UCLA

Small molecule chemical probes are valuable tools for modulating protein function and have the potential to serve as leads for future medications. However, the pharmacological targeting of the human proteome with FDA-approved small molecules remains limited, addressing only 4% of all proteins. Furthermore, ~80% of proteins lack well-defined binding pockets for engagement by conventional small drug-like molecules. Mass spectrometry-based cysteine chemoproteomics has emerged as a promising strategy to bridge this druggability gap by mapping cysteine ‘druggability’ across the proteome. However, key challenges persist, including limited sampling (~13% of all cysteines), insufficient stratification of functional significance, and limited mechanistic insights into the labeling preferences of electrophilic compounds.

This work integrates experimental and computational approaches to address these challenges and improve the design and analysis of cysteine chemoproteomics datasets. First, the Mass Spectrometry-based Chemoproteomics Detected Amino Acids (MS-CpDAA) Analysis Suite was developed to streamline the deconvolution of covalent labeling sites from high-throughput chemoproteomics experiments and to quantify the performance of novel experimental methods for expanding cysteine coverage (Chapter 1). Using MS-CpDAA, we expanded cysteine coverage 5.5- fold compared to prior studies, identifying 34,225 covalently labeled cysteines. Building on this, CysDB, a publicly accessible SQL database with an interactive web interface, was established to aggregate experimental measures of cysteine reactivity alongside structural and functional annotations for over 24% of the cysteinome (Chapter 2). Designed to integrate diverse datasets and prioritize protein targets, CysDB provides a scalable platform for advancing the field. Designed to facilitate target prioritization, CysDB also provides a scalable platform for data integration and supports continued learning as the field evolves. Finally, CIAA (Cysteine reactivity towards IodoAcetamide Alkyne), a random forest model, was developed to predict cysteines with enhanced reactivity toward the small molecule iodoacetamide alkyne (IAA) (Chapter 3). CIAA offers a structure-based approach to investigating protein-ligand interactions, linking cysteine reactivity to druggability and functionality.

Together, this dissertation expands our understanding of the druggable cysteinome by providing computational resources and methodologies to target biologically significant proteins previously considered ‘undruggable’ and advancing approaches for covalent drug design. Furthermore, these approaches can be readily adapted to assess the druggability of other residues, such as lysines and tyrosines, across the human proteome. By addressing key challenges in cysteine chemoproteomics, these approaches contribute to a broader foundation for structure-based investigations of protein functionality and ligandability, offering valuable contributions to the fields of drug discovery and precision medicine.