Statistical Analysis and Prediction of Covalent Ligand Targeted Cysteine Residues

Journal of Chemical Information and Modeling
Weilin ZhangLuhua Lai

Abstract

Targeted covalent compounds or drugs have good potency as they can bind to a specific target for a long time with low doses. Most currently known covalent ligands were discovered by chance or by modifying existing noncovalent compounds to make them covalently attached to a nearby reactive residue. Computational methods for novel covalent ligand binding prediction are highly demanded. We performed statistical analysis on protein complexes with covalent ligands attached to cysteine residues. We found that covalent modified cysteine residues have unique features compared to those not attached to covalent ligands, including lower pKa, higher exposure, and higher ligand binding affinity. SVM models were built to predict cysteine residues suitable for covalent ligand design with prediction accuracy of 0.73. Given a protein structure, our method can be used to automatically detect druggable cysteine residues for covalent ligand design, which is especially useful for identifying novel binding sites for covalent allosteric ligand design.

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Citations

Nov 23, 2018·International Journal of Molecular Sciences·Ali MunawarSergei V Strelkov
Jan 24, 2020·Frontiers in Chemistry·Zarko GagicKatarina Nikolic
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May 15, 2020·Journal of Chemical Information and Modeling·Hao LiangLuhua Lai
Aug 18, 2018·Journal of Chemical Information and Modeling·Ernest Awoonor-Williams, Christopher N Rowley

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