The main protease (Mpro) of the novel coronavirus SARS-CoV-2, which has caused the COVID-19 pandemic, is responsible for the maturation of its key proteins. Thus, inhibiting SARS-CoV-2 Mpro could ...prevent SARS-CoV-2 from multiplying. Because new inhibitors require thorough validation, repurposing current drugs could help reduce the validation process. Many recent studies used molecular docking to screen large databases for potential inhibitors of SARS-CoV-2 Mpro. However, molecular docking does not consider molecular dynamics and thus can be prone to error. In this work, we developed a protocol using free energy perturbation (FEP) to assess the potential inhibitors of SARS-CoV-2 Mpro. First, we validated both molecular docking and FEP on a set of 11 inhibitors of SARS-CoV-2 Mpro with experimentally determined inhibitory data. The experimentally deduced binding free energy exhibits significantly stronger correlation with that predicted by FEP (
R
= 0.94 ± 0.04) than with that predicted by molecular docking (
R
= 0.82 ± 0.08). This result clearly shows that FEP is the most accurate method available to predict the binding affinity of SARS-CoV-2 Mpro + ligand complexes. We subsequently used FEP to validate the top 33 compounds screened with molecular docking from the ZINC15 database. Thirteen of these compounds were predicted to bind strongly to SARS-CoV-2 Mpro, most of which are currently used as drugs for various diseases in humans. Notably, delamanid, an anti-tuberculosis drug, was predicted to inhibit SARS-CoV-2 Mpro in the nanomolar range. Because both COVID-19 and tuberculosis are lung diseases, delamanid has higher probability to be suitable for treating COVID-19 than other predicted compounds. Analysis of the complexes of SARS-CoV-2 Mpro and the top inhibitors revealed the key residues involved in the binding, including the catalytic dyad His14 and Cys145, which is consistent with the structural studies reported recently.
Free Energy Pertubation (FEP) can be used to accurately predict the binding affinity of a ligand to the main protease (Mpro) of the novel coronavirus SARS-CoV-2.
Mangrove forest plays a very important role for both ecosystem services and biodiversity conservation. In Vietnam, mangrove is mainly distributed in the Mekong delta. Recently, mangrove areas in this ...region decreased rapidly in both quality and quantity. The forest became bare, divided and scattered into many small patches, which was a major driver of ecosystem degradation. Without a quantitative method for effectively assessing mangrove health in the regional scale, the sustainably conserving mangrove is the challenge for the local governments. Remote sensing data has been widely used for monitoring mangrove distributions, while the characterization of spatial metrics is important to understand the underlying processes of mangrove change. The objectives of this study were to develop an approach to monitor mangrove health in Mui Ca Mau, Ca Mau province of Vietnam by utilizing satellite image textures to assess the mangrove patterns. The research result showed that mangrove areas increased double by 2015, but the forest had become more fragmented. We can be seen those changes in land use mainly come from land conversion from forest to shrimp farms, settlements areas and public constructions. The conserving existing mangrove forest in Mui Ca Mau should consider the relations between mangrove health and influencing factors indicated in the manuscript.
Four lignans, asarinin (1), horsfieldin (2), 5-(4-(3,4,5-trimethoxyphenyl)hexahydrofuro3,4-cfuran-1-yl)benzod1,3dioxole (3), 5-(4-(3,5-dimethoxyphenyl)hexahydrofuro3,4-cfuran-1-yl)benzod1,3dioxole ...(4), and four non-alkaloid compounds, piperonylic acid (5), hesperidin (6), syringin (7), and β-sitosterol (8) were isolated from the stem bark of Zanthoxylum rhetsa grown in Vietnam. Their chemical structures were elucidated by spectroscopic analysis and compared with the references. Except for compound 6, all the remaining compounds (1–5, 7, and 8) were isolated for the first time from Z. rhetsa. The isolated compounds were tested for their cytotoxic activity against three human cancer cell lines, LU-1, Hep-G2, and KB. Compound 5 showed moderate cytotoxic activity against all three cell lines with IC50 values ranging from 48.13 to 49.06 µg mL−1. Notably, compound 6 demonstrated selective cytotoxicity against LU-1, Hep-G2, and KB cancer cell lines (IC50 66.48–67.41 µg mL−1) while non-toxic toward normal Vero cell. Compound 6 also exhibited its ability to inhibit main protease (Mpro) enzyme in vitro with an IC50 value of 55.49 µg mL−1 and in silico with the binding affinity toward targeted protein of −14.36 kcal mol−1.
