Flexible fitting of PROTAC concentration-response curves with changepoint Gaussian Processes

Abstract

A proteolysis targeting chimera (PROTAC) is a new technology that marks proteins for degradation in a highly specific manner. During screening, PROTAC compounds are tested in concentration-response (CR) assays to determine their potency, and parameters such as the half-maximal degradation concentration (DC50) are estimated from the fitted CR curves. These parameters are used to rank compounds, with lower DC50 values indicating greater potency. However, PROTAC data often exhibit bi-phasic and poly-phasic relationships, making standard sigmoidal CR models inappropriate. A common solution includes manual omitting of points (the so called "masking" step) allowing standard models to be used on the reduced datasets. Due to its manual and subjective nature, masking becomes a costly and non-reproducible procedure. We, therefore, used a Bayesian changepoint Gaussian Processes model that can flexibly fit both non-sigmoidal and sigmoidal CR curves without user input. Parameters, such as the DC50, the maximum effect Dmax, and the point of departure (PoD) are estimated from the fitted curves. We then rank compounds based on one or more parameters, and propagate the parameter uncertainty into the rankings, enabling us to confidently state if one compound is better than another. Hence, we used a flexible and automated procedure for PROTAC screening experiments. By minimizing subjective decisions, our approach reduces time, cost, and ensures reproducibility of the compound ranking procedure. The code and data are provided on GitHub (https://github.com/elizavetase-menova/gp_concentration_response).