Neuraminidase (NA) is a membrane surface antigen which helps in the release of influenza viruses from the host cells after replication. Anti-influenza drugs such as zanamivir bind with eight highly conserved functional residues (R118, D151, R152, R224, E276, R292, R371, and Y406) in the active site of NA, thus restricting the viral release the from host cells. Binding of the drug in active site inhibits the ability of enzyme to cleave sialic acid residues on the cell membrane. Reports on the emergence of zanamivir-resistant strains of H1N1 Influenza virus necessitated a search for alternative drug candidates, preferably from plant source due to their known benefits such as less or no side effects, availability, and low cost. Withaferin A (WA), an active constituent of Withania somnifera ayurvedic herb, has been shown to have a broad range of medicinal properties including its anti-viral activity. The present study demonstrated that WA has the potential to attenuate the neuraminidase of H1N1 influenza. Our docking and simulation results predicted high binding affinity of the WA toward NA and revealed several interesting molecular interactions with the residues which are catalytically important during molecular dynamic simulations. The results presented in the article could be of high importance for further designing of target-specific anti-influenza drug candidates.
Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive advances in this important field, and accordingly, the sensitivity of protein fold recognition has been dramatically improved. Most DL-based methods take an intermediate bottleneck layer as the feature representation of proteins with new fold types. However, this strategy is indirect, inefficient and conditional on the hypothesis that the bottleneck layer’s representation is assumed as a good representation of proteins with new fold types. To address the above problem, in this work, we develop a new computational framework by combining triplet network and ensemble DL. We first train a DL-based model, termed FoldNet, which employs triplet loss to train the deep convolutional network. FoldNet directly optimizes the protein fold embedding itself, making the proteins with the same fold types be closer to each other than those with different fold types in the new protein embedding space. Subsequently, using the trained FoldNet, we implement a new residue–residue contact-assisted predictor, termed FoldTR, which improves protein fold recognition. Furthermore, we propose a new ensemble DL method, termed FSD_XGBoost, which combines protein fold embedding with the other two discriminative fold-specific features extracted by two DL-based methods SSAfold and DeepFR. The Top 1 sensitivity of FSD_XGBoost increases to 74.8% at the fold level, which is ~9% higher than that of the state-of-the-art method. Together, the results suggest that fold-specific features extracted by different DL methods complement with each other, and their combination can further improve fold recognition at the fold level. The implemented web server of FoldTR and benchmark datasets are publicly available at http://csbio.njust.edu.cn/bioinf/foldtr/.
Near-infrared (NIR) phosphor-converted lightemitting diodes (pc-LEDs) have attracted more and more attention because of their many potential optical applications. However, high-performance broadband NIR phosphors are still rare. Here, we demonstrated that Cr 3+ -to-Yb 3+ energy transfer (ET) is an effective strategy to boost the properties of NIR luminescence materials in Ca 2 LaZr 2 Ga 2.8 Al 0.2 O 12 : Cr 3+ ,Yb 3+ (CZGG: Cr 3+ , Yb 3+ ). In addition to the enrichment of shortwave NIR emission in 900−1100 nm, the overall thermal stability of CZGG: Cr 3+ , Yb 3+ can be improved by leveraging high-efficiency ET from Cr 3+ to Yb 3+ , ascribed to the fast ET from Cr 3+ to its nearest Yb 3+ as well as the superior thermal stability of Yb 3+ . Additionally, an NIR pc-LED device was packaged via combining CZGG: Cr 3+ , Yb 3+ with a blue LED chip to demonstrate its possible application in compact nonvisible light sources.
An NMR-based metabolomics approach combined with histopathology and correlation network analysis was adopted to explore the toxicity of C-dots in vivo.
Effective information about ecosystem services is essential to help optimize and prioritize activities that support conservation planning in the face of land use and climate changes. This study shows an approach that integrates several dissimilar models for assessing water-related ecosystem services to predict values in 2050 under three land use scenarios in the Yanhe watershed. The simulated output variables pertaining to water yield and sediment yield were used as indicators for two ecosystem-regulating services, i.e., water flow regulation and erosion regulation, which were quantified using the soil and water assessment tool (SWAT) model. The model results were translated into a relative ecosystem service valuation scale, which facilitated the analysis of spatial and seasonal changes and served as the basis for the applied mapping approach. The simulated results indicate that higher water-related regulation services were concentrated in the middle and lower reaches of rivers with high water yield and low sediment erosion. The highest water flow regulation services occurred in summer; nevertheless, this was when erosion regulation services were the lowest compared to other periods in 2050. A comparison of the three land use scenarios showed differences in the water-related regulation services. Scenario 1, with high forest coverage, had the highest erosion regulation services, but the water flow regulation services were the lowest. Scenario 3 showed the reverse pattern. Scenario 2 had intermediate water flow regulation and erosion regulation. Increasing vegetation cover in the watershed is conducive to controlling water and soil erosion but could lead to a decline in available water resources. Spatial mapping is a powerful tool for displaying the spatiotemporal differences in the water-related regulation services delivered by ecosystems and can help decision makers optimize land use in the future, with the goal of maximizing the benefits offered by ecological services in the Yanhe watershed.
The optically active C3 synthetic blocks are remarkably versatile intermediates for the synthesis of numerous pharmaceuticals and agrochemicals. This work provides a simple and efficient enzymatic synthetic route for the environment-friendly synthesis of C3 chiral building blocks. Chloroperoxidase (CPO)-catalyzed enantioselective halo-hydroxylation and epoxidation of chloropropene and allyl alcohol was employed to prepare C3 chiral building blocks in this work, including (R)-2,3-dichloro-1-propanol (DCP*), (R)-2,3-epoxy-1-propanol (GLD*), and (R)-3-chloro-1-2-propanediol (CPD*). The ee values of the formed C3 chiral building blocks DCP*, CPD*, and glycidol were 98.1, 97.5, and 96.7%, respectively. Moreover, the use of small amount of imidazolium ionic liquid enhanced the yield efficiently due to the increase of solubility of hydrophobic organic substrates in aqueous reaction media, as well as the improvement of affinity and selectivity of CPO to substrate.
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