Advances in earth observation and machine learning techniques have created new options for forest monitoring, primarily because of the various possibilities that they provide for classifying forest ...cover and estimating aboveground biomass (AGB).
This study aimed to introduce a novel model that incorporates the atom search algorithm (ASO) and adaptive neuro-fuzzy inference system (ANFIS) into mangrove forest classification and AGB estimation. The Ca Mau coastal area was selected as a case study since it has been considered the most preserved mangrove forest area in Vietnam and is being investigated for the impacts of land-use change on forest quality. The model was trained and validated with a set of Sentinel-1A imagery with VH and VV polarizations, and multispectral information from the SPOT image. In addition, feature selection was also carried out to choose the optimal combination of predictor variables. The model performance was benchmarked against conventional methods, such as support vector regression, multilayer perceptron, random subspace, and random forest, by using statistical indicators, namely, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2).
The results showed that all three indicators of the proposed model were statistically better than those from the benchmarked methods. Specifically, the hybrid model ended up at RMSE = 70.882, MAE = 55.458, R2 = 0.577 for AGB estimation.
From the experiments, such hybrid integration can be recommended for use as an alternative solution for biomass estimation. In a broader context, the fast growth of metaheuristic search algorithms has created new scientifically sound solutions for better analysis of forest cover.
The binding pose and affinity between a ligand and enzyme are very important pieces of information for computer-aided drug design. In the initial stage of a drug discovery project, this information ...is often obtained by using molecular docking methods. Autodock4 and Autodock Vina are two commonly used open-source and free software tools to perform this task, and each has been cited more than 6000 times in the last ten years. It is of great interest to compare the success rate of the two docking software programs for a large and diverse set of protein–ligand complexes. In this study, we selected 800 protein–ligand complexes for which both PDB structures and experimental binding affinity are available. Docking calculations were performed for these complexes using both Autodock4 and Autodock Vina with different docking options related to computing resource consumption and accuracy. Our calculation results are in good agreement with a previous study that the Vina approach converges much faster than AD4 one. However, interestingly, AD4 shows a better performance than Vina over 21 considered targets, whereas the Vina protocol is better than the AD4 package for 10 other targets. There are 16 complexes for which both the AD4 and Vina protocols fail to produce a reasonable correlation with respected experiments so both are not suitable to use to estimate binding free energies for these cases. In addition, the best docking option for performing the AD4 approach is the long option. However, the short option is the best solution for carrying out Vina docking. The obtained results probably will be useful for future docking studies in deciding which program to use.
The impact of direct-acting antivirals (DAA) therapy on lipid and glucose metabolism and kidney function in patients with hepatitis C virus (HCV) infection, along with its side effects on blood ...cells, remains controversial. Therefore, we conducted a study that enrolled 280 patients with HCV infection who achieved sustained virologic response after treatment with DAA therapy without ribavirin to evaluate the metabolic changes, renal function, and anemia risk based on real-world data. This study was an observational prospective study with a follow-up period of 12 weeks after the initiation of DAA therapy. Data on biochemical tests, renal function, blood counts, viral load, and host genomics were recorded before treatment and after 12 weeks of treatment with DAAs. DAA therapy reduced fibrosis-4 scores and improved liver function, with significant reductions in aspartate transaminase, alanine aminotransferase, and total bilirubin levels. However, DAA therapy slightly increased uric acid, cholesterol, and low-density lipoprotein cholesterol levels. It significantly reduced fasting blood glucose levels and hemoglobin A1C index (HbA1C) in the study group, while hemoglobin (Hb) and hematocrit (HCT) concentrations decreased significantly (4.78 ± 21.79 g/L and 0.09% ± 0.11%, respectively). The estimated glomerular filtration rate (eGFR) decreased by 12.89 ± 39.04 mL/min/1.73m.sup.2 . Most variations were not related to the genotype, except for Hb, HCT, and HbA1C. Anemia incidence increased from 23.58% before treatment to 30.72% after treatment. Patients with HCV-1 genotype had a higher rate of anemia than did patients with genotype 6 (36.23% vs. 24.62%). Multivariate analysis showed that the risk of anemia was related to female sex, cirrhosis status, fibrosis-4 score, pretreatment eGFR, and pretreatment Hb level. The results of our study can provide helpful information to clinicians for the prognosis and treatment of HCV infection.
