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The interaction of self-assembled dendritic amphiphiles with drugs and dyes in aqueous solutions is of great significance for designing and optimizing shape-persistent delivery systems. Here we present deeper insight for two examples of low molecular weight (LMW) nonionic dendritic amphiphiles as host molecules and a series of selected aromatic guest model molecules (benzene, naphthalene, biphenyl, terphenyl, anthracene, and pyrene). Aromatic guest molecules were incorporated into the self-assemblies of dendritic nanocarriers, and the resultant complexes were studied by a combination of UV, NMR, computational simulation, and small-angle X-ray-scattering (SAXS) techniques in order to determine the loading capacity, localization, and specific interactions in dendritic amphiphiles with guest molecules. Our findings revealed that the localization of guest molecules in the hydrophobic region and the loading capacity of guest molecules are dependent on their size and the arrangement of aromatic rings instead of the loading amount. Furthermore, the shape of self-assembled host molecules was found to be ellipsoidal and highly persistent even after loading the guest molecules. To the best of our knowledge, this is the first systematic host–guest study, particularly with low molecular weight nonionic dendritic amphiphilies and aromatic guest molecules. Thus, this study opens new possibilities and ways to explore the transport behavior of aromatic drugs with such nanocarriers.
In this paper, we investigate the interconversion processes of the major flame retardant - 1,2,5,6,9,10-hexabromocyclododecane (HBCD) - by the means of statistical thermodynamics based on classical force-fields. Three ideas will be presented. First, the application of classical hybrid Monte-Carlo simulations for quantum mechanical processes will be justified. Second, the problem of insufficient convergence properties of hybrid Monte-Carlo methods for the generation of low temperature canonical ensembles will be solved by an interpolation approach. Furthermore, it will be shown how free energy differences can be used for a rate matrix computation. The results of our numerical simulations will be compared to experimental results.
Hands-off linear interaction energy approach to binding mode and affinity estimation of estrogens
(2013)
With this work we target the development of a predictictive model for the identification of small molecules which bind to the estrogen receptor alpha and, thus, may act as endocrine disruptors. We propose a combined thermodynamic approach for the estimation of preferential binding modes along with corresponding free energy differences using a linear interaction energy (LIE) ansatz. The LIE model is extended by a Monte Carlo approach for the computation of conformational entropies as recently developed by our group. Incorporating the entropy contribution substantially increased the correlation with experimental affinity values. Both squared coefficients for the fitted data as well as the more meaningful leave-one-out cross-validation of predicted energies were elevated up to rFit2 = 0.87 and qLOO2 = 0.82, respectively. All calculations have been performed on a set of 31 highly diverse ligands regarding their structural properties and affinities to the estrogen receptor alpha. Comparison of predicted ligand orientations with crystallographic data retrieved from the Protein database pdb.org revealed remarkable binding mode predictions.
A novel, cost-efficient method for the analytical extraction of the Fusarium mycotoxin zearalenone (ZON) from edible oils by dynamic covalent hydrazine chemistry (DCHC) was developed and validated for its application with high performance liquid chromatography-fluorescence detection (HPLC-FLD). ZON is extracted from the edible oil by hydrazone formation on a polymer resin functionalised with hydrazine groups and subsequently released by hydrolysis. Specifity and precision of this approach are superior to liquid partitioning or gel permeation chromatography (GPC). DCHC also extracts zearalanone (ZAN) but not α-/β-zearalenol or -zearalanol. The hydrodynamic properties of ZON, which were estimated using molecular simulation data, indicate that the compound is unaffected by nanofiltration through the resin pores and thus selectively extracted. The method's levels of detection and quantification are 10 and 30 µg/kg, using 0.2 g of sample. Linearity is given in the range of 10-20,000 µg/kg, the average recovery being 89%. Bias and relative standard deviations do not exceed 7%. In a sample survey of 44 commercial edible oils based on various agricultural commodities (maize, olives, nuts, seeds, etc.) ZON was detected in four maize oil samples, the average content in the positive samples being 99 µg/kg. The HPLC-FLD results were confirmed by HPLC-tandem mass spectrometry and compared to those obtained by a liquid partitioning based sample preparation procedure.
