Douglasmills9571

We propose that modulation of azurin's folding landscape by the disulfide bridge may be related to both copper capturing and redox sensing.Riboswitches are regulatory ribonucleic acid (RNA) elements that act as ligand-dependent conformational switches that recognize their cognate ligand via a binding pocket located in their aptamer domain. In the apo form, the aptamer domain is dynamic, requiring an ensemble representation of its structure. Here, as a proof-of-concept, we used solvent accessibility information to construct a pair of dynamical ensembles of the aptamer domain of the well-studied S-adenosylmethionine (SAM) class-I riboswitch in the absence (-SAM) and presence (+SAM) of SAM. To achieve this, we first generated a large conformational library and then reweighted conformers in the library using solvent-accessible surface area (SASA) data derived from recently reported light-activated structural examination of RNA (LASER) reactivities measured in the -SAM and +SAM states of the riboswitch. The differences in the resulting -SAM and +SAM ensembles are consistent with a SAM-dependent reshaping of the free-energy landscape of the aptamer domain. Within our -SAM ensemble, we identified a "transient" state that is missing a critical long-range contact, leading us to speculate that it may be representative of a folding intermediate. Further structural analysis also revealed that the transient state harbors a hidden binding pocket that is distinct from the SAM-binding pocket and is predicted by docking calculations to selectively bind small-molecule ligands. The SASA-based method we applied to the SAM-I riboswitch aptamer domain is general and could be used to construct dynamical ensembles for other riboswitch aptamer domains and, more broadly, other classes of structured RNAs.Replacing metallic structures before critical damage is beneficial for safety and for saving energy and resources. One simple approach consists in visually monitoring the early stage of corrosion, and related change of pH, of coated metals. We prepare smart nanoparticle additives for coatings which act as a pH sensor. selleck products The nanoparticles are formed with a terpolymer containing two dyes as side chains, acting as donor and acceptor for a FRET process. Real time monitoring of the extent of localized corrosion on metallic structures is then carried out with a smartphone camera. Colored pH mapping can be then manually retrieved by an operator or automatically recorded by a surveillance camera.The formation of nanocrystals is at the heart of various scientific disciplines, but the atomic mechanisms underlying the early stages of crystallization from supersaturated solutions are still rather unclear. Here, we used in situ liquid-phase scanning transmission electron microscopy to study at the atomic level the very early stages of gold nanocrystal growth, and the evolution of its crystallinity. We found that the nucleation is initiated by the formation of poorly crystalline nanoparticles. These are transformed into monocrystals via nanocrystallization governed by a complex process of multiple out-and-in exchanges of matter between a crystalline-core and a disordered-shell, referred to as the cluster-cloud. Our observations at the crystal/cluster-cloud interface during growth demonstrate that the initially formed nanocrystals expel the poorly crystallized phases as nanoclusters into the cluster-cloud, then readsorb it by two distinct pathways, namely, by (i) monomer attachments and (ii) nanocluster coalescence. This growth process eventually leads to the formation of monocrystalline nanoparticles.The direct synthesis of hydrogen peroxide (H2 + O2 → H2O2) may enable low-cost H2O2 production and reduce environmental impacts of chemical oxidations. Here, we synthesize a series of Pd1Aux nanoparticles (where 0 ≤ x ≤ 220, ∼10 nm) and show that, in pure water solvent, H2O2 selectivity increases with the Au to Pd ratio and approaches 100% for Pd1Au220. Analysis of in situ XAS and ex situ FTIR of adsorbed 12CO and 13CO show that materials with Au to Pd ratios of ∼40 and greater expose only monomeric Pd species during catalysis and that the average distance between Pd monomers increases with further dilution. Ab initio quantum chemical simulations and experimental rate measurements indicate that both H2O2 and H2O form by reduction of a common OOH* intermediate by proton-electron transfer steps mediated by water molecules over Pd and Pd1Aux nanoparticles. Measured apparent activation enthalpies and calculated activation barriers for H2O2 and H2O formation both increase as Pd is diluted by Au, even beyond the complete loss of Pd-Pd coordination. These effects impact H2O formation more significantly, indicating preferential destabilization of transition states that cleave O-O bonds reflected by increasing H2O2 selectivities (19% on Pd; 95% on PdAu220) but with only a 3-fold reduction in H2O2 formation rates. The data imply that the transition states for H2O2 and H2O formation pathways differ in their coordination to the metal surface, and such differences in site requirements require that we consider second coordination shells during the design of bimetallic catalysts.Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations have been developed to simulate molecular systems, where an explicit description of changes in the electronic structure is necessary. However, QM/MM MD simulations are computationally expensive compared to fully classical simulations as all valence electrons are treated explicitly and a self-consistent field (SCF) procedure is required. Recently, approaches have been proposed to replace the QM description with machine-learned (ML) models. However, condensed-phase systems pose a challenge for these approaches due to long-range interactions. Here, we establish a workflow, which incorporates the MM environment as an element type in a high-dimensional neural network potential (HDNNP). The fitted HDNNP describes the potential-energy surface of the QM particles with an electrostatic embedding scheme. Thus, the MM particles feel a force from the polarized QM particles. To achieve chemical accuracy, we find that even simple systems require models with a strong gradient regularization, a large number of data points, and a substantial number of parameters.