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•5-hydroxymethylfurfural (HMF) and 2,5-dimethylfuran (DMF) are valuable chemicals.•HMF and DMF are synthesized from carbohydrate sources through catalytic pathway.•Reaction conditions ...and mechanism of forming HMF and DMF are thoroughly analyzed.•Yield of HMF and DMF using various catalysts and reaction conditions are evaluated.
In recent years, green energy sources have been proposed as alternatives for fossil fuels to meet energy demand while minimizing environmental pollution and global climate change. In this context, agricultural residues can be catalytically converted into furan derivatives. Among furan-based compounds, 5-hydroxymethylfurfural (HMF) and 2,5-dimethylfuran (DMF) are the valuable chemicals that can be converted into desired materials, including fuels. This review article discusses various catalytic HMF and DMF production pathways and the influence of feedstocks, catalysts, solvents, and hydrogen donors on the process yield. Additionally, reaction temperature and H2 pressure effects on the feedstock conversion and HMF and DMF production yields are also presented. The primary attention has been devoted to the literature published in the last five years. However, additional relevant examples have also been discussed to clarify the topic further where necessary. This review aims at providing state-to-the-art information on the current developmental state of DMF and HMF production for researchers in this field.
Essential oils are promising as environmentally friendly and safe sources of pesticides for human use. Furthermore, they are also of interest as aromatherapeutic agents in the treatment of ...Alzheimer’s disease, and inhibition of the enzyme acetylcholinesterase (AChE) has been evaluated as an important mechanism. The essential oils of some species in the genera Callicarpa, Premna, Vitex and Karomia of the family Lamiaceae were evaluated for inhibition of electric eel AChE using the Ellman method. The essential oils of Callicarpa candicans showed promising activity, with IC50 values between 45.67 and 58.38 μg/mL. The essential oils of Callicarpa sinuata, Callicarpa petelotii, Callicarpa nudiflora, Callicarpa erioclona and Vitex ajugifolia showed good activity with IC50 values between 28.71 and 54.69 μg/mL. The essential oils Vitex trifolia subsp. trifolia and Callicarpa rubella showed modest activity, with IC50 values of 81.34 and 89.38, respectively. trans-Carveol showed an IC50 value of 102.88 µg/mL. Molecular docking and molecular dynamics simulation were performed on the major components of the studied essential oils to investigate the possible mechanisms of action of potential inhibitors. The results obtained suggest that these essential oils may be used to control mosquito vectors that transmit pathogenic viruses or to support the treatment of Alzheimer’s disease.
The evolution of Internet of Things (IoT) networks has been studied owing to the associated benefits in useful applications. Although the evolution is highly helpful, the increasing day-to-day ...demands of mobile users have led to immense requirements for further performance improvements such as efficient spectrum utilization, massive device connectivity, and high data rates. Fortunately, reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) techniques have recently been introduced as two possible current-generation emerging technologies with immense potential of addressing the above-mentioned issues. In this paper, we propose the integration of RIS to the existing techniques (i.e., NOMA and relaying) to further enhance the performance for mobile users. We focus on a performance analysis of two-user group by exploiting two main performance metrics including outage probability and ergodic capacity. We provide closed-form expressions for both performance metrics to highlight how NOMA-aided RIS systems provide more benefits compared with the benchmark based on traditional orthogonal multiple access (OMA). Monte-Carlo simulations are performed to validate the correctness of obtained expressions. The simulations show that power allocation factors assigned to two users play a major role in the formation of a performance gap among two users rather than the setting of RIS. In particular, the strong user achieves optimal outage behavior when it is allocated 35% transmit power.
Multi-label text classification (MLTC) is the task that assigns each document to the most relevant subset of class labels. Previous works usually ignored the correlation and semantics of labels ...resulting in information loss. To deal with this problem, we propose a new model that explores label dependencies and semantics by using graph convolutional networks (GCN). Particularly, we introduce an efficient correlation matrix to model label correlation based on occurrence and co-occurrence probabilities. To enrich the semantic information of labels, we design a method to use external information from Wikipedia for label embeddings. Correlated label information learned from GCN is combined with fine-grained document representation generated from another sub-net for classification. Experimental results on three benchmark datasets show that our model outweighs prior state-of-the-art methods. Ablation studies also show several aspects of the proposed model. Our code is available at
https://github.com/chiennv2000/LR-GCN
.
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