Revealing the Distribution of the Atoms within Individual Bimetallic Catalyst Nanoparticles

To be able to correlate the catalytic properties of nanoparticles with their structure, detailed knowledge about their make‐up on the atomic level is required. Herein, we demonstrate how atom‐probe tomography (APT) can be used to quantitatively determine the three‐dimensional distribution of atoms within a Au@Ag nanoparticle with near‐atomic resolution. We reveal that the elements are not evenly distributed across the surface and that this distribution is related to the surface morphology and residues from the particle synthesis.     

Chemical Probes for the Functionalization of Polyketide Intermediates

A library of functionalized chemical probes capable of reacting with ketosynthase‐bound biosynthetic intermediates was prepared and utilized to explore in vivo polyketide diversification. Fermentation of ACP mutants of S. lasaliensis in the presence of the probes generated a range of unnatural polyketide derivatives, including novel putative lasalocid A derivatives characterized by variable aryl ketone moieties and linear polyketide chains (bearing alkyne/azide handles and fluorine) flanking the polyether scaffold. By providing direct information on microorganism tolerance and enzyme processing of unnatural malonyl‐ACP analogues, as well as on the amenability of unnatural polyketides to further structural modifications, the chemical probes constitute invaluable tools for the development of novel mutasynthesis and synthetic biology.     

Ion Pairing in Protic Ionic Liquids Probed by Far‐Infrared Spectroscopy: Effects of Solvent Polarity and Temperature

The cation–anion and cation–solvent interactions in solutions of the protic ionic liquid (PIL) [Et₃NH][I] dissolved in solvents of different polarities are studied by means of far infrared vibrational (FIR) spectroscopy and density functional theory (DFT) calculations. The dissociation of contact ion pairs (CIPs) and the resulting formation of solvent‐separated ion pairs (SIPs) can be observed and analyzed as a function of solvent concentration, solvent polarity, and temperature. In apolar environments, the CIPs dominate for all solvent concentrations and temperatures. At high concentrations of polar solvents, SIPs are favored over CIPs. For these PIL/solvent mixtures, CIPs are reformed by increasing the temperature due to the reduced polarity of the solvent. Overall, this approach provides equilibrium constants, free energies, enthalpies, and entropies for ion‐pair formation in trialkylammonium‐containing PILs. These results have important implications for the understanding of solvation chemistry and the reactivity of ionic liquids.     

Observation of Unusual Molecular Diffusion Behaviour below the Lower Critical Solution Temperature of Water/2‐Butoxyethanol Mixtures by using Fluorescence Correlation Spectroscopy

The effect of solute affinity on solute diffusion in binary liquids well below the lower critical solution temperature (LCST) was studied by using fluorescence correlation spectroscopy. We measured the hydrodynamic radii of a hydrophobic and an amphiphilic fluorescent dye under systematic variation of the relative molar fractions of water/2‐butoxyethanol and, for comparison, of water/methanol mixtures, which do not show phase separation. We found that the apparent hydrodynamic radius of the hydrophobic dye almost doubled in water/2‐butoxyethanol, whereas it remained largely unchanged for the amphiphilic dye and in water/methanol mixtures. Our results indicate that the translational diffusion of solutes is influenced by transient local solution structures, even at temperatures well below the LCST. We conclude that, even far below LCST, different solutes can experience different environments in binary liquid mixtures depending on both the solute and solvent properties, all of which impact their reactivity.     

