Identification of candidates from high-throughput proteomic profiling of PROTAC-induced degradation

Advances in mass spectrometry-based high-throughput proteomics have made it possible to perform rapid quantitative proteomic screens. These screens can be particularly useful for small molecules in pharmacology. Their effects are sometimes difficult to predict and many variants can be designed and produced cheaply. A specific application is their use in targeted proteolysis by facilitating proximity between the E3 ligase complex and the target protein. This leads to ubiquitination and subsequent degradation of the target protein. These molecules are known as proteolysis targeting chimeras (PROTACs). To determine both degradation levels and translational effects, we employ multiplexed enhanced protein dynamics (mePROD). In this method, newly translated proteins are labelled with SILAC, allowing them to be distinguished from previously present and differently labelled proteins. Using a 96-well plate to screen multiple compounds allows us to rapidly generate proteome-wide data of both steady-state and newly translated protein levels for a large number of candidates. However, this large amount of data requires an effective processing pipeline to find relevant effects within the screening data. We have developed an automated pipeline for processing SILAC-TMT tagged proteomics data, including statistical analysis using linear mixed models. Changes in effects are visualized using complex plots, such as circus or hive plots, to show overall trends without losing much detail. However, these plots can quickly become complex and not every data point may be visible. To improve the process of discovering relevant effects in our datasets, we developed a scoring system to identify new candidates. This score includes several layers of information, such as fold changes for all proteins across samples, and determines direct and indirect effects of the PROTAC. The result is an easy-to-read indicator of efficacy that retains the nuances of the data. In addition, this system be applied not only to small molecule screens but also to other targeted degradation systems such as siRNAs.