Molecular Systems Design & Engineering最新文献

Odorant-binding proteins (OBPs) and odorant receptors (ORs) have emerged as alternative targets for the development of environment-friendly molecules for pest management. The OBPs are the main proteins present in the antennal sensillum lymph of insects to perceive and interact with behaviorally active molecules in the long process of olfactory signal transduction. The disruption of olfaction by means of bioactive molecules could serve as an environment-friendly approach to alter the behavioral outcomes of insects for effective pest management. In this study, we have used in silico and experimental analysis to screen out behaviorally active molecules against Plutella xylostella. The selected molecules were subjected to docking, MD, and SMD simulations to analyze the binding affinity, stability, and conformational changes in the OBP1 and OR1 proteins. On the basis of in silico analysis, two behaviorally active molecules (ethyl gallate and methyl gallate) are selected to further check their antifeedant activity experimentally. Both molecules showed promising antifeedant/deterrent activity against the larvae of Plutella xylostella in experimental analysis at different concentrations, hence having the potential to be developed as novel antifeedants to protect crops grown under greenhouse and field conditions.

Silicon-type thin films, made of silica, silicon carbide (SiC), or oxycarbide, find use as membranes and electronic sensors, and in semiconductor and solar energy applications. Previously, we studied the preparation of nanoporous silica membranes via deposition of poly(1,3,5,7-tetravinyl-1,3,5,7-tetramethylcyclotetrasiloxane) (pV4D4) films onto SiC macroporous substrates via initiated chemical vapor deposition (iCVD) and their subsequent controlled-atmosphere pyrolysis. Here, we utilize a different method, plasma-enhanced chemical vapor deposition (PECVD), to deposit thin pV4D4 films onto a variety of substrates at significantly higher deposition rates than iCVD and employ a number of experimental techniques to comprehensively investigate the mechanism of conversion of these films into silica ceramics via controlled-atmosphere pyrolysis. The aim of these studies is to better understand the impact of preparation conditions on the structure and properties of the resulting ceramic films. The experiments are coupled with complementary molecular simulations of the pyrolysis process that employ a reactive force field (ReaxFF). This has allowed better understanding, at the molecular level, of the processes that take place during the conversion, via pyrolysis, of the pV4D4 polymer into a silica ceramic.

Ionic liquids have many intriguing properties and widespread applications such as separations and energy storage. However, ionic liquids are complex fluids and predicting their behavior is difficult, particularly in confined environments. We introduce fast and computationally efficient machine learning (ML) models that can predict diffusion coefficients and ionic conductivity of bulk and nanoconfined ionic liquids over a wide temperature range (350–500 K). The ML models are trained on molecular dynamics simulation data for 29 unique ionic liquids as bulk fluids and confined in graphite slit pores. This model is based on simple physical descriptors of the cations and anions such as molecular weight and surface area. We also demonstrate that accurate results can be obtained using only descriptors derived from SMILES (simplified molecular-input line-entry system) codes for the ions with minimal computational effort. This offers a fast and efficient method for estimating diffusion and conductivity of nanoconfined ionic liquids at various temperatures without the need for expensive molecular dynamics simulations.

This study aimed to perform a rational design for molecularly imprinted polymer (MIP) preparation by using pyrrole as the functional monomer for the detection of renal failure biomarkers. Theoretical optimization and frequency calculations were employed at the M06-2X/6-311++G(d,p) level of theory. As the main results, the proper molar ratio for each monomer–template complex and also the most appropriate solvent and cross-linking agent for each MIP were achieved. In the preparation of the pre-polymerization complex, non-polar solvents were found to perform a better stabilization, mainly those which are not protic solvents. As cross-linking agents, better results were obtained for divinylbenzene. The selectivity tests showed a high affinity of the studied MIPs for each template compared to its structural analogs. The frontier molecular orbital (FMO) distribution, molecular electrostatic potentials (MEPs), and natural bond orbital (NBO) analysis were explored to predict the potential active sites in the templates and functional monomer. Theoretical infrared (IR) analysis revealed the formation of strong hydrogen bonds between the N–H group of pyrroles and the oxygen atom of template molecules. This result was confirmed by the quantum theory of atoms in molecules. Finally, the proposed theoretical strategy yielded novel, experimentally testable hypotheses for the design of MIPs.

The molecular-level understanding of intrinsically disordered proteins is challenging due to experimental characterization difficulties. Computational understanding of IDPs also requires fundamental advances, as the leading tools for predicting protein folding (e.g., AlphaFold), typically fail to describe the structural ensembles of IDPs. The focus of this paper is to 1) develop new representations for intrinsically disordered proteins and 2) pair these representations with classical machine learning and deep learning models to predict the radius of gyration and derived scaling exponent of IDPs. Here, we build a new physically-motivated feature called the bag of amino acid interactions representation, which encodes pairwise interactions explicitly into the representation. This feature essentially counts and weights all possible non-bonded interactions in a sequence and thus is, in principle, compatible with arbitrary sequence lengths. To see how well this new feature performs, both categorical and physically-motivated featurization techniques are tested on a computational dataset containing 10 000 sequences simulated at the coarse-grained level. The results indicate that this new feature outperforms the other purely categorical and physically-motivated features and possesses solid extrapolation capabilities. For future use, this feature can potentially provide physical insights into amino acid interactions, including their temperature dependence, and be applied to other protein spaces.

