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

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

SUBMITTER: Zhang W 

PROVIDER: S-EPMC10025769 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature