Metal bioavailability: how does its significance change in the time?

The significance of terms "metal bioavailability" and "bioavailable metal fraction" is evolved in the time, passing from a very simple concept to a complex concept bound to abiotic and biotic aspects. At the beginning metal toxicity was related to metal fraction present in water phase, than only free metal ion activity was considered and the free ion activity model (FIAM) was proposed. Successively, due to the exceptions observed and to the consciousness that metal bioavailability could be considered as dynamic characteristic the concept of metal bioavailability became very complex, depending on physical, chemical and biological factors.     

How does the skin sense sun light? An integrative view of light sensing molecules

The consensus on the effects of excessive sun exposure on human health has long emphasized the negative effects of solar UV radiation. Nevertheless, although UV radiation has been demonized, less is known about the consequences of sun exposure while using sunscreen, which can lead to high visible light exposure. UV and visible light play key roles in vitamin D synthesis, reduction of blood pressure, among other beneficial effects. In this review, we aim to provide a comprehensive view of the wide range of responses of the human skin to sunlight by revisiting data on the beneficial and harmful effects of UV and visible light. We start by exploring the interaction of photons in the skin at several levels including physical (depth of photon penetration), chemical (light absorption and subsequent photochemical events), and biological (how cells and tissues respond). Skin responses to sun exposure can only be comprehensively understood through a consideration of the light-absorbing molecules present in the skin, especially the light-sensing proteins called opsins. Indeed, many of the cellular responses to sun exposure are modulated by opsins, which act as the “eyes of the skin”.     

Freely available conformer generation methods: how good are they?

Conformer generation has important implications in cheminformatics, particularly in computational drug discovery where the quality of conformer generation software may affect the outcome of a virtual screening exercise. We examine the performance of four freely available small molecule conformer generation tools (Balloon, Confab, Frog2, and RDKit) alongside a commercial tool (MOE). The aim of this study is 3-fold: (i) to identify which tools most accurately reproduce experimentally determined structures; (ii) to examine the diversity of the generated conformational set; and (iii) to benchmark the computational time expended. These aspects were tested using a set of 708 drug-like molecules assembled from the OMEGA validation set and the Astex Diverse Set. These molecules have varying physicochemical properties and at least one known X-ray crystal structure. We found that RDKit and Confab are statistically better than other methods at generating low rmsd conformers to the known structure. RDKit is particularly suited for less flexible molecules while Confab, with its systematic approach, is able to generate conformers which are geometrically closer to the experimentally determined structure for molecules with a large number of rotatable bonds (≥10). In our tests RDKit also resulted as the second fastest method after Frog2. In order to enhance the performance of RDKit, we developed a postprocessing algorithm to build a diverse and representative set of conformers which also contains a close conformer to the known structure. Our analysis indicates that, with postprocessing, RDKit is a valid free alternative to commercial, proprietary software.     

Topology prediction of helical transmembrane proteins: how far have we reached?

Transmembrane protein topology prediction methods play important roles in structural biology, because the structure determination of these types of proteins is extremely difficult by the common biophysical, biochemical and molecular biological methods. The need for accurate prediction methods is high, as the number of known membrane protein structures fall far behind the estimated number of these proteins in various genomes. The accuracy of these prediction methods appears to be higher than most prediction methods applied on globular proteins, however it decreases slightly with the increasing number of structures. Unfortunately, most prediction algorithms use common machine learning techniques, and they do not reveal why topologies are predicted with such a high success rate and which biophysical or biochemical properties are important to achieve this level of accuracy. Incorporating topology data determined so far into the prediction methods as constraints helps us to reach even higher prediction accuracy, therefore collection of such topology data is also an important issue.     

Quality control of Photosystem II: where and how does the degradation of the D1 protein by FtsH proteases start under light stress?--Facts and hypotheses

Degradation of the reaction center-binding D1 protein of Photosystem II is central in photoinhibition of Photosystem II. In higher plant chloroplasts, Photosystem II complexes are abundant in the grana. It has been suggested that the Photosystem II complexes containing photodamaged D1 protein migrate for their repair from the grana to the non-appressed stroma thylakoids, where the photodamaged D1 protein is degraded by a specific protease(s) such as filamentation temperature sensitive H (FtsH) protease. There are several possible ways to activate the FtsH proteases. As FtsH is a membrane-bound ATP-dependent metalloprotease, it requires ATP and zinc as essential part of its catalytic mechanism. It is also suggested that a membrane protein(s) associated with FtsH is required for modulation of the FtsH activity. Here, we propose several possible mechanisms for activation of the proteases, which depend on oligomerization of the monomer subunits. In relation to the oligomerization of FtsH subunits, we also suggest unique distribution of active FtsH hexamers on the thylakoids: hexamers of the FtsH proteases are localized near the Photosystem II complexes at the grana. Degradation of the D1 protein probably takes place in the grana rather than in the stroma thylakoids to circumvent long-distance migration of both the Photosystem II complexes containing the photodamaged D1 protein and the proteases.     