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The burgeoning of mechanically interlocked molecules (MIMs) has attracted great interest in the chemical science community. The development of synthetic methods provides more opportunities to delve into MIMs, especially the motions in response to external stimuli. In addition to the traditional analytical strategies, such as NMR and UV-vis spectroscopy, facile and efficient characterization methods are urgently needed to recognize or even “visualize” the nanoscopic motions. Here we summarize recently reported catenanes and rotaxanes capable of outputting optical signals, such as fluorescence, circularly polarized luminescence, phosphorescence, and plasmonic resonance signals, in response to their molecular motions.

Here, a green and sustainable functionalization of oxidized carbon black by dry ball milling with tetraphenyl phosphonium bromide (TPPBr) is reported. The reaction proceeds efficiently under solvent-free conditions, in the absence of a base, and in a reduced time with a reagent ratio of 1 to 1, much lower than the corresponding reaction in solution. The new mechanochemical approach provides the product with high stability, good mass efficiency, and high sustainability, meeting the green metrics related to waste prevention. This new procedure represents an easy way to realize new fillers and tailor their properties, such as the dispersibility in less polar environments, depending on their potential use.

Ultrathin two-dimensional Janus transition metal dichalcogenides (2D JTMDs) have attracted much attention due to their potential applications in electrocatalysis, sensors, and other electromechanical devices. In the present work, a first principles-based quantum mechanical (QM) hybrid periodic density functional theory (DFT) method has been employed to examine the equilibrium structure, geometry and electronic properties (such as the electronic band structure, band gap and total density of states (DOS)) of a 2D monolayer WSSe JTMD. We have performed non-periodic quantum mechanical DFT computations to find out the most favorable hydrogen evolution reaction (HER) pathway on the W-edges (100) and S-/Se-edges (010) of the 2D Janus WSSe material. The present research shows that the 2D monolayer Janus WSSe TMD follows the Volmer–Heyrovsky reaction mechanism with very low H*-migration and Heyrovsky reaction energy barriers about 2.33–7.52 kcal mol^?1 during the H₂ evolution. It was found that the 2D Janus WSSe has a high value of turnover frequency (TOF) of ～1.91 × 10⁷ s^?1 and a very low Tafel slope (m = 29.54 mV dec^?1 at T = 298.15 K) due to better overlapping of the d-orbital electron cloud of the W atom and the s-orbital electron cloud of the H₂ appearing in the HOMO–LUMO structure of the Heyrovsky TS. The present study demonstrates the extraordinary HER activity and performance of the 2D monolayer WSSe JTMD. Our research exhibits how to computationally devise a highly active electrocatalyst from 2D JTMDs utilizing their active edges, and the current investigation will boost the further development of superior 2D electrocatalysts for efficient HER.

The limitation of NO and NO₂ (NO_x) emissions out of exhaust gases released from diesel engines in confined environments requires efficient adsorbents. Since NO_x species are present in trace amounts (50–1000 ppm) in exhaust gases, and always co-exist with a large content of H₂O (2–12 wt%), adsorbents need to be highly selective to trap NO_x over H₂O. To this end, periodic density functional theory (DFT) calculations in combination with dispersion corrections have been used for a systematic screening of monovalent and divalent cation-exchanged faujasite zeolites. The present work investigates the effect of the cation nature and Si/Al ratios (1.4; 2.43; 23; 47), on the adsorption selectivity of faujasite towards NO and NO₂ against H₂O. Alkali and alkali-earth metals Li(I), Na(I), K(I), Rb(I), Cs(I) and Ca(II), Ba(II), as well as monovalent and divalent transition metals Cu(I), Ag(I), and Zn(II), Pt(II), Pd(II), Cu(II), Fe(II), Co(II), Ni(II) embedded in faujasites, have been explored for their ability to capture NO and NO₂. Bond activation of adsorbed gases has also been checked for the most promising materials to assess the tendency of these gases to further react with the adsorption site. Bader charges and charge density difference calculations were carried out for the most effective faujasite structures to assess the bond formation between materials and adsorbed gases. Much weaker interaction energies were predicted for Y vs. X faujasites, which is in favour of the material's regeneration. Cu(I) and Fe (II) based Y zeolites (Si/Al = 2.43) were identified as the most attractive candidates. Nevertheless, iron strongly activated the bonds of NO₂ upon adsorption raising doubts about its implementation with faujasite. This is the first time that such a large screening of cationic zeolites has been performed for a separation topic using DFT calculations. In the specific case of NO_x/H₂O separation, the present work helped to exclude most of the zeolites explored from future theoretical or experimental investigations, highlighting the potential of Cu(I)Y and the promising selectivity that Fe(II) can bestow on a zeolite.