Many drugs produce clear clinical or cellular effects, but the molecular targets underlying these effects often remain unknown. Identifying these targets is essential for understanding mechanisms of action, anticipating side effects, and guiding drug optimization.
Direct biochemical validation methods can provide strong evidence for drug–target interactions, but they are often time-consuming, expensive, and difficult to apply at large scale. This creates a need for high-throughput approaches that can prioritize candidate target proteins for focused downstream validation. Limited Proteolysis Coupled to Mass Spectrometry, LiP-MS, is a proteome-wide approach for target identification that detects drug-induced changes in protein structure or accessibility through changes in protease-generated peptide patterns.
However, existing LiP-MS scoring strategies have mainly been tested on small sets of well-characterized, high-affinity ligands, leaving their performance on broader and more heterogeneous drug–target panels unclear. In this thesis, we evaluate LiP-MS-based target identification on a diverse panel of drug–target pairs and develop a computational framework for improving target prioritization.
The framework combines per-drug statistical evidence with cross-drug background patterns in order to recover known target proteins while reducing the impact of recurrent false-positive responders. We further incorporate biological knowledge about protein structure and function by testing whether high-scoring peptides fall within known active or binding domains. This additional layer helps distinguish biologically plausible candidates from proteins whose statistical signal is less likely to reflect a true drug–target interaction.
Together, this work provides a systematic analysis of factors affecting LiP-MS target identification and introduces a pipeline that integrates statistical scoring with protein domain information. By improving recovery of known targets and supporting more confident interpretation of ambiguous hits, the proposed framework can help guide future validation experiments toward the most relevant candidate target proteins.