Target identification by chemical proteomics and bioinformatic investigations with small molecule ligands

Abstract

Understanding the protein targets and mechanisms of action of bioactive small molecules remains a central goal in chemical biology and drug discovery. Unbiased approaches, particularly chemical proteomics and systems-level bioinformatics, have emerged as powerful strategies to uncover the molecular interactions and biological consequences of small molecule engagement in cells. In this thesis, I apply these complementary methodologies—activity-based protein profiling (ABPP), photo-affinity labeling (PAL), and bioinformatic analyses—to investigate two distinct but mechanistically rich chemical spaces: opioid analgesics such as heroin and morphine, and cereblon (CRBN)-binding immunomodulatory drugs (IMiDs) that induce targeted protein degradation. Chapter 1 introduces the conceptual and methodological foundation for this work, beginning with a background on ABPP and PAL, highlighting their ability to turn small molecules into molecular probes that illuminate their target landscapes through quantitative mass spectrometry. The chapter also introduces gene ontology (GO) annotations and the database for annotation, visualization, and integrated discovery (DAVID) bioinformatics suite, which are essential for contextualizing proteomic hit lists. Further, I provide an overview of machine learning principles and illustrate how these computational tools can accelerate target identification and therapeutic discovery. The chapter concludes with two thematic deep dives: a historical and pharmacological overview of opioids and their receptors and the development of chemical probes to study them, and a survey of CRBN biology, including the evolution of IMiD-based molecular glues and the discovery of the endogenous CRBN degron—the cyclic imide degron—formed either spontaneously from protein damage or enzymatically via Protein-L-isoaspartate O-methyltransferase (PCMT1). Chapter 2 describes the design, synthesis, and characterization of novel chemical probes for morphine and heroin, including photo-click morphine (PCM-1, PCM-2) and the acyl-donating probe Di-Alkynyl-Acyl-Morphine (DAAM). These tools were validated for their ability to engage opioid receptors and induce G-protein signaling. Confocal imaging revealed receptor-independent localization of these probes to lysosomes across diverse cell lines. Chemoproteomic analysis uncovered voltage-dependent anion channel 1 (VDAC1) as a shared target across all probes and identified lysine 234 of solute carrier family 25 member 3 (SLC25A3) as a selective site of acylation by DAAM. This residue was later confirmed to be directly acetylated by heroin itself, representing one of the first demonstrations of small-molecule-mediated post-translational modification by heroin. These findings suggest potential mechanisms for the mitochondrial and metabolic dysfunctions associated with chronic heroin use. Chapter 3 focuses on the proteomic and computational reanalysis of the Broad Institute’s PRISM screen using the CRBN-dependent degrader DEG-35. By separating wild-type from mutant populations and refining statistical comparisons, I report biologically meaningful indicators of sensitivity, including intact p53 signaling, protein Mdm4 (MDM4) dependency, and alterations in the switch/sucrose non-fermentable (SWI/SNF) complex. I report the development of a multilayer perceptron (MLP) model trained on the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) viability data to predict DEG-35 sensitivity across the extended DepMap cell line repository. The model achieved strong performance (Area under the Receiver Operative Characteristic Curve (AUROC) = 0.98) and accurately identified sensitive cell lines, including SCCOHT-1, which is actively being experimentally validated. This work highlights the potential of integrating chemogenomics data with machine learning to stratify responders and uncover mechanistic biomarkers. Chapter 4 presents a novel inversion of traditional GO analysis using the DAVID tool: instead of applying GO terms post hoc to hit lists, I used it as a filtering method for hypothesis generation. Starting with all human proteins ending in C-terminal asparagine or glutamine—potential substrates for PCMT1-mediated cyclic imide formation—I performed annotation clustering and literature-driven prioritization. From this list, Hairy and Enhancer of Split 1 (HES1) emerged as a top candidate. In vitro assays confirmed that PCMT1 catalyzes the formation of the cyclic imide degron on the HES1 C-terminus, enabling CRBN binding. Full-length HES1 showed PCMT1-dependent CRBN engagement, and follow-up experiments in SH-SY5Y cells demonstrated that knockout of either PCMT1 or CRBN led to impaired neuronal differentiation, suggesting a role for this degradation pathway in developmental timing. These findings advance our understanding of CRBN’s endogenous substrates and open new directions for exploring its role in neurodevelopment. Together, this thesis demonstrates how integrative chemical biology—uniting chemical proteomics, computational modeling, and bioinformatics—can illuminate the complex interactions between small molecules and the proteome, uncover new biological mechanisms, and inspire new directions for therapeutic or biological discovery.Chemistry and Chemical Biolog