Comparative molecular field analysis of substrates for an aryl sulfotransferase based on catalytic mechanism and protein homology modeling

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.

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.

Reengineering the pharmaceutical industry by crash-testing molecules

The recent decline in drug approvals and the increase in late-stage failures indicate that the ability to generate and screen large numbers of molecules has not improved the drug pipeline. Perhaps the pharmaceutical industry should follow the example of the automotive industry and agree upon a shared modeling language with vendors and academics to enable integration of predictive computational tools across the industry. This will then enable the virtual 'crash-testing' of drugs before synthesis, biological testing and, most importantly, clinical trials. This represents an ambitiously progressive approach using the models for simulating every stage of the drug discovery and development process. Combining the relevant computational algorithms into a grand unified model would enable prioritization of the best ideas before pursuing a discovery program, selecting a target or synthesizing a molecule. The successful application of these virtual crash-testing principles by any of its current proponents could revitalize the pharmaceutical industry so that failure is avoided.

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

Understanding the catalytic function and substrate specificity of cytosolic sulfotransferases (SULTs) involved in drug metabolism is essential for predicting the metabolic outcomes of many xenobiotics. Although multiple isoforms of cytosolic SULTs have been identified and characterized in humans and other species, relatively little is known about the specific molecular interactions that govern their selectivity for substrates. The use of three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques has emerged as a powerful tool for understanding the relationships among protein structure, catalytic function, and substrate specificity. We have found that a specific adaptation of a ligand-based 3D-QSAR method, comparative molecular field analysis (CoMFA), is particularly useful for prediction of the catalytic efficiencies of SULTs. This approach has been used to study the function of a prototypical rat hepatic phenol SULT and has now been extended to a member of the hydroxysteroid SULT family. Key aspects of this methodology incorporate strategies for finding the most meaningful bioactive conformation with respect to the protein structure, use of a model of an enzyme-substrate complex incorporating the mechanism of sulfuryl transfer, and the utilization of log(k(cat)/K(m)) as the parameter for correlation analysis. The success of this approach with members of two different families of cytosolic SULTs suggests that it may be of more general use in the study of other SULTs.

Sub- and Supercritical Chiral Separation of Racemic Compounds on Columns with Stationary Phases Having Different Functional Groups

Separation of the enantiomers of each of three different racemates, neutral rac-alpha-tetralol, acidic rac-2-phenylpropionic acid, and basic rac-1-phenylethylamine, using subcritical and supercritical fluid chromatography with two different chiral stationary phases, heptakis(2,3,6-tri-O-methyl)-beta-cyclodextrin (Sumichiral OA-7500 column) and tris(3,5-dimethylphenylcarbamate) of amylose (Chiralpak AD-H column), was compared. The elution order of the enantiomers of the three racemates was determined, and the effects of the type of alcohol modifier, column oven temperature, mobile phase composition, flow rate, and pressure were examined. The most appropriate column oven temperature depended on both the type of alcohol modifier and the compound analyzed. Lower alcohol content improved the peak separation of both rac-alpha-tetralol on the Sumichiral OA-7500 column and rac-1-phenylethylamine on the Chiralpak AD-H column, while the same phenomenon was not observed with either rac-alpha-tetralol or rac-2-phenylpropionic acid on the Chiralpak AD-H column. Decreasing outlet pressure improved the peak separation obtained with rac-2-phenylpropionic acid, but had little effect on either rac-alpha-tetralol or rac-1-phenylethylamine.

Analysis of the activating mutations within the activation loop of leukemia targets Flt-3 and c-Kit based on protein homology modeling

Molecular modeling provides a mechanistic hypothesis at the molecular level for the constitutive activation recently observed and reported for tyrosine protein kinases Flt-3 and c-Kit. Three-dimensional homology models for the active and inactive forms of these two kinases were made. Comparison of these models at the molecular level reveals that mutations of specific residues located in the activation loop (D835X and 836-deletion in Flt-3; D816V in c-Kit) as well as a 6-base pair (6-bp) insertion at residue 840 in Flt-3 operate in a similar way. Each mutation tends to weaken the forces that maintain the activation-loop folded inwards. None of the mutations are found to particularly stabilize the active state directly. The reason why the equilibrium is shifted towards the gate-open conformation of the protein is because, at least in these models, the mutations are found to critically destabilize the inactive conformational state of the kinase.

Molecular Determinants of Steroid Inhibition for the Mouse Constitutive Androstane Receptor

The constitutive androstane receptor (CAR) regulates drug and steroid metabolism through binding to cytochrome P450 2B, 2C, and 3A gene enhancers. Uniquely among nuclear receptors, mouse CAR (mCAR) can be suppressed by androstenol and activated by structurally diverse drugs, pesticides, and environmental pollutants. To gain insight into presently ill-defined structural requirements of mCAR ligands, we employed a mCAR inhibition assay in mammalian HEK293 cells to create a QSAR model that could well predict the inhibition by three unknown steroids. Two novel mCAR inhibitors were thus identified. Yeast two-hybrid assays indicated that steroids inhibit mCAR primarily by promoting association of mCAR with the corepressor NCoR, with only minor contribution from other mechanisms. Analysis of chimeric and mutant mCAR constructs suggested that androstenol sensitivity is controlled by residues between amino acids 201-263 (helices 5-7) and it does not depend on the residue 350 within helix 12, as previously suggested.

CoMFA Modeling of Enzyme Kinetics: Km Values for Sulfation of Diverse Phenolic Substrates by Human Catecholamine Sulfotransferase SULT1A3

Three-dimensional QSAR models were developed for predicting kinetic Michaelis constant (K(m)) values for phenolic substrates of human catecholamine sulfating sulfotransferase (SULT1A3). The K(m) values were correlated to the steric and electronic molecular fields of the substrates utilizing Comparative Molecular Field Analysis (CoMFA). The evaluated SULT1A3 substrate data set consisted of 95 different substituted phenols, catechols, catecholamines, steroids, and related structures for which the K(m) values were available. The data set was divided in three different subgroups in the initial analysis: (1). for the first CoMFA model substrates with only one reacting hydroxyl group were selected (n = 51), (2).the second model was build with structurally rigid substrates (n = 59), and (3). finally all substrates of the data set were included in the analysis (n = 95). Substrate molecules were aligned using the aromatic ring and the reacting hydroxyl group as a template. After the initial analysis different substrate alignment rules based on the existing knowledge of the SULT1A3 active site structure were evaluated. After this optimization a final CoMFA model was built including all 95 substrates of the data set. Cross-validated q(2) values (leave-one-out and leave-n-out) and coefficient contour maps were calculated for all derived CoMFA models. All four CoMFA models were statistically significant with q(2) values up to 0.624. These predictive QSAR models will provide us information about the factors that affect substrate binding at the active site of human catecholamine sulfotransferase SULT1A3.