H2S biosynthesis and catabolism: new insights from molecular studies

Hydrogen sulfide (H₂S) has profound biological effects within living organisms and is now increasingly being considered alongside other gaseous signalling molecules, such as nitric oxide (NO) and carbon monoxide (CO). Conventional use of pharmacological and molecular approaches has spawned a rapidly growing research field that has identified H₂S as playing a functional role in cell-signalling and post-translational modifications. Recently, a number of laboratories have reported the use of siRNA methodologies and genetic mouse models to mimic the loss of function of genes involved in the biosynthesis and degradation of H₂S within tissues. Studies utilising these systems are revealing new insights into the biology of H₂S within the cardiovascular system, inflammatory disease, and in cell signalling. In light of this work, the current review will describe recent advances in H₂S research made possible by the use of molecular approaches and genetic mouse models with perturbed capacities to generate or detoxify physiological levels of H₂S gas within tissues.

Using cheminformatics to predict cross reactivity of "designer drugs" to their currently available immunoassays

Background

A challenge for drug of abuse testing is presented by ‘designer drugs’, compounds typically discovered by modifications of existing clinical drug classes such as amphetamines and cannabinoids. Drug of abuse screening immunoassays directed at amphetamine or methamphetamine only detect a small subset of designer amphetamine-like drugs, and those immunoassays designed for tetrahydrocannabinol metabolites generally do not cross-react with synthetic cannabinoids lacking the classic cannabinoid chemical backbone. This suggests complexity in understanding how to detect and identify whether a patient has taken a molecule of one class or another, impacting clinical care.

Methods

Cross-reactivity data from immunoassays specifically targeting designer amphetamine-like and synthetic cannabinoid drugs was collected from multiple published sources, and virtual chemical libraries for molecular similarity analysis were built. The virtual library for synthetic cannabinoid analysis contained a total of 169 structures, while the virtual library for amphetamine-type stimulants contained 288 compounds. Two-dimensional (2D) similarity for each test compound was compared to the target molecule of the immunoassay undergoing analysis.

Results

2D similarity differentiated between cross-reactive and non-cross-reactive compounds for immunoassays targeting mephedrone/methcathinone, 3,4-methylenedioxypyrovalerone, benzylpiperazine, mephentermine, and synthetic cannabinoids.

Conclusions

In this study, we applied 2D molecular similarity analysis to the designer amphetamine-type stimulants and synthetic cannabinoids. Similarity calculations can be used to more efficiently decide which drugs and metabolites should be tested in cross-reactivity studies, as well as to design experiments and potentially predict antigens that would lead to immunoassays with cross reactivity for a broader array of designer drugs.

You are lost without a map: Navigating the sea of protein structures

X-ray crystal structures propel biochemistry research like no other experimental method, since they answer many questions directly and inspire new hypotheses. Unfortunately, many users of crystallographic models mistake them for actual experimental data. Crystallographic models are interpretations, several steps removed from the experimental measurements, making it difficult for nonspecialists to assess the quality of the underlying data. Crystallographers mainly rely on "global" measures of data and model quality to build models. Robust validation procedures based on global measures now largely ensure that structures in the Protein Data Bank (PDB) are largely correct. However, global measures do not allow users of crystallographic models to judge the reliability of "local" features in a region of interest. Refinement of a model to fit into an electron density map requires interpretation of the data to produce a single "best" overall model. This process requires inclusion of most probable conformations in areas of poor density. Users who misunderstand this can be misled, especially in regions of the structure that are mobile, including active sites, surface residues, and especially ligands. This article aims to equip users of macromolecular models with tools to critically assess local model quality. Structure users should always check the agreement of the electron density map and the derived model in all areas of interest, even if the global statistics are good. We provide illustrated examples of interpreted electron density as a guide for those unaccustomed to viewing electron density.