The directive of the protein: how does cytochrome P450 select the mechanism of dopamine formation?

Dopamine can be generated from tyramine via arene hydroxylation catalyzed by a cytochrome P450 enzyme (CYP2D6). Our quantum mechanical/molecular mechanical (QM/MM) results reveal the decisive impact of the protein in selecting the 'best' reaction mechanism. Instead of the traditional Meisenheimer-complex mechanism, the study reveals a mechanism involving an initial hydrogen atom transfer from the phenolic hydroxyl group of the tyramine to the iron-oxo of the compound I (Cpd I), followed by a ring-π radical rebound that eventually leads to dopamine by keto-enol rearrangement. This mechanism is not viable in the gas phase since the O-H bond activation by Cpd I is endothermic and the process does not form a stable intermediate. By contrast, the in-protein reaction has a low barrier and is exothermic. It is shown that the local electric field of the protein environment serves as a template that stabilizes the intermediate of the H-abstraction step and thereby mediates the catalysis of dopamine formation at a lower energy cost. Furthermore, it is shown that external electric fields can either catalyze or inhibit the process depending on their directionality.     

The way to AI-controlled synthesis: how far do we need to go?

Chemical synthesis always plays an irreplaceable role in chemical, materials, and pharmacological fields. Meanwhile, artificial intelligence (AI) is causing a rapid technological revolution in many fields by replacing manual chemical synthesis and has exhibited a much more economical and time-efficient manner. However, the rate-determining step of AI-controlled synthesis systems is rarely mentioned, which makes it difficult to apply them in general laboratories. Here, the history of developing AI-aided synthesis has been overviewed and summarized. We propose that the hardware of AI-controlled synthesis systems should be more adaptive to execute reactions with different phase reagents and under different reaction conditions, and the software of AI-controlled synthesis systems should have richer kinds of reaction prediction modules. An updated system will better address more different kinds of syntheses. Our viewpoint could help scientists advance the revolution that combines AI and synthesis to achieve more progress in complicated systems.

It is still a long march for AI-controlled synthesis to enter into general laboratories. Flaws in the architecture of AI-controlled synthesis systems must be overcome.     

Examining the performance of DFT methods in uranium chemistry: does core size matter for a pseudopotential?

We have investigated the performance of DFT in U(VI) chemistry. A large, representative selection of functionals has been tested, in combination with two ECPs developed in Stuttgart that have different-sized cores (60 and 78 electrons for U). In addition, several tests were undertaken with another 14 electron pseudopotential, which was developed in Los Alamos. The experimental database contained vibrational wavenumbers, thermochemical data, and (19)F chemical shifts for molecules of the type UF(6-n)Cl(n). For the prediction of vibrational wavenumbers, the large-core RECP (14 electrons) gives results that are at least as good as those obtained with the small-core RECP (32 electrons). GGA functionals are as successful as hybrid GGA for vibrational spectroscopy; typical errors are only a few percent with the Stuttgart pseudopotentials. For thermochemistry, hybrid versions of DFT are more successful than GGA, LDA, or meta-GGA. Marginally better results are obtained with a 32 electron ECP than with 14; since the experimental uncertainties are at least 25 kJ/mol for each reaction, the best functionals give results that are essentially indistinguishable from experiment. However, large-basis CCSD(T) results match experiment better than any DFT that we examined. Our findings for NMR spectroscopy are rather disappointing; no combination of pseudopotential, functional, and basis yields even a qualitatively correct prediction of trends in the (19)F chemical shifts of UF(6-n)Cl(n) species. Results yielded by the large-core RECP are, in general, slightly less bad than those obtained with the small core. We conclude that DFT cannot be recommended for predictions of NMR spectra in this series of compounds, though this conclusion should not be generalized. Our most important result concerns the good performance of the large-core Stuttgart pseudopotential. Given its computational efficiency, we recommend that it be used with DFT methods for the prediction of molecular geometries, vibrational frequencies, and thermochemistry of a given oxidation state. The hybrid GGA functionals MPW1PW91 and PBE0 give the best results overall.     

35 years of palladium-catalyzed cross-coupling with Grignard reagents: how far have we come?

This tutorial review is intended to provide the reader with a timely review of major developments and the current state-of-the-art of palladium-catalyzed cross-coupling reactions with Grignard reagents. Organomagnesium reagents, the most reactive and most easily accessible nucleophiles for carbon-carbon bond forming cross-coupling reactions, were the first nucleophiles ever employed in cross-coupling reactions, but have only recently been re-discovered for highly efficient and (stereo)selective coupling reactions. This is mostly a consequence of improved catalyst systems with bulky phosphine, phosphonate or carbene ligands and new metal-halogen exchange procedures for the generation of functionalized Grignard reagents.     

