Homology modeling of mosquito cytochrome P450 enzymes involved in pyrethroid metabolism: insights into differences in substrate selectivity.

BACKGROUND: Cytochrome P450 enzymes (P450s) have been implicated in insecticide resistance. Anopheles minumus mosquito P450 isoforms CYP6AA3 and CYP6P7 are capable of metabolizing pyrethroid insecticides, however CYP6P8 lacks activity against this class of compounds. FINDINGS: Homology models of the three An. minimus P450 enzymes were constructed using the multiple template alignment method. The predicted enzyme model structures were compared and used for molecular docking with insecticides and compared with results of in vitro enzymatic assays. The three model structures comprise common P450 folds but differences in geometry of their active-site cavities and substrate access channels are prominent. The CYP6AA3 model has a large active site allowing it to accommodate multiple conformations of pyrethroids. The predicted CYP6P7 active site is more constrained and less accessible to binding of pyrethroids. Moreover the predicted hydrophobic interface in the active-site cavities of CYP6AA3 and CYP6P7 may contribute to their substrate selectivity. The absence of CYP6P8 activity toward pyrethroids appears to be due to its small substrate access channel and the presence of R114 and R216 that may prevent access of pyrethroids to the enzyme heme center. CONCLUSIONS: Differences in active site topologies among CYPAA3, CYP6P7, and CYP6P8 enzymes may impact substrate binding and selectivity. Information obtained using homology models has the potential to enhance the understanding of pyrethroid metabolism and detoxification mediated by P450 enzymes.

Viral entry inhibitors block dengue antibody-dependent enhancement in vitro.

Severe dengue virus (DENV) disease symptoms, including dengue hemorrhagic fever and dengue shock syndrome, have been correlated with the presence of pre-existing antibodies that enhance rather than neutralize infections in Fc receptor bearing cells. These antibodies can originate from previous infection with a different serotype of dengue, or from waning antibody titers that occur in infants and young children as they are weaned from breast milk that contains protective dengue-specific antibodies. Despite the apparent importance of this antibody dependent enhancement (ADE) effect, there has been no description of any specific inhibitors of this process. We explored DENV entry inhibitors as a potential strategy to block ADE. Two different peptide entry inhibitors were tested for the ability to block antibody-mediated DENV-2 infection of human, FcRII bearing K562 cells in vitro. Both peptides were able to inhibit ADE, showing that entry inhibitors are possible candidates for the development of specific treatment for severe DENV infection.

Binding capacity of ER-α ligands and SERMs: comparison of the human, dog and cat.

The estrogen molecule is the major risk factor related to mammary gland tumors, with estrogen receptor alpha (ER- α) as the important target stimulating growth. Therefore one alternative approach to treatment of breast cancer is to use selective estrogen receptor modulator (SERM), hormonal therapy. In this study, the structures of ER- α in humans, dogs and cats were predicted using the amino acid sequencing data bank and corrected for general protein structures, receptor sites and docking by adding 2,344 ligands with 15 SERMs into the database and calculating estimated inhibition constants (Ki). Thereby, ranking of best ligands of SERMs in humans, dogs and cats could be achieved. The results show that the shapes of ER- α differ between species but the major pocket sites are the same. Bazedoxifene, a new SERM proved to be the best estrogen antagonist and ER- α inhibitor in all species (human, dog, cat) with the lowest Ki. The other good ligands for dogs and cats are Neohesperidin, Dihydrochalcone, and Schreiber2. The differences in these protein structures may explain why there are only a few SERMs or other ligands which can be used as anti-cancer drugs.

Modeling of pyruvate decarboxylases from ethanol producing bacteria.

Pyruvate decarboxylase (PDC) is a key enzyme in homoethanol fermentation process, which decarboxylates 2-keto acid pyruvate into acetaldehyde and carbon dioxide. PDC enzymes from potential ethanol-producing bacteria such as Zymomonas mobilis, Zymobacter palmae and Sarcina ventriculi have different K(m) and k(cat) values for the substrate pyruvate at their respective optimum pH. In this study, the putative three-dimensional structures of PDC dimer of Z. palmae PDC and S. ventriculi PDC were generated based on the X-ray crystal structures of Z. mobilis PDC, Saccharomyces cerevisiae PDC form-A and Enterobacter cloacae indolepyruvate decarboxylase in order to compare the quaternary structures of these bacterial PDCs with respect to enzyme-substrate interactions, and subunit-subunit interfaces that might be related to the different biochemical characteristics. The PROCHECK scores for both models were within recommended intervals. The generated models are similar to the X-ray crystal structure of Z. mobilis PDC in terms of binding modes of the cofactor, the position of Mg(2+), and the amino acids that form the active sites. However, subunit-subunit interface analysis showed lower H-bonding in both models compared with X-ray crystal structure of Z. mobilis PDC, suggesting a smaller interface area and the possibility of conformational change upon substrate binding in both models. Both models have predicted lower affinity towards branched and aromatic 2-keto acids, which correlated with the molecular volumes of the ligands. The models shed valuable information necessary for further improvement of PDC enzymes for industrial production of ethanol and other products.

Structural optimization and de novo design of dengue virus entry inhibitory peptides.

Viral fusogenic envelope proteins are important targets for the development of inhibitors of viral entry. We report an approach for the computational design of peptide inhibitors of the dengue 2 virus (DENV-2) envelope (E) protein using high-resolution structural data from a pre-entry dimeric form of the protein. By using predictive strategies together with computational optimization of binding "pseudoenergies", we were able to design multiple peptide sequences that showed low micromolar viral entry inhibitory activity. The two most active peptides, DN57opt and 1OAN1, were designed to displace regions in the domain II hinge, and the first domain I/domain II beta sheet connection, respectively, and show fifty percent inhibitory concentrations of 8 and 7 microM respectively in a focus forming unit assay. The antiviral peptides were shown to interfere with virus:cell binding, interact directly with the E proteins and also cause changes to the viral surface using biolayer interferometry and cryo-electron microscopy, respectively. These peptides may be useful for characterization of intermediate states in the membrane fusion process, investigation of DENV receptor molecules, and as lead compounds for drug discovery.

Molecular dynamics simulation of a human thiopurine S-methyltransferase complexed with 6-mercaptopurine model.

Human thiopurine S-methyltransferase (TPMT) is an essential protein in 6-mercaptopurine (6MP) drug metabolism. To understand the pharmacogenetics of TPMT and 6MP, X-ray co-crystal structures of TPMT complexes with S-adenosyl-L-methionine (AdoMet) and 6MP are required. However, the co-crystal structure of this complex has not been reported because 6MP is poorly water soluble. We used molecular dynamics (MD) simulation to predict the structure of the complex of human TPMT-AdoHcy(CH(2))6MP, where the sulfur atoms of AdoHcy and 6MP were linked by a CH(2) group. After 1300 picoseconds of MD simulation, the trajectory showed that 6MP was stabilized in the TPMT active site by formation of non-bonded interactions between 6MP and Phe40, Pro196 and Arg226 side chains of TPMT. The intersulfur distance between AdoHcy and 6MP as well as the binding modes and the interactions of our TPMT-AdoHcy model are consistent with those observed in the X-ray crystal structure of murine TPMT-AdoHcy-6MP complex. The predicted binding modes of AdoHcy and 6MP in our model are consistent with those observed in murine TPMT X-ray crystal structures, which provides structural insights into the interactions of TPMT, AdoHcy, and 6MP at the atomic level and may be used as a starting point for further study of thiopurine drug pharmacogenetics.

VarDetect: a nucleotide sequence variation exploratory tool.

BACKGROUND: Single nucleotide polymorphisms (SNPs) are the most commonly studied units of genetic variation. The discovery of such variation may help to identify causative gene mutations in monogenic diseases and SNPs associated with predisposing genes in complex diseases. Accurate detection of SNPs requires software that can correctly interpret chromatogram signals to nucleotides. RESULTS: We present VarDetect, a stand-alone nucleotide variation exploratory tool that automatically detects nucleotide variation from fluorescence based chromatogram traces. Accurate SNP base-calling is achieved using pre-calculated peak content ratios, and is enhanced by rules which account for common sequence reading artifacts. The proposed software tool is benchmarked against four other well-known SNP discovery software tools (PolyPhred, novoSNP, Genalys and Mutation Surveyor) using fluorescence based chromatograms from 15 human genes. These chromatograms were obtained from sequencing 16 two-pooled DNA samples; a total of 32 individual DNA samples. In this comparison of automatic SNP detection tools, VarDetect achieved the highest detection efficiency. AVAILABILITY: VarDetect is compatible with most major operating systems such as Microsoft Windows, Linux, and Mac OSX. The current version of VarDetect is freely available at http://www.biotec.or.th/GI/tools/vardetect.