A comprehensive molecular dynamics approach to protein retention modeling in ion exchange chromatography

In downstream processing, the underlying adsorption mechanism of biomolecules to adsorbent material are still subject of extensive research. One approach to more mechanistic understanding is simulating this adsorption process and hereby the possibility to identify the parameters with strongest impact. So far this method was applied with all-atom molecular dynamics simulations of two model proteins on one cation exchanger. In this work we developed a molecular dynamics tool to simulate protein-adsorber interaction for various proteins on an anion exchanger and ran gradient elution experiments to relate the simulation results to experimental data. We were able to show that simulation results yield similar results as experimental data regarding retention behavior as well as binding orientation. We could identify arginines in case of cation exchangers and aspartic acids in case of anion exchangers as major contributors to binding.

Simplified AutoDock force field for hydrated binding sites

A set of high quality structures of protein-ligand complexes with experimentally determined binding affinities has been extracted from the Protein Data Bank and used to test and recalibrate AutoDock force field. Since for some binding sites water molecules are crucial for bridging the receptor-ligand interactions, they have to be included in the analysis. To simplify the process of incorporating water molecules into the binding sites and make it less ambiguous, new simple water model was created. After recalibration of the force field on the new dataset much better correlation between the computed and experimentally determined binding affinities was achieved and the quality of pose prediction improved even more.

Conjugates of 17-substituted testosterone and epitestosterone with pyropheophorbide a differing in the length of linkers

Conjugates of 17α-substituted testosterone (1 and 2) and 17β-substituted epitestosterone (3 and 4) with pyropheophorbide a were synthesized. The scheme consisted of synthesis of 17α-hydroxy-3-oxopregn-4-en-21-oic and 17β-hydroxy-3-oxopregn-4-en-21-oic acids, and their coupling with pyropheophorbide a by means of either ethylene diamine, or 1,5-diamino pentane linkers. Mutual influence of steroidal and macrocyclic fragments in conjugates molecules was dependent on configuration of C17 and length of linker, that was established by analysis of ¹H NMR spectra and molecular models of conjugates. Studies of interaction of conjugates with prostate carcinoma cells revealed that their uptake and internalization were independent on the androgen receptor activity, but dependent on the structure of conjugates, decreasing in the following row: 3 > 4 ≥ 1 > 2. Conjugates significantly decreased the LNCaP and PC-3 cells growth at 96 h incubation. Epitestosterone derivatives 3 and 4 also showed superior anti-proliferative activity versus testosterone ones. Conformationally more rigid conjugates 1 and 3, comprising short linkers, were more active than those with long linkers; conjugate 3 was the most potent.

Custom-tailored adsorbers: A molecular dynamics study on optimal design of ion exchange chromatography material

The performance of functionalized materials, e.g., ion exchange resins, depends on multiple resin characteristics, such as type of ligand, ligand density, the pore accessibility for a molecule, and backbone characteristics. Therefore, the screening and identification process for optimal resin characteristics for separation is very time and material consuming. Previous studies on the influence of resin characteristics have focused on an experimental approach and to a lesser extent on the mechanistic understanding of the adsorption mechanism. In this in silico study, a previously developed molecular dynamics (MD) tool is used, which simulates any given biomolecule on resins with varying ligand densities. We describe a set of simulations and experiments with four proteins and six resins varying in ligand density, and show that simulations and experiments correlate well in a wide range of ligand density. With this new approach simulations can be used as pre-experimental screening for optimal adsorber characteristics, reducing the actual number of screening experiments, which results in a faster and more knowledge-based development of custom-tailored adsorbers.

Molecular and structural basis of androgen receptor responses to dihydrotestosterone, medroxyprogesterone acetate and Δ(4)-tibolone

Medroxyprogesterone acetate (MPA) has widely been used in hormone replacement therapy (HRT), and is associated with an increased risk of breast cancer, possibly due to disruption of androgen receptor (AR) signaling. In contrast, the synthetic HRT Tibolone does not increase breast density, and is rapidly metabolized to estrogenic 3α-OH-tibolone and 3β-OH-tibolone, and a delta-4 isomer (Δ(4)-TIB) that has both androgenic and progestagenic properties. Here, we show that 5α-dihydrotestosterone (DHT) and Δ(4)-TIB, but not MPA, stabilize AR protein levels, initiate specific AR intramolecular interactions critical for AR transcriptional regulation, and increase proliferation of AR positive MDA-MB-453 breast cancer cells. Structural modeling and molecular dynamic simulation indicate that Δ(4)-TIB induces a more stable AR structure than does DHT, and MPA a less stable one. Microarray expression analyses confirms that the molecular actions of Δ(4)-TIB more closely resembles DHT in breast cancer cells than either ligand does to MPA.

