Systematic discovery and validation of degrader targets using deep proteomics screening combined with bioinformatics

Targeted protein degradation (TPD) using molecular glue degraders (MGD) represents a groundbreaking therapeutic strategy to eliminate disease-related proteins previously considered undruggable. However, despite its huge potential, systematic discovery of new molecular glues and their cellular degradation targets has been challenging. We introduce a deep proteomic screening followed by advanced biostatistics analysis to improve TPD drug discovery at all stages. This deep proteomic screening utilizes data-independent acquisition (DIA) based mass spectrometry to analyze compound libraries of potential molecular glues against cellular proteomes with very high throughput, coverage, and sensitivity. It can identify and quantify over 11,000 proteins per sample from cell lines treated with molecular glues, facilitating comprehensive proteomics-based drug and drug target discovery.

Our pipeline begins with highly automated sample preparation workflows, ensuring efficient and reproducible screening. Following this, single-shot DIA-based mass spectrometry is performed. The quality of each sample is monitored in parallel before the final analysis, with wide range of quality control measures. For visualization, statistical analysis, and data interpretation, we utilize internal R Shiny applications optimized for efficiency and scalability, enhancing our ability to manage and analyze very large screens effectively. Statistical workflows are tailored to maximize the yield of putative neosubstrates, through optimized filtering, normalization and batch correction procedures.

Likely direct degrader targets are identified using a sophisticated and comprehensive scoring system. Initially, targets are prioritized based on statistical test results. Then, the disease relevance of each target is assessed using available evidence of its causal relationship with the disease of interest. Further evaluation includes checking for the presence of structural motifs relevant to E3 ubiquitin ligase recruitment. Additionally, cheminformatics approaches are used to classify compounds based on structural similarity and/or presence of chemical substructures relevant to E3-MGD-neosubstrate complex formation and correlate this information with proteome changes.

To further demonstrate degrader-induced modifications, we are performing ubiqutinomics experiments and are reliably quantifying up to 50,000 ubiquitination sites. Such an approach offers rapid validation of cellular downregulations being due to E3 ligase-neosubstrate relationships, without the need for pharmacological intervention or genetic modification. As part of mechanistic validation, we are also performing interactomics experiments to detect degrader induced E3 ligase binding.