Prediction of Substrates for Glutathione Transferases by Covalent Docking

Guang Qiang Dong, Sara Calhoun, Hao Fan, Chakrapani Kalyanaraman, Megan C. Branch, Susan T. Mashiyama, Nir London, Matthew P. Jacobson, Patricia C. Babbitt, Brian K. Shoichet, Richard N. Armstrong, Andrej Sali

Research output: Contribution to journalArticlepeer-review


Enzymes in the glutathione transferase (GST) superfamily catalyze the conjugation of glutathione (GSH) to electrophilic substrates. As a consequence they are involved in a number of key biological processes, including protection of cells against chemical damage, steroid and prostaglandin biosynthesis, tyrosine catabolism, and cell apoptosis. Although virtual screening has been used widely to discover substrates by docking potential noncovalent ligands into active site clefts of enzymes, docking has been rarely constrained by a covalent bond between the enzyme and ligand. In this study, we investigate the accuracy of docking poses and substrate discovery in the GST superfamily, by docking 6738 potential ligands from the KEGG and MetaCyc compound libraries into 14 representative GST enzymes with known structures and substrates using the PLOP program [Jacobson et al. Proteins 2004, SS, 351]. For X-ray structures as receptors, one of the top 3 ranked models is within 3 angstrom all-atom root mean square deviation (RMSD) of the native complex in 11 of the 14 cases; the enrichment LogAUC value is better than random in all cases, and better than 25 in 7 of 11 cases. For comparative models as receptors, near-native ligand-enzyme configurations are often sampled but difficult to rank highly. For models based on templates with the highest sequence identity, the enrichment LogAUC is better than 25 in 5 of 11 cases, not significantly different from the crystal structures. In conclusion, we show that covalent docking can be a useful tool for substrate discovery and point out specific challenges for future method improvement.
Original languageEnglish
Pages (from-to)1687-1699
Number of pages13
JournalJournal of Chemical Information and Modeling
Issue number6
StatePublished - 6 May 2014
Externally publishedYes


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