Need for database extension for reliable identification of bacteria from extreme environments using MALDI TOF mass spectrometry

The ability of MALDI TOF MS (matrix-assisted laser desorption ionisation time-of-flight mass spectrometry) to identify cultivable microflora from two waste disposal sites from non-ferrous metal industry was analysed. Despite the harsh conditions (extreme pH values and heavy metal content in red mud disposal site from aluminium production or high heavy metal content in nickel sludge), relatively high numbers of bacteria were recovered. In both environments, the bacterial community was dominated by Gram-positive bacteria, especially by actinobacteria. High-quality MALDI TOF mass spectra were obtained but most of the bacteria isolates could be not identified using MALDI Biotyper software. The overall identification rate was lower than 20 %; in two of the environments tested identification rates were lower than 10 %. As a dominant bacterial species, Microbacterium spp. in drainage water from an aluminium red mud disposal site near ?iar nad Hronom, Bacillus spp. in red mud samples from the same site, and Arthrobacter spp. from nickel smelter sludge near Sereï were identified by a combination of the Biolog system and 16S rRNA sequence analysis. As the primary focus of the MALDI TOF MS-based methodology is directed towards medically important bacteria, reference database spectra expansion and refinement are needed to improve the ability of MALDI TOF MS to identify environmental bacteria, especially those from extreme environments.     

ChemStable: a web server for rule-embedded naïve Bayesian learning approach to predict compound stability

Predicting compound chemical stability is important because unstable compounds can lead to either false positive or to false negative conclusions in bioassays. Experimental data (COMDECOM) measured from DMSO/H₂O solutions stored at 50 °C for 105 days were used to predicted stability by applying rule-embedded naïve Bayesian learning, based upon atom center fragment (ACF) features. To build the naïve Bayesian classifier, we derived ACF features from 9,746 compounds in the COMDECOM dataset. By recursively applying naïve Bayesian learning from the data set, each ACF is assigned with an expected stable probability (p _s ) and an unstable probability (p _uns ). 13,340 ACFs, together with their p _s and p _uns data, were stored in a knowledge base for use by the Bayesian classifier. For a given compound, its ACFs were derived from its structure connection table with the same protocol used to drive ACFs from the training data. Then, the Bayesian classifier assigned p _s and p _uns values to the compound ACFs by a structural pattern recognition algorithm, which was implemented in-house. Compound instability is calculated, with Bayes’ theorem, based upon the p _s and p _uns values of the compound ACFs. We were able to achieve performance with an AUC value of 84 % and a tenfold cross validation accuracy of 76.5 %. To reduce false negatives, a rule-based approach has been embedded in the classifier. The rule-based module allows the program to improve its predictivity by expanding its compound instability knowledge base, thus further reducing the possibility of false negatives. To our knowledge, this is the first in silico prediction service for the prediction of the stabilities of organic compounds.     

Protein Allostery at Atomic Resolution

Protein allostery is a phenomenon involving the long range coupling between two distal sites in a protein. In order to elucidate allostery at atomic resoluion on the ligand-binding WW domain of the enzyme Pin1, multistate structures were calculated from exact nuclear Overhauser effect (eNOE). In its free form, the protein undergoes a microsecond exchange between two states, one of which is predisposed to interact with its parent catalytic domain. In presence of the positive allosteric ligand, the equilibrium between the two states is shifted towards domain–domain interaction, suggesting a population shift model. In contrast, the allostery-suppressing ligand decouples the side-chain arrangement at the inter-domain interface thereby reducing the inter-domain interaction. As such, this mechanism is an example of dynamic allostery. The presented distinct modes of action highlight the power of the interplay between dynamics and function in the biological activity of proteins.     

Cross-Module Enoylreduction in the Azalomycin F Polyketide Synthase

The colinearity of canonical modular polyketide synthases, which creates a direct link between multienzyme structure and the chemical structure of the biosynthetic end-product, has become a cornerstone of knowledge-based genome mining. Herein, we report genetic and enzymatic evidence for the remarkable role of an enoylreductase in the polyketide synthase for azalomycin F biosynthesis. This internal enoylreductase domain, previously identified as acting only in the second of two chain extension cycles on an initial iterative module, is shown to also catalyze enoylreduction in trans within the next module. The mechanism for this rare deviation from colinearity appears to involve direct cross-modular interaction of the reductase with the longer acyl chain, rather than back transfer of the substrate into the iterative module, suggesting an additional and surprising plasticity in natural PKS assembly-line catalysis.