A novel approach for the simulation of host.guest systems by systematically scanning the host moleculefs orientations within the guest cavity is presented along with a thermodynamic strategy for determining preferential binding modes and corresponding optimal interaction energies between host and guest molecules. By way of example, the elution order of hexabromocyclododecane stereoisomers from high performance liquid chromatography separation on a permethylated β-cyclcodextrin stationary phase has been computed using classical molecular dynamics simulations with the explicit solvents water and acetonitrile. Comparison of estimated with experimental separation data reveals remarkable squared coefficients of correlation with R2=0.87 and a very high correlation RLOO2 . 0:72 using the leaveone-out cross-validation method and water as solvent. In particular, the approach presented shapes up as very robust in terms of the evaluated time range under consideration, reflecting well thermodynamic equilibria. These and further observations correlating with experimental results suggest the suitability of the underlying force fields and our multi-mode approach for the estimation of relative binding affinities for host–guest systems with unknown binding modes.
The emphasis of the present work was to investigate the photochemical conversion of trans- to cis-zearalenone in edible oils under real-life conditions. For quantitation purposes a cis-zearalenone standard was synthesized and characterized for its identity and purity (≥95%) by 1H NMR, X-ray crystallography, HPLC fluorescence and mass spectrometric detection. In a sample survey of 12 edible oils (9 corn oils, 3 hempseed oils) from local supermarkets all corn oils contained trans-zearalenone (median 194 µg/kg), but no cis-zearalenone was detected. For alteration studies trans-zearalenone contaminated corn oils were exposed to sunlight over 4 and 30 weeks, revealing an obvious shift toward cis-zearalenone up to a cis/trans ratio of 9:1 by storage in colorless glass bottles. Irradiation experiments of trans-zearalenone in different organic solvents confirmed the preferred formation of cis-zearalenone possibly caused by entropic effects rather than by enthalpic entities as investigated by quantum chemical and classical force field simulations.
This work addresses the problem of determining the number of components from sequential spectroscopic data analyzed by non-negative matrix factorization without separability assumption (SepFree NMF). These data are stored in a matrix M of dimension “measured times” versus “measured wavenumbers” and can be decomposed to obtain the spectral fingerprints of the states and their evolution over time. SepFree NMF assumes a memoryless (Markovian) process to underline the dynamics and decomposes M so that M = WH, with W representing the components’ fingerprints and H their kinetics. However, the rank of this decomposition (i.e., the number of physical states in the process) has to be guessed from pre-existing knowledge on the observed process. We propose a measure for determining the number of components with the computation of the minimal memory effect resulting from the decomposition; by quantifying how much the obtained factorization is deviating from the Markovian property, we are able to score factorizations of a different number of components. In this way, we estimate the number of different entities which contribute to the observed system, and we can extract kinetic information without knowing the characteristic spectra of the single components. This manuscript provides the mathematical background as well as an analysis of computer generated and experimental sequentially measured Raman spectra.
Raman spectroscopy is a well established tool for the analysis of vibration spectra, which then allow for the determination of individual substances in a chemical sample, or for their phase transitions. In the time-resolved-Raman-sprectroscopy the vibration spectra of a chemical sample are recorded sequentially over a time interval, such that conclusions for intermediate products (transients) can be drawn within a chemical process. The observed data-matrix M from a Raman spectroscopy can be regarded as a matrix product of two unknown matrices W and H, where the first is representing the contribution of the spectra and the latter represents the chemical spectra. One approach for obtaining W and H is the non-negative matrix factorization. We propose a novel approach, which does not need the commonly used separability assumption. The performance of this approach is shown on a real world chemical example.