High accuracy in silico sulfotransferase models.

Area of Science:

• Biochemistry
• Pharmacology
• Computational Biology

Background:

• Cytosolic sulfotransferases (SULTs) are crucial for metabolizing endogenous compounds and xenobiotics via sulfation.
• Accurate in silico prediction of SULT activity has been a significant challenge in drug discovery.
• SULT1A1 and SULT2A1 are key enzymes in human Phase II metabolism, processing numerous drugs and metabolites.

Purpose of the Study:

• To develop and validate in silico models for predicting SULT1A1 and SULT2A1 substrates and inhibitors.
• To identify novel drug substrates and potential inhibitors using these computational models.
• To assess the accuracy and utility of in silico SULT prediction for drug discovery.

Main Methods:

• Incorporation of recent structural and dynamic insights into SULT binding and reactivity.
• Development of in silico models for SULT1A1 and SULT2A1.
• Testing models against a diverse set of 1,455 FDA-approved small molecule drugs.
• Experimental validation of predicted novel substrates.

Main Results:

• SULT1A1 models predicted 76 substrates, with 21 new ones confirmed experimentally.
• SULT2A1 models predicted 22 substrates, with 4 new ones confirmed experimentally.
• Models demonstrated 100% accuracy in substrate identification at specific thresholds, identifying 23 new drug substrates and predicting new drug inhibitor linkages.

Conclusions:

• Accurate in silico prediction of Phase II sulfonation is now achievable.
• These models significantly advance the discovery of metabolic potential for drug development.
• The findings enable more efficient identification of drug substrates and inhibitors for SULT enzymes.