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Statistical Analysis and Prediction of Covalent Ligand Targeted Cysteine Residues.

Research paper by Weilin W Zhang, Jianfeng J Pei, Luhua L Lai

Indexed on: 17 May '17Published on: 17 May '17Published in: Journal of Chemical Information and Modeling



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 non-covalent 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 Cys residues for covalent ligand design, which is especially useful for identifying novel binding sites for covalent allosteric regulating ligand design.