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J Chem Inf Model. 2017 Jun 26;57(6):1453-1460. doi: 10.1021/acs.jcim.7b00163. Epub 2017 May 30.

Statistical Analysis and Prediction of Covalent Ligand Targeted Cysteine Residues.

Zhang W1, Pei J2, Lai L1,2,3.

Author information

1
Peking-Tsinghua Center for Life Sciences, AAIS, Peking University , Beijing 100871, P.R. China.
2
Center for Quantitative Biology, AAIS, Peking University , Beijing 100871, P.R. China.
3
BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University , Beijing 100871, P.R. China.

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.

PMID:
28510428
DOI:
10.1021/acs.jcim.7b00163
[Indexed for MEDLINE]

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