Role of solvents in the gelation process: can light scattering studies shed some light?

Sol–gel reactions continue to be of interest for the preparation of nanostructured materials. Two chemical reactions that are important in the sol–gel process are the hydrolysis and condensation reactions. The rate of the these two reactions are affected by a number of factors such as reaction pH, temperature, humidity, amount of water, type of alkoxide, molar ratio of alkoxide to water, and nature of solvent. Moreover, there is a physical process, that of particle aggregation that is also important in the overall gelation process. The role of solvents in these chemical and physical processes is still not very clear. In order to clarify the role of solvents in the gelation process, small angle light scattering studies (SALS) were carried out. A model system chosen was a colloidal silica solution that contained preformed silica particles of 10–15 nm in diameter. SALS studies indicate that gelation times are independent of the nature of solvent.     

X-ray tomography: how to evaluate the reconstruction quality?

Different reconstruction techniques are used to reconstruct the distribution of the physical characteristics, describing a sample under investigation, from a set of tomographic projections. We present a technique for the evaluation of the reconstruction quality. The technique is based on the comparison of two images (phantom and reconstructed image) by means of the correlation coefficient and of the mean square error between them. In parallel, the correlation coefficient and mean square error are calculated for the wavelet transforms of the phantom and reconstructed images. The scales for the wavelet transform are chosen in agreement with the major geometric parameters of the phantom. Then the correlation coefficient of the wavelet transform with the chosen scale yields an evaluation of the quality of the phantom parameters reconstruction. The accuracy of the parameters reconstruction is determined by the mean square error for the selected scale. The phantom used for the analysis is a medium with randomly distributed grains. The distribution is characterized by two parameters: grain size and grain density (average number of grains per unit area). The parameters are used as the scales for the wavelet transform calculation. We make a comparison of the Algebraic Reconstruction Technique (ART) and the Filtered Back Projection Algorithm.     

Magnetic criteria of aromaticity in a benzene cation and anion: how does the Jahn–Teller effect influence the aromaticity?

The aromatic/antiaromatic behavior of the Jahn–Teller (JT) active benzene cation and anion has been investigated using Density Functional Theory (DFT) calculations of Nuclear Independent Chemical Shifts (NICS) and magnetic susceptibility. NICS parameters have been scanned along the Intrinsic Distortion Path (IDP) for the benzene cation showing antiaromaticity which decreases with increasing deviation from D_6h to D_2h symmetry. Changes in NICS values along the IDP from D_6h to C_2v in the benzene anion revealed non-aromatic character.     

cis-1,5-Diaminocyclooctane: the most basic gaseous primary amine?

The gas phase basicity of the title compound has been determined to be greater than that of putrescine, making it the most basic primary diamine measured to date.     

New model for a theoretical density functional theory investigation of the mechanism of the carbonic anhydrase: how does the internal bicarbonate rearrangement occur?

A theoretical density functional theory (DFT, B3LYP) investigation has been carried out on the catalytic cycle of the carbonic anhydrase. A model system including the Glu106 and Thr199 residues and the "deep" water molecule has been used. It has been found that the nucleophilic attack of the zinc-bound OH on the CO(2) molecule has a negligible barrier (only 1.2 kcal mol(-1)). This small value is due to a hydrogen-bond network involving Glu106, Thr199, and the deep water molecule. The two usually proposed mechanisms for the internal bicarbonate rearrangement have been carefully examined. In the presence of the two Glu106 and Thr199 residues, the direct proton transfer (Lipscomb mechanism) is a two-step process, which proceeds via a proton relay network characterized by two activation barriers of 4.4 and 9.0 kcal mol(-1). This pathway can effectively compete with a rotational mechanism (Lindskog mechanism), which has a barrier of 13.2 kcal mol(-1). The fast proton transfer found here is basically due to the effect of the Glu106 residue, which stabilizes an intermediate situation where the Glu106 fragment is protonated. In the absence of Glu106, the barrier for the proton transfer is much larger (32.3 kcal mol(-1)) and the Lindskog mechanism becomes favored.     

How do aminoadamantanes block the influenza M2 channel, and how does resistance develop?

The interactions between channels and their cognate blockers are at the heart of numerous biomedical phenomena. Herein, we unravel one particularly important example bearing direct pharmaceutical relevance: the blockage mechanism of the influenza M2 channel by the anti-flu amino-adamantyls (amantadine and rimantadine) and how the channel and, consequently, the virus develop resistance against them. Using both computational analyses and experimental verification, we find that amino-adamantyls inhibit M2's H(+) channel activity by electrostatic hindrance due to their positively charged amino group. In contrast, the hydrophobic adamantyl moiety on its own does not impact conductivity. Additionally, we were able to uncover how mutations in M2 are capable of retaining drug binding on the one hand yet rendering the protein and the mutated virus resistant to amino-adamantyls on the other hand. We show that the mutated, drug-resistant protein has a larger binding pocket for the drug. Hence, despite binding the channel, the drug remains sufficiently mobile so as not to exert a H(+)-blocking positive electrostatic hindrance. Such insight into the blocking mechanism of amino-adamantyls, and resistance thereof, may aid in the design of next-generation anti-flu agents.     

