Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation

Authors

Wubing Zhang
Shourya S Roy Burman
Jiaye Chen
Katherine A Donovan
Yang Cao
Chelsea Shu
Boning Zhang
Zexian Zeng
Shengqing Gu
Yi Zhang
Dian Li
Eric S Fischer
Collin Tokheim
X Shirley Liu

Publication Date

10-1-2022

Journal

Genomics, Proteomics & Bioinformatics

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.

Keywords

Targeted protein degradation, Degradability, Protein-intrinsic feature, Ubiquitination, Machine learning

Comments

Supplementary Materials

PMID: 36494034