Structural Characterization of CYP51 from Trypanosoma cruzi and Trypanosoma brucei Bound to the Antifungal Drugs Posaconazole and Fluconazole

Chagas Disease is caused by kinetoplastid protozoa Trypanosoma cruzi, whose sterols resemble those of fungi, in both composition and biosynthetic pathway. Azole inhibitors of sterol 14α-demethylase (CYP51), such as fluconazole, itraconazole, voriconazole, and posaconazole, successfully treat fungal infections in humans. Efforts have been made to translate anti-fungal azoles into a second-use application for Chagas Disease. Ravuconazole and posaconazole have been recently proposed as candidates for clinical trials with Chagas Disease patients. However, the widespread use of posaconazole for long-term treatment of chronic infections may be limited by hepatic and renal toxicity, a requirement for simultaneous intake of a fatty meal or nutritional supplement to enhance absorption, and cost. To aid our search for structurally and synthetically simple CYP51 inhibitors, we have determined the crystal structures of the CYP51 targets in T. cruzi and T. brucei, both bound to the anti-fungal drugs fluconazole or posaconazole. The structures provide a basis for a design of new drugs targeting Chagas Disease, and also make it possible to model the active site characteristics of the highly homologous Leishmania CYP51. This work provides a foundation for rational synthesis of new therapeutic agents targeting the three kinetoplastid parasites

Rationale and study design of the prospective, longitudinal, observational cohort study “rISk strAtification in end-stage renal disease” (ISAR) study

Background: The ISAR study is a prospective, longitudinal, observational cohort study to improve the cardiovascular risk stratification in endstage renal disease (ESRD). The major goal is to characterize the cardiovascular phenotype of the study subjects, namely alterations in micro-and macrocirculation and to determine autonomic function. Methods/design: We intend to recruit 500 prevalent dialysis patients in 17 centers in Munich and the surrounding area. Baseline examinations include: (1) biochemistry, (2) 24-h Holter Electrocardiography (ECG) recordings, (3) 24-h ambulatory blood pressure measurement (ABPM), (4) 24 h pulse wave analysis (PWA) and pulse wave velocity (PWV), (5) retinal vessel analysis (RVA) and (6) neurocognitive testing. After 24 months biochemistry and determination of single PWA, single PWV and neurocognitive testing are repeated. Patients will be followed up to 6 years for (1) hospitalizations, (2) cardiovascular and (3) non-cardiovascular events and (4) cardiovascular and (5) all-cause mortality. Discussion/conclusion: We aim to create a complex dataset to answer questions about the insufficiently understood pathophysiology leading to excessively high cardiovascular and non-cardiovascular mortality in dialysis patients. Finally we hope to improve cardiovascular risk stratification in comparison to the use of classical and non-classical (dialysis-associated) risk factors and other models of risk stratification in ESRD patients by building a multivariable Cox-Regression model using a combination of the parameters measured in the study

Methyl Effects on Protein–Ligand Binding

The effects of addition of a methyl group to a lead compound on biological activity are examined. A literature analysis of >2000 cases reveals that an activity boost of a factor of 10 or more is found with an 8% frequency, and a 100-fold boost is a 1 in 200 event. Four cases in the latter category are analyzed in depth to elucidate any unusual aspects of the protein–ligand binding, distribution of water molecules, and changes in conformational energetics. The analyses include Monte Carlo/free-energy perturbation (MC/FEP) calculations for methyl replacements in inhibitor series for p38α MAP kinase, ACK1, PTP1B, and thrombin. Methyl substitutions <i>ortho</i> to an aryl ring can be particularly effective at improving activity by inducing a propitious conformational change. The greatest improvements in activity arise from coupling the conformational gain with the burial of the methyl group in a hydrophobic region of the protein

Simple Predictive Models of Passive Membrane Permeability Incorporating Size-Dependent Membrane-Water Partition

We investigate the relationship between passive permeability and molecular size, in the context of solubility-diffusion theory, using a diverse compound set with molecular weights ranging from 151 to 828, which have all been characterized in a consistent manner using the RRCK cell monolayer assay. Computationally, each compound was subjected to extensive conformational search and physics-based permeability prediction, and multiple linear regression analyses were subsequently performed to determine, empirically, the relative contributions of hydrophobicity and molecular size to passive permeation in the RRCK assay. Additional analyses of Log <i>D</i> and PAMPA data suggest that these measurements are not size selective, a possible reason for their sometimes weak correlation with cell-based permeability

Testing Physical Models of Passive Membrane Permeation

The biophysical basis of passive membrane permeability is well-understood, but most methods for predicting membrane permeability in the context of drug design are based on statistical relationships that indirectly capture the key physical aspects. Here, we investigate molecular mechanics-based models of passive membrane permeability and evaluate their performance against different types of experimental data, including parallel artificial membrane permeability assays (PAMPA), cell-based assays, in vivo measurements, and other in silico predictions. The experimental data sets we use in these tests are diverse, including peptidomimetics, congeneric series, and diverse FDA approved drugs. The physical models are not specifically trained for any of these data sets; rather, input parameters are based on standard molecular mechanics force fields, such as partial charges, and an implicit solvent model. A systematic approach is taken to analyze the contribution from each component in the physics-based permeability model. A primary factor in determining rates of passive membrane permeation is the conformation-dependent free energy of desolvating the molecule, and this measure alone provides good agreement with experimental permeability measurements in many cases. Other factors that improve agreement with experimental data include deionization and estimates of entropy losses of the ligand and the membrane, which lead to size-dependence of the permeation rate

Fe anomalous dispersion data collection and phasing statistics.

1<p>Values in parentheses are for highest-resolution shell; R_sym is meaningless when the individual spot I/σI value is below 1.</p

Sequence alignments between host and pathogen CYP51.

<p>Sequence alignments between CYP51 from <i>Trypanosoma cruzi</i>, <i>Trypanosoma brucei, Aspergillus fumigatus</i>, <i>Candida albicans</i> and human. Accession numbers of the proteins in the Swiss-Prot/TrEMBL (<a href="http://us.expasy.org/sprot" target="_blank">http://us.expasy.org/sprot</a>) and NCBI (<a href="http://www.ncbi.nlm.nih.gov/" target="_blank">http://www.ncbi.nlm.nih.gov/</a>) databases are given next to the name of the protein. Alignments were performed using CLUSTALW program online <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000651#pntd.0000651-Thompson1" target="_blank">[61]</a>. The figure was generated using ESPript <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000651#pntd.0000651-Gouet1" target="_blank">[62]</a>. The secondary structure annotation and residue numbering at the top correspond to CYP51_Tc, residue numbering at the bottom corresponds to human CYP51. The α-helices are labeled with capital letters according to generally accepted P450 nomenclature. The β-strands of large β-sheets are labeled with dashed numbers. Sequential numbers are used to label short two-residue β-strands. Residues within 7 Å of fluconazole are labeled with blue triangles. Additional residues constituting the hydrophobic tunnel are labeled with green triangles. Human H236 and H489 and the corresponding residues in the pathogenic species are highlighted in yellow. Residues corresponding to CYP51_Tc I105 are highlighted in cyan. Mutation hot spots at the tunnel opening are marked with black stars. Gray stars highlight residues in alternate conformations.</p

Spectral characterization of CYP51_Tc and CYP51_Tb.

<p>Soret and visible regions of the CYP51_Tc (<b>A</b>) and CYP51_Tb (<b>B</b>) spectra are shown. The ferric protein (dashed trace) was reduced with sodium dithionite to a ferrous form (solid trace) in the presence of CO. The spectra were recorded at room temperature in a 1 ml quartz cuvette containing 1 µM CYP51 in 10 mM Tris-HCl, pH 7.5, and 10% glycerol using a Cary UV-visible scanning spectrophotometer (Varian). CYP51_Tc has a Soret maximum at 420 nm which upon reduction with sodium dithionite and CO binding shifts to 449 nm (<b>A</b>). CYP51_Tb has a Soret maximum at 417 nm which upon reduction and CO binding shifts to 446 nm (<b>B</b>).</p