Filtern
Erscheinungsjahr
Dokumenttyp
- Zeitschriftenartikel (18)
- Beitrag zu einem Tagungsband (2)
- Vortrag (1)
- Posterpräsentation (1)
Sprache
- Englisch (22)
Schlagworte
- Molecular dynamics (3)
- Crystallization (2)
- HBCD (2)
- Polymorphism (2)
- Raman spectroscopy (2)
- Affinity (1)
- Algorithm (1)
- Analysis (1)
- Anion receptors (1)
- Beta-cyclodextrin (1)
Organisationseinheit der BAM
Eingeladener Vortrag
- nein (1)
Ergopeptides, like ergocornine and a-ergocryptine, exist in an S- and in an R-configuration. Kinetic experiments imply that certain configurations are preferred depending on the solvent. The experimental methods are explained in this article. Furthermore, computational methods are used to understand this configurational preference. Standard quantum chemical methods can predict the favored configurations by using minimum energy calculations on the potential energy landscape. However, the explicit role of the solvent is not revealed by this type of methods. In order to better understand its influence, classical mechanical molecular simulations are applied. It appears from our research that 'folding' the ergopeptide molecules into an intermediate state (between the S- and the R-configuration) is mechanically hindered for the preferred configurations.
The choice of solvents influences crystalline solid formed during the crystallization of active pharmaceutical ingredients (API). The underlying effects are not always well understood because of the complexity of the systems. Theoretical models are often insufficient to describe this phenomenon. In this study, the crystallization behavior of the model drug paracetamol in different solvents was studied based on experimental and molecular dynamics data. The crystallization process was followed in situ using time-resolved Raman spectroscopy. Molecular dynamics with simulated annealing algorithm was used for an atomistic understanding of the underlying processes. The experimental and theoretical data indicate that paracetamol molecules adopt a particular geometry in a given solvent predefining the crystallization of certain polymorphs
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 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.
Crystallization is a complex phenomenon with farreaching implications for the production and formulation of active pharmaceutical ingredients. Understanding this process is critical for achieving control over key physicochemical properties that can affect, for example, the bioavailability and stability of a drug. In this study, we were able to reveal intricate and diverse dynamics of the formation of metastable intermediates of paracetamol crystallization varying with the choice of solvent. We demonstrate the efficacy of our novel approach utilizing an objective function-based non-negative matrix factorization technique for the analysis of time-resolved Raman
spectroscopy data, in conjunction with time-lapse photography.
Furthermore, we emphasize the crucial importance of integrating Raman spectroscopy with supplementary experimental instrumentation for the mathematical analysis of the obtained 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.
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.
To accelerate the growth of Industry 4.0 technologies, the digitalization of mechanical testing laboratories as one of the main data-driven units of materials processing industries is introduced in this paper. The digital lab infrastructure consists of highly detailed and standard-compliant materials testing knowledge graphs for a wide range of mechanical testing processes, as well as some tools that enable the efficient ontology development and conversion of heterogeneous materials’ mechanical testing data to the machine-readable data of uniform and standardized structures. As a basis for designing such a digital lab, the mechanical testing ontology (MTO) was developed based on the ISO 23718 and ISO/IEC 21838-2 standards for the semantic representation of the mechanical testing experiments, quantities, artifacts, and report data. The trial digitalization of materials mechanical testing lab was successfully performed by utilizing the developed tools and knowledge graph of processes for converting the various experimental test data of heterogeneous structures, languages, and formats to standardized Resource Description Framework (RDF) data formats. The concepts of data storage and data sharing in data spaces were also introduced and SPARQL queries were utilized to evaluate how the introduced approach can result in the data retrieval and response to the competency questions. The proposed digital materials mechanical testing lab approach allows the industries to access lots of trustworthy and traceable mechanical testing data of other academic and industrial organizations, and subsequently organize various data-driven research for their faster and cheaper product development leading to a higher performance of products in engineering and ecological aspects.
This article describes a simple and quick in silico method for the prediction of cytochrome P450 (CYP)-mediated hydroxylation of drug-like compounds. Testosterone and progesterone, two known substrates of CYP3A4, are used to test the method. Further, we apply the procedure to predict sites of hydroxylation of isomers of the flame retardant hexabromocyclododecane by CYP3A4. Within the method, the compound is rotated in the binding pocket of the cytochrome, so that each hydrogen under consideration is placed near the active centre. Afterwards, short molecular dynamics simulations are provided for each step of the rotation. All steps of the simulation are compared concerning the distances between the hydrogens and the active centre and the corresponding energies. The computational results correlate well with experimental results.