Browsing by Author "Korczynska, Magdalena"

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    SQLite file for TopCysteineDB: A Cysteinome-wide Database Integrating Structural and Chemoproteomics Data for Cysteine Ligandability Prediction
    (N/A, 2025-04) Bonus, Michele; Greb, Julian; Majmudar, Jaimeen D.; Boehm, Markus; Korczynska, Magdalena; Nazemi, Azadeh; Mathiowetz, Alan M.; Gohlke, Holger
    Development of targeted covalent inhibitors and covalent ligand-first approaches have emerged as a powerful strategy in drug design, with cysteines being attractive targets due to their nucleophilicity and relative scarcity. While structural biology and chemoproteomics approaches have generated extensive data on cysteine ligandability, these complementary data types remain largely disconnected. Here, we present TopCysteineDB, a comprehensive resource integrating structural information from the PDB with chemoproteomics data from activity-based protein profiling experiments. Analysis of the complete PDB yielded 264,234 unique cysteines, while the proteomics dataset encompasses 41,898 detectable cysteines across the human proteome. Using TopCovPDB, an automated classification pipeline complemented by manual curation, we identified 787 covalent cysteines and systematically categorized other functional roles, including metal-binding, cofactor-binding, and disulfide bonds. Mapping residue-wise structural information to sequence space enabled cross-referencing between structural and proteomics data, creating a unified view of cysteine ligandability. For TopCySPAL, a machine learning model was developed, integrating structural features and proteomics data, achieving strong predictive performance (AUROC: 0.964, AUPRC: 0.914) and robust generalization to novel cases. TopCysteineDB and TopCySPAL are freely accessible through a webinterface, TopCysteineDBApp (https://topcysteinedb.hhu.de/), designed to facilitate exploration of cysteine sites across the human proteome. The interface provides an interactive visualization featuring a color-coded mapping of chemoproteomics data onto cysteine site structures and the highlighting of identified peptide sequences. It offers customizable dataset downloads and ligandability predictions for user-provided structures. This resource advances targeted covalent inhibitor design by providing integrated access to previously dispersed data types and enabling systematic analysis and prediction of cysteine ligandability.