (board member), Neomorph Inc and an equity holder in C4 Therapeutics. E.S.F. is a consultant to Novartis, Sanofi, EcoR1 capital, Avilar, and Deerfield. The Fischer lab receives or has received research funding from Astellas, Novartis, Voronoi, Ajax, and Deerfield. K.A.D is a consultant to Kronos Bio. All the other authors declare no competing interests.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 29, 2021.
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Machine learning modeling of protein-intrinsic features predicts tractability of targeted protein degradation
Wubing Zhang, Shourya S. Roy Burman, Jiaye Chen, Katherine A. Donovan, Yang Cao, Boning Zhang, Zexian Zeng, Yi Zhang, Dian Li, Eric S. Fischer, Collin Tokheim, X. Shirley Liu
bioRxiv 2021.09.27.462040; doi: https://doi.org/10.1101/2021.09.27.462040
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Machine learning modeling of protein-intrinsic features predicts tractability of targeted protein degradation
Wubing Zhang, Shourya S. Roy Burman, Jiaye Chen, Katherine A. Donovan, Yang Cao, Boning Zhang, Zexian Zeng, Yi Zhang, Dian Li, Eric S. Fischer, Collin Tokheim, X. Shirley Liu
bioRxiv 2021.09.27.462040; doi: https://doi.org/10.1101/2021.09.27.462040

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