Virtual Screening and Experimental Testing of B1 Metallo-ß-lactamase Inhibitors.

The global rise of metallo-ß-lactamases (MBLs) is problematic due to their ability to inactivate most ß-lactam antibiotics. MBL inhibitors that could be coadministered with and restore the efficacy of ß-lactams are highly sought after. In this study, we employ virtual screening of candidate MBL inhibitors without thiols or carboxylates to avoid off-target effects using the Avalanche software package, followed by experimental validation of the selected compounds. As target enzymes, we chose the clinically relevant B1 MBLs NDM-1, IMP-1, and VIM-2. Among 32 compounds selected from an approximately 1.5 million compound library, 6 exhibited IC50 values less than 40 µM against NDM-1 and/or IMP-1. The most potent inhibitors of NDM-1, IMP-1, and VIM-2 had IC50 values of 19 ± 2, 14 ± 1, and 50 ± 20 µM, respectively. While chemically diverse, the most potent inhibitors all contain combinations of hydroxyl, ketone, ester, amide, or sulfonyl groups. Docking studies suggest that these electron-dense moieties are involved in Zn(II) coordination and interaction with protein residues. These novel scaffolds could serve as the basis for further development of MBL inhibitors. A procedure for renaming NDM-1 residues to conform to the class B ß-lactamase (BBL) numbering scheme is also included.

Fragment-based Shape Signatures: a new tool for virtual screening and drug discovery.

Since its introduction in 2003, the Shape Signatures method has been successfully applied in a number of drug design projects. Because it uses a ray-tracing approach to directly measure molecular shape and properties (as opposed to relying on chemical structure), it excels at scaffold hopping, and is extraordinarily easy to use. Despite its advantages, a significant drawback of the method has hampered its application to certain classes of problems; namely, when the chemical structures considered are large and contain heterogeneous ring-systems, the method produces descriptors that tend to merely measure the overall size of the molecule, and begin to lose selective power. To remedy this, the approach has been reformulated to automatically decompose compounds into fragments using ring systems as anchors, and to likewise partition the ray-trace in accordance with the fragment assignments. Subsequently, descriptors are generated that are fragment-based, and query and target molecules are compared by mapping query fragments onto target fragments in all ways consistent with the underlying chemical connectivity. This has proven to greatly extend the selective power of the method, while maintaining the ease of use and scaffold-hopping capabilities that characterized the original implementation. In this work, we provide a full conceptual description of the next generation Shape Signatures, and we underline the advantages of the method by discussing its practical applications to ligand-based virtual screening. The new approach can also be applied in receptor-based mode, where protein-binding sites (partitioned into subsites) can be matched against the new fragment-based Shape Signatures descriptors of library compounds.

Application of screening methods, shape signatures and engineered biosensors in early drug discovery process.

PURPOSE: In this study, two unreported estrogen antagonists were identified using a combination of computational screening and a simple bacterial estrogen sensor. METHODS: Molecules here presented were initially part of a group obtained from a library of over a half million chemical compounds, using the Shape Signatures method. The structures within this group were then clustered and compared to known antagonists based on their physico-chemical parameters, and possible binding modes of the compounds to the Estrogen Receptor alpha (ER alpha) were analyzed. Finally, thirteen candidate compounds were purchased, and two of them were shown to behave as potential subtype-selective estrogen antagonists using a set of bacterial estrogen biosensors, which included sensors for ER alpha, ER beta, and a negative control thyroid hormone beta biosensor. These activities were then analyzed using an ELISA assay against activated ER alpha in human MCF-7 cell extract. RESULTS: Two new estrogen receptor antagonists were detected using in silico Shape Signatures method with an engineered subtype-selective bacterial estrogen biosensor and commercially available ELISA assay. Additional thyroid biosensor control experiments confirmed no compounds interacted with human thyroid receptor beta. CONCLUSIONS: This work demonstrates an effective combination of computational analysis and simple bacterial screens for rapid identification of potential hormone-like therapeutics.

Understanding nuclear receptors using computational methods.

Nuclear receptors (NRs) are important targets for therapeutic drugs. NRs regulate transcriptional activities through binding to ligands and interacting with several regulating proteins. Computational methods can provide insights into essential ligand-receptor and protein-protein interactions. These in turn have facilitated the discovery of novel agonists and antagonists with high affinity and specificity as well as have aided in the prediction of toxic side effects of drugs by identifying possible off-target interactions. Here, we review the application of computational methods toward several clinically important NRs (with special emphasis on PXR) and discuss their use for screening and predicting the toxic side effects of xenobiotics.

Evaluation of broad spectrum protein kinase inhibitors to probe the architecture of the malarial cyclin dependent protein kinase Pfmrk.

