Biomedical subjects

Randy J Zauhar

Publications and source records attributed to Randy J Zauhar.

5 recordsLinked to original sources

PubMed2006

Evidence for a strong selenium-aromatic interaction derived from crystallographic data and ab initio quantum chemical calculations.

Increasing attention is being paid to the role of selenium, both as an essential component required for the activity of many enzymes and in the context of selenium-based pharmaceutical agents. A wide range of therapeutics that include selenium are on the market and under development, such as antihypertensive, anticancerogenic, antiviral, and immunosuppressive agents. Computer-aided drug design (CADD) has proven to be an important tool for the development of new drugs. Many CADD techniques, including docking, molecular dynamics simulation, and other receptor-based approaches, require an accurate understanding of the nature of the intermolecular forces that act to stabilize protein-ligand complexes; moreover, a quantitative assessment of these interactions furthers our efforts to rationalize the drug design process. In this paper, we consider one class of interaction involving selenium, that between Se and aromatic rings. Prior work has shown that interactions between divalent sulfur and aromatic rings are observed much more frequently than would be expected on the basis of chance, both in protein structures and the crystal structures of organic compounds that include these moieties. Recent studies on the optimization of inhibitor-protein binding also suggest that sulfur-aromatic interactions are important in stabilizing these complexes and may be crucial focal point for CADD. Given that selenium and sulfur have similar chemistry, and that selenium is significantly more polarizable, we propose that Se-aromatic interactions may also play an important stabilizing role in the structure of folded proteins and in drug-protein complexes. We have tested this hypothesis against data from the Cambridge Crystallographic Database and ab initio quantum chemical calculations. We have found evidence that selenium does interact strongly with aromatic rings and may play a role analogous to sulfur in stabilizing protein folds. In addition, selenium should be considered along with sulfur in rational drug design strategies that seek to improve binding to target protein sites that include aromatic rings.

Crystallography, X-Ray↗

PubMed2006

Shape Signatures: speeding up computer aided drug discovery.

Identifying potential lead molecules is becoming a more automated process. We review Shape Signatures, a tool that is effective and easy to use compared with most computer aided drug design techniques. Laboratory researchers can apply this in silico technique cost-effectively without the need for specialized computer backgrounds. Identifying a potential lead molecule requires database screening, and this becomes rate-limiting once the database becomes too large. The use of Shape Signatures eliminates this concern and offers molecule screening rates that are in advance of any currently available method. Shape Signatures provides a conduit for researchers to conduct rapid identification of potential active molecules, and studies with this tool can be initiated with only one bioactive lead or receptor site.

Computer-Aided Design↗

PubMed2006

Molecular dynamics of a biophysical model for beta2-adrenergic and G protein-coupled receptor activation.

This study analyzes 16 molecular dynamic simulations of a biophysical model for beta(2)-adrenergic (B2AR) and G protein-coupled receptor (GPCR) activation. In this model, a highly conserved cysteine residue, C106 (C3.25 or CysIII:01), provides a free sulfhydryl or thiol group in an acid-base equilibrium between uncharged (RSH) and charged (RS(-)) states that functions as an electrostatic molecular switch for receptor activation. The transition of C106 in the B2AR between acid and base states significantly changes the helical/transmembrane (TM) domain interactions and the electrostatic interaction energy differences (DeltaDeltaE(EL)). The DeltaDeltaE(EL) changes correlate well with the experimentally observed ligand efficacies. The TM interaction energies display patterns compatible with those previously recognized as responsible for GPCR activation. Key differences between the agonist, epinephrine, and the antagonist, pindolol, are seen for the TM3 x 6, TM3 x 4, TM6 x 7 and TM1 x 7 interaction energies. Pindolol also produces a weaker DeltaDeltaE(EL) interaction and less TM interaction energy changes, which are important differences between the agonist and antagonist ligands. The D115E mutant with pindolol displays a greater DeltaDeltaE(EL) and TM interactions than for the wild-type B2AR with pindolol. This explains the higher activity of pindolol in the D115E mutant. The constitutively active D130A mutant displays TM interaction patterns similar to those for the activating ligands implying a common pattern for receptor activation. These findings support the broad concept of protean agonism and demonstrate the potential for allosteric modulation. They also demonstrate that this two-state model agrees with many previous experimental and theoretical observations of GPCRs.

Amino Acid Sequence↗

PubMed2006

Comparison of methods for the prediction of the metabolic sites for CYP3A4-mediated metabolic reactions.

Predictions of the metabolic sites for new chemical entities, synthesized or only virtual, are important in the early phase of drug discovery to guide chemistry efforts in the synthesis of new compounds with reduced metabolic liability. This information can now be obtained from in silico predictions, and therefore, a thorough and unbiased evaluation of the computational techniques available is needed. Several computational methods to predict the metabolic hot spots are emerging. In this study, metabolite identification using MetaSite and a docking methodology, GLUE, were compared. Moreover, the published CYP3A4 crystal structure and computed CYP3A4 homology models were compared for their usefulness in predicting metabolic sites. A total of 227 known CYP3A4 substrates reported to have one or more metabolites adding up to 325 metabolic pathways were analyzed. Distance-based fingerprints and four-point pharmacophore derived from GRID molecular interaction fields were used to characterize the substrate and protein in MetaSite and the docking methodology, respectively. The CYP3A4 crystal structure and homology model with the reactivity factor enabled achieved a similar prediction success (78%) using the MetaSite method. The docking method had a relatively lower prediction success (approximately 57% for the homology model), although it still may provide useful insights for interactions between ligand and protein, especially for uncommon reactions. The MetaSite methodology is automated, rapid, and has relatively accurate predictions compared with the docking methodology used in this study.

Binding Sites↗

PubMed2003

Shape signatures: a new approach to computer-aided ligand- and receptor-based drug design.

A unifying principle of rational drug design is the use of either shape similarity or complementarity to identify compounds expected to be active against a given target. Shape similarity is the underlying foundation of ligand-based methods, which seek compounds with structure similar to known actives, while shape complementarity is the basis of most receptor-based design, where the goal is to identify compounds complementary in shape to a given receptor. These approaches can be extended to include molecular descriptors in addition to shape, such as lipophilicity or electrostatic potential. Here we introduce a new technique, which we call shape signatures, for describing the shape of ligand molecules and of receptor sites. The method uses a technique akin to ray-tracing to explore the volume enclosed by a ligand molecule, or the volume exterior to the active site of a protein. Probability distributions are derived from the ray-trace, and can be based solely on the geometry of the reflecting ray, or may include joint dependence on properties, such as the molecular electrostatic potential, computed over the surface. Our shape signatures are just these probability distributions, stored as histograms. They converge rapidly with the length of the ray-trace, are independent of molecular orientation, and can be compared quickly using simple metrics. Shape signatures can be used to test for both shape similarity between compounds and for shape complementarity between compounds and receptors and thus can be applied to problems in both ligand- and receptor-based molecular design. We present results for comparisons between small molecules of biological interest and the NCI Database using shape signatures under two different metrics. Our results show that the method can reliably extract compounds of shape (and polarity) similar to the query molecules. We also present initial results for a receptor-based strategy using shape signatures, with application to the design of new inhibitors predicted to be active against HIV protease.

Binding Sites↗