The cognitive life of mechanical molecular models

The use of physical models of molecular structures as research tools has been central to the development of biochemistry and molecular biology. Intriguingly, it has received little attention from scholars of science. In this paper, I argue that these physical models are not mere three-dimensional representations but that they are in fact very special research tools: they are cognitive augmentations. Despite the fact that they are external props, these models serve as cognitive tools that augment and extend the modeler's cognitive capacities and performance in molecular modeling tasks. This cognitive enhancement is obtained because of the way the modeler interacts with these models, the models' materiality contributing to the solving of the molecule's structure. Furthermore, I argue that these material models and their component parts were designed, built and used specifically to serve as cognitive facilitators and cognitive augmentations.

Human health risk assessment of cinnamate UV absorbers: In vitro and in silico investigations

Organic UV absorbers (UVAs) are contaminants of emerging concern. Environmental persistence and potential toxicological enrichment studies of UVAs have attracted international concern. It is important to study the toxicity mechanism of UVAs. This study is the first to report the toxicological mechanism of two cinnamate UV absorbers (CUVAs), 2-ethyl 4-methoxycinnamate (OMC) and isoamyl 4-methoxycinnamate (IMC) based on cellular models and molecular models. Cellular models demonstrated that the CUVAs-induced apoptosis might be associated with cellular mitochondrial damage pathways. The results of molecular models showed that OMC and IMC could affect the binding between major proteins and enzymes in the mitochondrial damage pathway and contaminants, ultimately leading to apoptosis. The cellular-molecular models showed that IMC and OMC have dose-effect relationships on cytotoxicity. The composite model is more informative than a single model. This study further indicate that UVAs causes toxicology effects that have implications for the environment and human health.

Miniature physical sphere-in-contact models of heterogeneous catalysts and metal nanoparticles

CONTEXT

Physical molecular models have played a fundamental role in the understanding of chemical reactions on heterogeneous catalysts and on metal nanoparticles. To date, these physical models have been based on separate models of the metal nanoparticle (NP) or surface and of the substrate and the molecular structure of reactant and product adsorbates and their intermediates. In this paper, we try to provide a new miniature physical molecular model, the sphere-in-contact model of heterogeneous catalysts and metal nanoparticles that can build inexpensive, small and efficient molecular models that can be transported or shipped easily and that depict the chemical reaction as a whole, showing reactants, intermediates, products, the metal nanoparticle bound to the substrate which can give information about a reaction mechanism. These models reveal that there are certain rules with respect to the kind of sites you observe at the metal NP interface with the support by small movement of the nanoparticle.

METHODS

We have used in this study physical molecular models using the sphere-in-contact model. This is the first time such physical models are built for heterogeneous catalytic reactions and metal nanoparticles, and they are constructed out of spheres that fuse together when exposed to water.

Review paper: The importance of consideration of collagen cross-links in computational models of collagen-based tissues

Collagen as the main protein in Extra Cellular Matrix (ECM) is the main load-bearing component of fibrous tissues. Nanostructure and architecture of collagen fibrils play an important role in mechanical behavior of these tissues. Extensive experimental and theoretical studies have so far been performed to capture these properties, but none of the current models realistically represent the complexity of network mechanics because still less is known about the collagen's inner structure and its effect on the mechanical properties of tissues. The goal of this review article is to emphasize the significance of cross-links in computational modeling of different collagen-based tissues, and to reveal the need for continuum models to consider cross-links properties to better reflect the mechanical behavior observed in experiments. In addition, this study outlines the limitations of current investigations and provides potential suggestions for the future work.