Biomedical subjects

Philip D Mosier

Publications and source records attributed to Philip D Mosier.

7 recordsLinked to original sources

PubMed2006

Differential helical orientations among related G protein-coupled receptors provide a novel mechanism for selectivity. Studies with salvinorin A and the kappa-opioid receptor.

Salvinorin A, the active component of the hallucinogenic sage Salvia divinorum, is an apparently selective and highly potent kappa-opioid receptor (KOR) agonist. Salvinorin A is unique among ligands for peptidergic G protein-coupled receptors in being nonnitrogenous and lipid-like in character. To examine the molecular basis for the subtype-selective binding of salvinorin A, we utilized an integrated approach using chimeric opioid receptors, site-directed mutagenesis, the substituted cysteine accessibility method, and molecular modeling and dynamics studies. We discovered that helix 2 is required for salvinorin A binding to KOR and that two residues (Val-108(2.53) and Val-118(2.63)) confer subtype selectivity. Intriguingly, molecular modeling studies predicted that these loci exhibit an indirect effect on salvinorin A binding, presumably through rotation of helix 2. Significantly, and in agreement with our in silico predictions, substituted cysteine accessibility method analysis of helix 2 comparing KOR and the delta-opioid receptor, which has negligible affinity for salvinorin A, revealed that residues known to be important for salvinorin A binding exhibit a differential pattern of water accessibility. These findings imply that differences in the helical orientation of helix 2 are critical for the selectivity of salvinorin A binding to KOR and provide a structurally novel basis for ligand selectivity.

Amino Acid Sequence↗

PubMed2006

Finding a needle in a haystack: development of a combinatorial virtual screening approach for identifying high specificity heparin/heparan sulfate sequence(s).

We describe a combinatorial virtual screening approach for predicting high specificity heparin/heparan sulfate sequences using the well-studied antithrombin-heparin interaction as a test case. Heparan sulfate hexasaccharides were simulated in the 'average backbone' conformation, wherein the inter-glycosidic bond angles were held constant at the mean of the known solution values, irrespective of their sequence. Molecular docking utilized GOLD with restrained inter-glycosidic torsions and intra-ring conformations, but flexible substituents at the 2-, 3-, and 6-positions and explicit incorporation of conformational variability of the iduronate residues. The approach reproduces the binding geometry of the sequence-specific heparin pentasaccharide to within 2.5 A. Screening of a combinatorial virtual library of 6,859 heparin hexasaccharides using a dual filter strategy, in which predicted antithrombin affinity was the first filter and self-consistency of docking was the second, resulted in only 10 sequences. Of these, nine were found to bind antithrombin in a manner identical to the natural pentasaccharide, while a novel hexasaccharide bound the inhibitor in a unique but dramatically different geometry and orientation. This work presents the first approach on combinatorial library screening for heparin/heparan sulfate GAGs to determine high specificity sequences and opens up huge opportunities to investigate numerous other physiologically relevant GAG-protein interactions.

Antithrombins↗

PubMed2005

Identification of the molecular mechanisms by which the diterpenoid salvinorin A binds to kappa-opioid receptors.

Salvinorin A is a naturally occurring hallucinogenic diterpenoid from the plant Salvia divinorumthat selectively and potently activates kappa-opioid receptors (KORs). Salvinorin A is unique in that it is the only known lipid-like molecule that selectively and potently activates a G-protein coupled receptor (GPCR), which has as its endogenous agonist a peptide; salvinorin A is also the only known non-nitrogenous opioid receptor agonist. In this paper, we identify key residues in KORs responsible for the high binding affinity and agonist efficacy of salvinorin A. Surprisingly, we discovered that salvinorin A was stabilized in the binding pocket by interactions with tyrosine residues in helix 7 (Tyr313 and Tyr320) and helix 2 (Tyr119). Intriguingly, activation of KORs by salvinorin A required interactions with the helix 7 tyrosines Tyr312, Tyr313, and Tyr320 and with Tyr139 in helix 3. In contrast, the prototypical nitrogenous KOR agonist U69593 and the endogenous peptidergic agonist dynorphin A (1-13) showed differential requirements for these three residues for binding and activation. We also employed a novel approach, whereby we examined the effects of cysteine-substitution mutagenesis on the binding of salvinorin A and an analogue with a free sulfhydryl group, 2-thiosalvinorin B. We discovered that residues predicted to be in close proximity, especially Tyr313, to the free thiol of 2-thiosalvinorin B when mutated to Cys showed enhanced affinity for 2-thiosalvinorin B. When these findings are taken together, they imply that the diterpenoid salvinorin A utilizes unique residues within a commonly shared binding pocket to selectively activate KORs.

