Monday, September 26, 2022

CysDB: A Human Cysteine Database based on Experimental Quantitative Chemoproteomics [@Keribackus]

Boatner, L.; Palafox, M.; Schweppe, D.; Backus, K. 

ChemRxiv 2022

https://chemrxiv.org/engage/chemrxiv/article-details/632e3d710e3c6a3b31266100

Cysteine chemoproteomics studies provide proteome-wide portraits of the ligandability or potential ‘druggability’ of thousands of cysteine residues. Consequently, these studies are enabling resources for closing the druggability gap, namely achieving pharmacological manipulation of ~99% of the human proteome that remains untargeted by FDA approved small molecules. Recent interactive dataset repositories, such as OxiMouse and SLCABPP, have enabled users to interface more readily with cysteine chemoproteomics studies1,2. However, these databases remain limited to single studies and therefore do not provide a mechanism to perform cross-study analyses. Here we report CysDB as a curated community-wide repository of cysteine chemoproteomics data that incorporates high coverage data derived from nine studies generated by the Backus, Cravatt, Gygi, Wang, and Yang research groups. CysDB is a SQL relational database that is publicly available at https://backuslab.shinyapps.io/cysdb/ and features chemoproteomic measures of identification, hyperreactivity, and ligandability for 62,888 cysteines (24% of all cysteines the human proteome). The CysDB web application also includes annotations of functionality (UniProtKB/Swiss-Prot, Pfam, Panther), known druggability (FDA approved targets, DrugBank, ChEMBL), disease-relevance and genetic variation (ClinVar, Cancer Gene Census, Online Mendelian Inheritance in Man), and structural features (Protein Data Bank). Showcasing the utility of CysDB, here we report the discovery and enrichment of ligandable cysteines in undruggable classes of proteins, the observation that a subset of cysteines showed marked preference for specific classes of electrophiles (chloroacetamide vs acrylamide), and that ligandable cysteines are present in numerous undrugged disease-relevant proteins. Most importantly, we have designed CysDB for the incorporation of new datasets and features to support the continued growth of the druggable cysteineome.



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