Novel paradigms for drug discovery: computational multitarget screening.

An established paradigm in current drug development is (i) to identify a single protein target whose inhibition is likely to result in the successful treatment of a disease of interest; (ii) to assay experimentally large libraries of small-molecule compounds in vitro and in vivo to identify promising inhibitors in model systems; and (iii) to determine whether the findings are extensible to humans. This complex process, which is largely based on trial and error, is risk-, time- and cost-intensive. Computational (virtual) screening of drug-like compounds simultaneously against the atomic structures of multiple protein targets, taking into account protein-inhibitor dynamics, might help to identify lead inhibitors more efficiently, particularly for complex drug-resistant diseases. Here we discuss the potential benefits of this approach, using HIV-1 and Plasmodium falciparum infections as examples. We propose a virtual drug discovery 'pipeline' that will not only identify lead inhibitors efficiently, but also help minimize side-effects and toxicity, thereby increasing the likelihood of successful therapies.

Pre-docking filter for protein and ligand 3D structures.

Virtual drug screening using protein-ligand docking techniques is a time-consuming process, which requires high computational power for binding affinity calculation. There are millions of chemical compounds available for docking. Eliminating compounds that are unlikely to exhibit high binding affinity from the screening set should speed-up the virtual drug screening procedure. We performed docking of 6353 ligands against twenty-one protein X-ray crystal structures. The docked ligands were ranked according to their calculated binding affinities, from which the top five hundred and the bottom five hundred were selected. We found that the volume and number of rotatable bonds of the top five hundred docked ligands are similar to those found in the crystal structures and corresponded with the volume of the binding sites. In contrast, the bottom five hundred set contains ligands that are either too large to enter the binding site, or too small to bind with high specificity and affinity to the binding site. A pre-docking filter that takes into account shapes and volumes of the binding sites as well as ligand volumes and flexibilities can filter out low binding affinity ligands from the screening sets. Thus, the virtual drug screening procedure speed is increased.

Identification of potential HIV-1 targets of minocycline.

Minocycline, a broad spectrum antibiotic, has been discovered to have inhibitory activity against HIV-1 in vitro, but the targets inhibited are unknown. We used a docking with dynamics protocol developed by us to predict the binding affinities of minocycline against seven active sites of five HIV-1 proteins to putatively identify the potential target(s) of minocycline. The results indicate that minocycline has the highest predicted binding affinity against HIV-1 integrase.

Structural insights into the cellular retinaldehyde-binding protein (CRALBP).

Cellular retinaldehyde-binding protein (CRALBP) is an essential protein in the human visual cycle without a known three-dimensional structure. Previous studies associate retinal pathologies to specific mutations in the CRALBP protein. Here we use homology modeling and molecular dynamics methods to investigate the structural mechanisms by which CRALBP functions in the visual cycle. We have constructed two conformations of CRALBP representing two states in the process of ligand association and dissociation. Notably, our homology models map the pathology-associated mutations either directly in or adjacent to the putative ligand-binding cavity. Furthermore, six novel residues have been identified to be crucial for the hinge movement of the lipid-exchange loop in CRALBP. We conclude that the binding and release of retinoid involve large conformational changes in the lipid-exchange loop at the entrance of the ligand-binding cavity.

Prediction of HIV-1 protease inhibitor resistance using a protein-inhibitor flexible docking approach.

