A Comparative Molecular Field Analysis‐Based Approach to Prediction of Sulfotransferase Catalytic Specificity

From Steroid and Drug Metabolism to Glycobiology, Using Sulfotransferase Structures to Understand and Tailor Function

Sulfotransferases are ubiquitous enzymes that transfer a sulfo group from the universal cofactor donor 3'-phosphoadenosine 5'-phosphosulfate to a broad range of acceptor substrates. In humans, the cytosolic sulfotransferases are involved in the sulfation of endogenous compounds such as steroids, neurotransmitters, hormones, and bile acids as well as xenobiotics including drugs, toxins, and environmental chemicals. The Golgi associated membrane-bound sulfotransferases are involved in post-translational modification of macromolecules from glycosaminoglycans to proteins. The sulfation of small molecules can have profound biologic effects on the functionality of the acceptor, including activation, deactivation, or enhanced metabolism and elimination. Sulfation of macromolecules has been shown to regulate a number of physiologic and pathophysiological pathways by enhancing binding affinity to regulatory proteins or binding partners. Over the last 25 years, crystal structures of these enzymes have provided a wealth of information on the mechanisms of this process and the specificity of these enzymes. This review will focus on the general commonalities of the sulfotransferases, from enzyme structure to catalytic mechanism as well as providing examples into how structural information is being used to either design drugs that inhibit sulfotransferases or to modify the enzymes to improve drug synthesis. SIGNIFICANCE STATEMENT: This manuscript honors Dr. Masahiko Negishi's contribution to the understanding of sulfotransferase mechanism, specificity, and roles in biology by analyzing the crystal structures that have been solved over the last 25 years.

In silico mechanistic profiling to probe small molecule binding to sulfotransferases

Drug metabolizing enzymes play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs, for a rapid excretion, in some cases they render their substrates more toxic thereby inducing severe side effects and adverse drug reactions, or their inhibition can lead to drug–drug interactions. We focus on sulfotransferases (SULTs), a family of phase II metabolizing enzymes, acting on a large number of drugs and hormones and showing important structural flexibility. Here we report a novel in silico structure-based approach to probe ligand binding to SULTs. We explored the flexibility of SULTs by molecular dynamics (MD) simulations in order to identify the most suitable multiple receptor conformations for ligand binding prediction. Then, we employed structure-based docking-scoring approach to predict ligand binding and finally we combined the predicted interaction energies by using a QSAR methodology. The results showed that our protocol successfully prioritizes potent binders for the studied here SULT1 isoforms, and give new insights on specific molecular mechanisms for diverse ligands’ binding related to their binding sites plasticity. Our best QSAR models, introducing predicted protein-ligand interaction energy by using docking, showed accuracy of 67.28%, 78.00% and 75.46%, for the isoforms SULT1A1, SULT1A3 and SULT1E1, respectively. To the best of our knowledge our protocol is the first in silico structure-based approach consisting of a protein-ligand interaction analysis at atomic level that considers both ligand and enzyme flexibility, along with a QSAR approach, to identify small molecules that can interact with II phase dug metabolizing enzymes.

Accurate prediction of glucuronidation of structurally diverse phenolics by human UGT1A9 using combined experimental and in silico approaches

PURPOSE

Catalytic selectivity of human UGT1A9, an important membrane-bound enzyme catalyzing glucuronidation of xenobiotics, was determined experimentally using 145 phenolics and analyzed by 3D-QSAR methods.

METHODS

Catalytic efficiency of UGT1A9 was determined by kinetic profiling. Quantitative structure activity relationships were analyzed using CoMFA and CoMSIA techniques. Molecular alignment of substrate structures was made by superimposing the glucuronidation site and its adjacent aromatic ring to achieve maximal steric overlap. For a substrate with multiple active glucuronidation sites, each site was considered a separate substrate.

RESULTS

3D-QSAR analyses produced statistically reliable models with good predictive power (CoMFA: q2 = 0.548, r2 = 0.949, r pred 2 = 0.775; CoMSIA: q2 = 0.579, r2 = 0.876, r pred 2 = 0.700). Contour coefficient maps were applied to elucidate structural features among substrates that are responsible for selectivity differences. Contour coefficient maps were overlaid in the catalytic pocket of a homology model of UGT1A9, enabling identification of the UGT1A9 catalytic pocket with a high degree of confidence.

CONCLUSION

CoMFA/CoMSIA models can predict substrate selectivity and in vitro clearance of UGT1A9. Our findings also provide a possible molecular basis for understanding UGT1A9 functions and substrate selectivity.

Structure-based methods for the prediction of drug metabolism

There is a tantalising possibility that we may be able to predict the metabolism of a drug directly from its structure, thus obviating the requirement for animal tests in this area. There are a number of techniques that can be used to estimate a range of events associated with metabolism, and may allow us to achieve this aim. This paper considers the role of (quantitative) structure-activity relationships, and pharmacophore and homology modelling in the prediction of metabolism. Examples are also presented where such approaches have been formalised into expert systems. Clearly, many advances have been made in this area in recent years. Discussed herein is the importance of fully integrating the diverse systems and approaches available to fulfil the aspiration to predict metabolism directly from structure.