We tested Pfmrk against several naphthalene and isoquinoline sulfonamides previously reported as protein kinase A (PKA) inhibitors. Pfmrk is a Cyclin Dependent protein Kinase (CDK) from Plasmodium falciparum, the causative parasite of the most lethal form of malaria. We find that the isoquinoline sulfonamides are potent inhibitors of Pfmrk and that substitution on the 5 position of the isoquinoline ring greatly influences the degree of potency. Molecular modeling studies suggest that the nitrogen atom in the isoquinoline ring plays a key role in ligand-receptor interactions. Structural analysis suggests that even subtle differences in amino acid composition within the active sites are responsible for conferring specificity of these inhibitors for Pfmrk over PKA.

Development and application of hybrid structure based method for efficient screening of ligands binding to G-protein coupled receptors.

G-protein coupled receptors (GPCRs) comprise a large superfamily of proteins that are targets for nearly 50% of drugs in clinical use today. In the past, the use of structure-based drug design strategies to develop better drug candidates has been severely hampered due to the absence of the receptor's three-dimensional structure. However, with recent advances in molecular modeling techniques and better computing power, atomic level details of these receptors can be derived from computationally derived molecular models. Using information from these models coupled with experimental evidence, it has become feasible to build receptor pharmacophores. In this study, we demonstrate the use of the Hybrid Structure Based (HSB) method that can be used effectively to screen and identify prospective ligands that bind to GPCRs. Essentially; this multi-step method combines ligand-based methods for building enriched libraries of small molecules and structure-based methods for screening molecules against the GPCR target. The HSB method was validated to identify retinal and its analogues from a random dataset of approximately 300,000 molecules. The results from this study showed that the 9 top-ranking molecules are indeed analogues of retinal. The method was also tested to identify analogues of dopamine binding to the dopamine D2 receptor. Six of the ten top-ranking molecules are known analogues of dopamine including a prodrug, while the other thirty-four molecules are currently being tested for their activity against all dopamine receptors. The results from both these test cases have proved that the HSB method provides a realistic solution to bridge the gap between the ever-increasing demand for new drugs to treat psychiatric disorders and the lack of efficient screening methods for GPCRs.

Inhibition of human liver catechol-O-methyltransferase by tea catechins and their metabolites: structure-activity relationship and molecular-modeling studies.

(-)-Epigallocatechin-3-gallate (EGCG) is the major polyphenol present in green tea. We previously demonstrated that EGCG was both a substrate and potent inhibitor of human liver cytosolic catechol-O-methyltransferease (COMT). We now report the structure-activity relationship for the inhibition of COMT-catalyzed O-methylation of catecholestrogens in human liver cytosol by tea catechins and some of their metabolites. The most potent inhibitors were catechins with a galloyl-type D-ring, including EGCG (IC(50)=0.07 microM), 4''-O-methyl-EGCG (IC(50)=0.10 microM), 4',4''-di-O-methyl-EGCG (4',4''-DiMeEGCG) (IC(50)=0.15 microM), and (-)-epicatechin-3-gallate (ECG) (IC(50)=0.20 microM). Catechins without the D-ring showed two to three orders of magnitude less inhibitory potency. Enzyme kinetic analyses revealed that EGCG behaved as a mixed inhibitor, whereas 4',4''-di-O-methyl-EGCG exhibited competitive kinetics for the S-adenosylmethionine (SAM), and noncompetitive kinetics for the catechol binding site. These compounds may represent a new type of COMT inhibitor. In silico molecular-modeling studies using a homology model of human COMT were conducted to aid in the understanding the catalytic and inhibitory mechanisms. Either D-ring or B-ring of EGCG could be accommodated to the substrate binding pocket of human COMT. However, the close proximity (2.6A) of 4''-OH to the critical residue Lys144, the higher acidity of the hydroxyl groups of the D-ring, and the hydrophobic interactions between the D-ring and residues in the binding pocket greatly facilitated the interaction of the D-ring with the enzyme, and resulted in increased inhibitory potency. These results provide mechanistic insight into the inhibition of COMT by commonly consumed tea catechins.

Fractal fingerprinting of chromatographic profiles based on wavelet analysis and its application to characterize the quality grade of medicinal herbs.

Extracting chemical fingerprints is an important step for representing and interpreting chromatographic data. In this paper, the chromatographic profile is decomposed into components at different resolution levels using wavelet analysis, then the fractal dimensions of these components are computed as the chemical fingerprints. The chromatographic fingerprint is characterized by the vector composed of these chemical fingerprints, which can represent the chemical patterns of different categories of complex samples. Computer simulations reveal that the fractal fingerprints are more stable than the original chromatographic profile data with respect to variations of peak retention time. To demonstrate the validity of this method, the evaluation of the quality of the medicinal herb Angelica sinensis (Oliv.) diels is investigated. Principal component analysis of the fractal fingerprints indicates that samples belonging to the same quality grade are clustered together, while those belonging to different quality grades are separated. Using these fractal fingerprints taken from the chromatographic scans as inputs for an artificial neural network (ANN). The quality grades of two sets of the herbs were verified by cross-validation, indicating that 96.7% of the herbs are correctly identified with respect to their quality grades evaluated by experienced experts, and 100.0% of the herbs are correctly identified with respect to their quality grades determined by pharmacodynamical evaluation.