Emergence of drug resistance remains one of the most challenging issues in the treatment of HIV-1 infection. Here we focus on resistance to HIV-1 protease inhibitors (PIs) at a molecular level, which can be analysed genotypically or phenotypically. Genotypic assays are based on the analysis of mutations associated with reduced drug susceptibility, but are problematic because of the numerous mutations and mutational patterns that confer drug resistance. Phenotypic resistance or susceptibility can be experimentally evaluated by measuring the amount of free drug bound to HIV-1 protease molecules, but this procedure is expensive and time-consuming. To overcome these problems, we have developed a docking protocol that takes protein-inhibitor flexibility into account to predict phenotypic drug resistance. For six FDA-approved Pls and a total of 1792 HIV-1 protease sequence mutants, we used a combination of inhibitor flexible docking and molecular dynamics (MD) simulations to calculate protein-inhibitor binding energies. Prediction results were expressed as fold changes of the calculated inhibitory constant (Ki), and the samples predicted to have fold-increase in calculated Ki above the fixed cut-off were defined as drug resistant. Our combined docking and MD protocol achieved accuracies ranging from 72-83% in predicting resistance/susceptibility for five of the six drugs evaluated. Evaluating the method only on samples where our predictions concurred with established knowledge-based methods resulted in increased accuracies of 83-94% for the six drugs. The results suggest that a physics-based approach, which is readily applicable to any novel PI and/or mutant, can be used judiciously with knowledge-based approaches that require experimental training data to devise accurate models of HIV-1 Pl resistance prediction.

Virtual screening of HIV-1 protease inhibitors against human cytomegalovirus protease using docking and molecular dynamics.

The clearance of cytomegalovirus viraemia in HIV-1-infected patients may partly result from the inhibition of cytomegalovirus protease by HIV-1 protease inhibitors contained in highly active antiretroviral therapy. We used a computational method to calculate the binding affinity of six HIV-1 protease inhibitors to cytomegalovirus protease based on its X-ray crystallography structure. The calculations showed that amprenavir and indinavir occupy the substrate-binding site of the cytomegalovirus protease with high affinity, and may be implicated in alleviating cytomegalovirus infection.

Heptad-repeat-2 mutations enhance the stability of the enfuvirtide-resistant HIV-1 gp41 hairpin structure.

Enfuvirtide (T20) is a peptide-based fusion inhibitor derived from the heptad repeat 2 (HR2) region of HIV-1 glycoprotein 41 (gp41). The inhibitor binds to the gp41 heptad repeat 1 (HR1) region, thereby blocking viral HR1/HR2 association. Mutations in HR1 have been reported to cause enfuvirtide resistance and reduce viral fitness. In this study, we first showed that scores obtained by a residue-specific all-atom probability discriminatory function (RAPDF) may be used as a reliable predictor of structural stability of gp41 mutants by comparing it to experimentally determined melting temperatures, and as a reliable indicator of enfuvirtide resistance by comparing it to experimentally determined fusion inhibition and viral fitness levels. We then generated an initial set of 28 theoretical structures of the HR1/HR2 hairpin complex where each structure consists of one mutation on HR1 known to cause enfuvirtide resistance and a wild-type amino acid at the corresponding HR2 residue. Mutations were then introduced in the corresponding HR2 residue of each structure where the wild-type amino acid was changed to each of the other nineteen amino acids. The enfuvirtide-resistant HR1 mutants with compensatory mutations at the corresponding HR2 residues had better RAPDF scores than those HR1 mutants with wild-type HR2. This indicates that mutations in HR2 improve structural stability of the HR1/HR2 hairpin complex and may lead to enhanced enfuvirtide resistance when present with resistant HR1 mutations. Modification of the amino acid side chains that contribute to enfuvirtide resistance using the RAPDF scores as a guide may help design of a second generation of fusion inhibitors against the enfuvirtide-resistant strains.

Simple linear model provides highly accurate genotypic predictions of HIV-1 drug resistance.

Drug resistance is a major obstacle to the successful treatment of HIV-1 infection. Genotypic assays are used widely to provide indirect evidence of drug resistance, but the performance of these assays has been mixed. We used standard stepwise linear regression to construct drug resistance models for seven protease inhibitors and 10 reverse transcriptase inhibitors using data obtained from the Stanford HIV drug resistance database. We evaluated these models by hold-one-out experiments and by tests on an independent dataset. Our linear model outperformed other publicly available genotypic interpretation algorithms, including decision tree, support vector machine and four rules-based algorithms (HIVdb, VGI, ANRS and Rega) under both tests. Interestingly, our model did well despite the absence of any terms for interactions between different residues in protease or reverse transcriptase. The resulting linear models are easy to understand and can potentially assist in choosing combination therapy regimens.