Rushgeorge9961

This offers a way to fabricate more large-area thin films of amphiphilic molecules.A series of coarse-grained models for molecular simulation of proteins are considered, with emphasis on the application of predicting protein-protein self-interactions for monoclonal antibodies (MAbs). As an illustrative example and for quantitative comparison, the models are used to predict osmotic virial coefficients over a broad range of attractive and repulsive self-interactions and solution conditions for a series of MAbs where the second osmotic virial coefficient has been experimentally determined in prior work. The models are compared based on how well they can predict experimental behavior, their computational burdens, and scalability. An intermediate-resolution model is also introduced that can capture specific electrostatic interactions with improved efficiency and similar or improved accuracy when compared to the previously published models. Guidance is included for the selection of coarse-grained models more generally for capturing a balance of electrostatic, steric, and short-ranged nonelectrostatic interactions for proteins from low to high concentrations.Ionic liquids (ILs) are known to have tunable solvation properties, based on the pairing of different anions and cations, but the compositional landscape is vast and challenging to navigate efficiently. Some computational screening protocols are available, but they can be either time-consuming or difficult to implement. Herein, we perform a detailed investigation of the fundamental role of electrostatic interactions in these systems. find more We effectively develop a bridge between the previous volume-based approach with a quantum structure-property relationship approach to create fast, simple screening guidelines. We propose a new parameter that is applicable to both monovalent and multivalent ions, the ionic polarity index (IPI), which is defined as the ratio of the average electrostatic surface potential (V̅) of the ion to the net charge of the ion (q). The IPI correlation has been tested on a diverse data set of 121 ions, and reliable predictions can be obtained within a homologous series of IL compounds.Here, poly(vinyl alcohol) (PVA) with numerous hydroxyl groups has been applied as a suitable substrate for efficient formation of zinc oxide (ZnO) nanoparticles with a flower shape (confirmed by electron-scanning microscopy), silver iodide (AgI) nanoparticles, and chlorophyll (Chl), as a natural-based photocatalyst (PVA/ZnO/AgI/Chl). First, an efficient preparation route for the PVA/ZnO/AgI/Chl nanophotocatalyst is presented starting from the extraction of Chl from fresh spinach. Then, the catalytic role of the prepared composite is precisely investigated in degradation of methylene blue (MB). The effects of visible-light irradiation, different contact times, and the employed ingredients on the architecture of the PVA/ZnO/AgI/Chl are screened in the degradation process of MB. It is demonstrated that the best result (MB removal efficiency ca. 95.5%) is achieved by applying the visible-light irradiation using a LED lamp (70 W, λ = 425 nm) for a 60 min duration. Moreover, the photocatalytic performance of PVA/ZnO/AgI/Chl has been further confirmed by degradation of Congo red (CR) (ca. 92%, in 150 min) and 4-chlorophenol (4-CP) (88%, in 270 min), as well. As another function of the prepared PVA/ZnO/AgI/Chl composite, a substantial antibacterial property against human bacterial pathogens such as Staphylococcus aureus and Escherichia coli as Gram-positive and Gram-negative bacteria has been noticed, studied by agar diffusion cup plate and colony methods. The zones of inhibition have been evaluated ca. 20 and 12 mm for the S. aureus and E. coli cell lines, respectively. Finally, a great synergy between the prepared composite and the visible light has been observed through the examination of the live bacteria 99.6% for S. aureus and 99.8% for E. coli in the presence of visible light, after the subjection of PVA/ZnO/AgI/Chl particles to the bacteria, verified by the colony counter method.The interaction of rhenium(III) halides Re3Br9 and Re3I9 with aqueous solution of sodium cyanide resulted in the formation of the first trinuclear halide-cyanide rhenium cluster complexes [Re3X3(CN)9]5-/[Re3X3(CN)9]4- (X = Br or I) crystallized as salts of the compositions Cs4Na[Re3Br3(CN)9]·5.25H2O (1), Cs4Na[Re3I3(CN)9]·6H2O (2), Cs4[Re3Br3(CN)9]·2H2O·0.5CsCl (3), and Cs4[Re3I3(CN)9]·(4). All of the compounds are stable in air in the solid state and in aqueous solution. The substitution of apical halide ligands in the parent compounds Re3X9 by cyanides led to reduction of the original metallocluster Re39+ (12 cluster valence electrons (CVEs)) to Re37+ (14 CVEs), forming the compounds 1 and 2. The apical CN- ligands affect the electronic structure of the Re3 metallocluster stabilizing reduced form. Complexes 1 and 2 represent the first examples of triangular rhenium clusters with the Re37+ metallocluster. The reaction of 1 and 2 with H2O2 resulted in formation of compounds 3 and 4 with the formal charge of the Re3 metallocluster equal to 8+, and no further oxidation to Re39+ occurred. The compounds were characterized by the X-ray diffraction analysis, NMR and UV-vis spectroscopies, mass spectrometry, cyclic voltammetry, and magnetic susceptibility measurements.In cross-linking mass spectrometry, the identification of cross-linked peptide pairs heavily relies on the ability of a database search engine to measure the similarities between experimental and theoretical MS/MS spectra. However, the lack of accurate ion intensities in theoretical spectra impairs the performance of search engines, in particular, on proteome scales. Here we introduce pDeepXL, a deep neural network to predict MS/MS spectra of cross-linked peptide pairs. To train pDeepXL, we used the transfer-learning technique because it facilitated the training with limited benchmark data of cross-linked peptide pairs. Test results on more than ten data sets showed that pDeepXL accurately predicted the spectra of both noncleavable DSS/BS3/Leiker cross-linked peptide pairs (>80% of predicted spectra have Pearson's r values higher than 0.9) and cleavable DSSO/DSBU cross-linked peptide pairs (>75% of predicted spectra have Pearson's r values higher than 0.9). pDeepXL also achieved the accurate prediction on unseen data sets using an online fine-tuning technique.