High Accuracy in Silico Sulfotransferase Models*

Background: Human sulfotransferases (SULTs) regulate the bioactivities of hundreds of compounds in vivo. Results: The in silico models of SULTs developed here proved remarkably accurate in identifying SULT substrates in a 1,455-compound library containing all FDA-approved drugs. Conclusion: Highly accurate in silico SULT models are now available. Significance: The elision of mechanism and modeling can produce remarkably accurate in silico tools for the study of biology. Predicting enzymatic behavior in silico is an integral part of our efforts to understand biology. Hundreds of millions of compounds lie in targeted in silico libraries waiting for their metabolic potential to be discovered. In silico “enzymes” capable of accurately determining whether compounds can inhibit or react is often the missing piece in this endeavor. This problem has now been solved for the cytosolic sulfotransferases (SULTs). SULTs regulate the bioactivities of thousands of compounds—endogenous metabolites, drugs and other xenobiotics—by transferring the sulfuryl moiety (SO3) from 3′-phosphoadenosine 5′-phosphosulfate to the hydroxyls and primary amines of these acceptors. SULT1A1 and 2A1 catalyze the majority of sulfation that occurs during human Phase II metabolism. Here, recent insights into the structure and dynamics of SULT binding and reactivity are incorporated into in silico models of 1A1 and 2A1 that are used to identify substrates and inhibitors in a structurally diverse set of 1,455 high value compounds: the FDA-approved small molecule drugs. The SULT1A1 models predict 76 substrates. Of these, 53 were known substrates. Of the remaining 23, 21 were tested, and all were sulfated. The SULT2A1 models predict 22 substrates, 14 of which are known substrates. Of the remaining 8, 4 were tested, and all are substrates. The models proved to be 100% accurate in identifying substrates and made no false predictions at Kd thresholds of 100 μm. In total, 23 “new” drug substrates were identified, and new linkages to drug inhibitors are predicted. It now appears to be possible to accurately predict Phase II sulfonation in silico.