Amino Acid Sequence↗

PubMed2003

Predicting the genotoxicity of thiophene derivatives from molecular structure.

We report several binary classification models that directly link the genetic toxicity of a series of 140 thiophene derivatives with information derived from the compounds' molecular structure. Genetic toxicity was measured using an SOS Chromotest. IMAX (maximal SOS induction factor) values were recorded for each of the 140 compounds both in the presence and in the absence of S9 rat liver homogenate. Compounds were classified as genotoxic if IMAX >or= 1.5 in either test or nongenotoxic if IMAX < 1.5 for both tests. The molecular structures were represented by numerical descriptors that encoded the topological, geometric, electronic, and polar surface area properties of the thiophene derivatives. The classification models used were linear discriminant analysis (LDA), k-nearest neighbor classification (k-NN), and the probabilistic neural network (PNN). These were used in conjunction with either a genetic algorithm or a generalized simulated annealing to find optimal subsets of descriptors for each classifier. The quality of the resulting models was determined by the number of misclassified compounds, with preference given to models that produced fewer false negative classifications. Model sizes ranged from seven descriptors for LDA to three descriptors for k-NN and PNN. Very good classification results were obtained with all three classifiers. Classification rates for the LDA, k-NN, and PNN models were 80, 85, and 85%, respectively, for the prediction set compounds. Additionally, a consensus model was generated that incorporated all three of the basic model types. This consensus model correctly predicted the genotoxicity of 95% of the prediction set compounds.

DNA Damage↗

PubMed2002

Discovery of imidazole glycerol phosphate dehydratase inhibitors through 3-D database searching.

Imidazole glycerol phosphate dehydratase (IGPD) has become an attractive target for herbicide discovery since it is present in plants and not in mammals. Currently no knowledge is available on the 3-D structure of the IGPD active site. Therefore, we used a pharmacophore model based on known inhibitors and 3-D database searches to identify new active compounds. In vitro testing of compounds from the database searches led to the identification of a class of pyrrole aldehydes as novel inhibitors of IGPD.

Aldehydes↗

PubMed2002

Prediction of peptide ion collision cross sections from topological molecular structure and amino acid parameters.

Quantitative structure-property relationships (QSPRs) have been developed to predict the ion mobility spectrometry (IMS) collision cross sections of singly protonated lysine-terminated peptides using information derived from topological molecular structure and various amino acid parameters. The primary amino acid sequence alone is sufficient to accurately predict the collision cross section. The models were built using multiple linear regression (MLR) and computational neural networks (CNNs). The best MLR model found contains six descriptors and predicts 94 of 113 peptides (83%) to within 2% of their experimentally determined values. The best CNN model using the same six descriptors predicts 105 of the 113 peptides (93%) to within 2% of their experimentally determined values. The best overall CNN model, using a different set of six descriptors, predicts 109 of the 113 peptides (96%) to within 2% of their experimentally determined values. In addition, this model can discriminate among peptides having identical amino acid composition, but differing in primary amino acid sequence. This represents a capability not found in previously described models. The descriptors used in the models presented may provide some insight into the nature of peptide ion folding in the gas phase.

Amino Acid Sequence↗

PubMedDate not supplied

QSAR/QSPR studies using probabilistic neural networks and generalized regression neural networks.

The Probabilistic Neural Network (PNN) and its close relative, the Generalized Regression Neural Network (GRNN), are presented as simple yet powerful neural network techniques for use in Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) studies. The PNN methodology is applicable to classification problems, and the GRNN is applicable to continuous function mapping problems. The basic underlying theory behind these probability-based methods is presented along with two applications of the PNN/GRNN methodology. The PNN model presented identifies molecules as potential soluble epoxide hydrolase inhibitors using a binary classification scheme. The GRNN model presented predicts the aqueous solubility of nitrogen- and oxygen-containing small organic molecules. For each application, the network inputs consist of a small set of descriptors that encode structural features at the molecular level. Each of these studies has also been previously addressed in this research group using more traditional techniques such as k-nearest neighbor classification, multiple linear regression, and multilayer feed-forward neural networks. In each case, the predictive power of the PNN and GRNN models was found to be comparable to that of the more traditional techniques but requiring significantly fewer input descriptors.

Bayes Theorem↗