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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Updated: May 13, 2025

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TopCysteineDB: A Cysteinome-wide Database Integrating Structural and Chemoproteomics Data for Cysteine Ligandability

Michele Bonus1, Julian Greb1, Jaimeen D Majmudar2

  • 1Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf 40225 Düsseldorf, Germany.

Journal of Molecular Biology
|May 10, 2025
PubMed
Summary

TopCysteineDB integrates structural and chemoproteomics data to map cysteine ligandability. This resource aids in designing targeted covalent inhibitors by providing a unified view and predictive models for cysteine sites.

Keywords:
chemoproteomicscovalentcysteinedatabaseligandabilitymachine learningreactivity

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Area of Science:

  • Biochemistry
  • Structural Biology
  • Drug Discovery

Background:

  • Targeted covalent inhibitors are crucial in drug design, with cysteines as key targets due to their reactivity.
  • Existing structural and chemoproteomics data on cysteine ligandability are often disconnected, hindering comprehensive analysis.
  • A unified resource is needed to integrate these diverse data types for improved drug development.

Purpose of the Study:

  • To develop TopCysteineDB, a comprehensive database integrating Protein Data Bank (PDB) structural information with activity-based protein profiling (ABPP) chemoproteomics data.
  • To systematically identify and categorize functional roles of cysteines, including covalent modification, metal-binding, cofactor-binding, and disulfide bonds.
  • To create TopCySPAL, a machine learning model for predicting cysteine ligandability by integrating structural and proteomics data.

Main Methods:

  • Collected and analyzed structural data for 264,234 unique cysteines from the PDB.
  • Integrated chemoproteomics data for 41,898 detectable cysteines across the human proteome.
  • Developed an automated classification pipeline (TopCovPDB) with manual curation to identify and categorize cysteine functions.
  • Trained a machine learning model (TopCySPAL) using integrated structural features and proteomics data.

Main Results:

  • Identified 787 covalent cysteines and categorized other functional roles.
  • Established cross-referencing between structural and proteomics data, creating a unified view of cysteine ligandability.
  • TopCySPAL achieved high predictive performance (AUROC: 0.964, AUPRC: 0.914) for cysteine ligandability.
  • TopCysteineDB and TopCySPAL are accessible via a web interface (TopCysteineDBApp) with interactive visualizations and prediction tools.

Conclusions:

  • TopCysteineDB provides an unprecedented integrated view of cysteine ligandability by combining structural and chemoproteomics data.
  • The developed machine learning model, TopCySPAL, accurately predicts cysteine ligandability, advancing targeted covalent inhibitor design.
  • This resource facilitates systematic exploration and prediction of cysteine site reactivity, accelerating the development of novel therapeutics.