1,3-Dipolar reactions involving corannulene: how does its rim and spoke addition vary?

[reaction: see text] Corannulene undergoes 1,3-dipolar reactions with the dipoles, diazomethane, nitrile oxide, and nitrone through its rim and spoke pi bonds; the rim addition yields "one possible" adduct whereas two "regioselective" adducts are formed by spoke addition. Mechanisms of these reactions have been investigated at the B3LYP/6-31G(d) level. Computations show that both rim and spoke additions prefer concerted pathways that lie 2-5 kcal/mol lower in energy than stepwise paths. Stepwise additions can take place in two ways and the activation energies of these two modes differ by 1-2 kcal/mol. A close inspection of the energy profiles reveals that rim addition is more favorable kinetically and thermodynamically than spoke addition in view of lower activation energy and higher exothermicity observed for rim addition. The rim bond of corannulene is more flexible for distortion and also has a stronger double bond (i.e. pi-character) than the spoke bond and this facilitates rim addition over spoke addition. Deformation energy analysis also confirms the above through higher deformation in corannulene from the spoke addition when compared to rim addition. In the spoke addition, regio1 reaction is kinetically more favored than regio2 reaction. Attempts to react corannulene in an endohedral fashion have led to the exohedral adduct. Computed activation energies suggest that corannulene acts as a deactivated dipolarophile compared to ethylene. Even more striking is the observation that rim and spoke double bonds in corannulene are part of the local aromatic system but it shows remarkable reactivity compared to benzene despite the loss of aromaticity during the reaction. This is well indicated by computed NICS values. Inclusion of acetonitrile as solvent through the PCM model increases the reaction rate and exothermicity.     

How does the solvent guide the hydration process?

The hydration numbers are investigated of the glycine amino acid in solutions of substances with different effects on the structure of water: urea, monomethylurea, and 1,3-dimethylurea. Glycine loses a half of its hydration water in a 20m urea solution and only a quarter of it in a 20m dimethylurea solution. The constancy of the hydration number of glycine in concentrated dimethylurea solutions is due to the compensatory effect of the interactions in the ternary and binary systems.     

Pushing the detectability of voltammetry: how low can we go?

The coupling of adsorptive accumulation with catalytic reactions results in remarkably low (sub-picomolar) detection limits. This review assesses various strategies for attaining such dual-amplification effects, that lead to the most sensitive voltammetric technique, adsorptive-catalytic stripping voltammetry (AdCtSV).     

Protein crystal nucleation: is the pair interaction potential the primary determinant of kinetics?

Fundamental understanding of protein crystal nucleation facilitates crystallization of biological macromolecules for structure determination and control of crystal size distribution. In the studies presented here, nucleation kinetics of hen egg-white lysozyme crystals were measured at solution conditions that exhibited equal solubility by adjusting pH, temperature, or sodium chloride concentration. It was observed that solution conditions that lead to equal solubility resulted in equal nucleation rates and hence kinetic parameters. Since the solubility of globular proteins correlates with the osmotic second virial coefficient, B(22), an integral measure of the protein pair interaction potential, this observation indicates that the protein pair interaction plays a key role in determining nucleation kinetic parameters.     

Cluster cross sections from pickup measurements: are the established methods consistent?

Pickup of several molecules, H(2)O, HBr, and CH(3)OH, and Ar atoms on free Ar(N) clusters has been investigated in a molecular beam experiment. The pickup cross sections of the clusters with known mean sizes, ?≈ 150 and 260 were measured by two independent methods: (i) the cluster beam velocity decrease due to the momentum transfer of the picked up molecules to the clusters, and (ii) Poisson distribution of a selected cluster fragment ion as a function of the pickup pressure. In addition, the pickup cross sections were calculated using molecular dynamics and Monte Carlo simulations. The simulations support the results of the velocity measurements. On the other hand, the Poisson distributions yield significantly smaller cross sections, inconsistent with the known Ar(N) cluster sizes. These results are discussed in terms of: (i) an incomplete coagulation of guest molecules on the argon clusters when two or more molecules are picked up; and (ii) the fragmentation pattern of the embedded molecules and their clusters upon ionization on the Ar cluster. We conclude that the Poisson distribution method has to be cautiously examined, if conclusions should be drawn about the cluster cross section, or the mean cluster size ?, and the number of picked up molecules.