A dynamical model of combination therapy applied to glioma.

Glioma is a human brain tumor that is very difficult to treat at an advanced stage. Studies of glioma biomarkers have shown that some markers are released into the bloodstream, so data from these markers indicate a decrease in the concentration of blood glucose and serum glucose in patients with glioma; these suggest an association between glucose and glioma. This decrease mechanism in glucose concentration can be described by the coupled ordinary differential equations of the early-stage glioma growth and interactions between glioma cells, immune cells, and glucose concentration. In this paper, we propose developing a new mathematical model to explain how glioma cells evolve and survive combination therapy between chemotherapy and oncolytic virotherapy, as an alternative to glioma treatment. In this study, three therapies were applied for analysis, that is, (1) chemotherapy, (2) virotherapy, and (3) a combination of chemotherapy and virotherapy. Virotherapy uses specialist viruses that only attack tumor cells. Based on the simulation results of the therapy carried out, we conclude that combination therapy can reduce the glioma cells significantly compared to the other two therapies. The simulation results of this combination therapy can be an alternative to glioma therapy.

Potential molecular mechanism of reuterin on the inhibition of Aspergillus flavus conidial germination: An in silico study.

Reuterin is a natural antifungal agent derived from certain strains of Limosilactobacillus reuteri. Our previous study revealed that 6 mM reuterin inhibited completely the conidial germination of aflatoxigenic Aspergillus flavus. This study investigated the potential molecular mechanism of reuterin in inhibiting A. flavus conidial germination, which was pre-assumed that it correlated to the inhibition of some essential enzyme activity involved in conidial germination, specifically 1,3-ß-glucan synthase, chitin synthase, and catalases (catalase, bifunctional catalase-peroxidase, and spore-specific catalase). The complex of 1,3-ß-glucan synthase and chitin synthase with reuterin had a lower binding affinity than that with the substrate. Conversely, the complex of catalases with reuterin had a higher binding affinity than that with the substrate. It was suggested that 1,3-ß-glucan synthase and chitin synthase tended to bind the substrate rather than bind reuterin. In contrast, catalases tended to bind reuterin rather than bind the substrate. Therefore, reuterin could be a potential inhibitor of catalases but may not be an inhibitor of 1,3-ß-glucan synthase and chitin synthase. In this in silico study, we predicted that the potential molecular mechanism of reuterin in inhibiting A. flavus conidial germination was due to the inhibition of catalases activities by competitively binding to the enzymes active sites, thus resulting in the accumulation of reactive oxygen species in cells, leading to cells damage. PRACTICAL APPLICATION: This in silico study revealed that reuterin is a potential inhibitor of catalases in A. flavus, thereby interfering with the antioxidant system during conidial germination. This finding shows that reuterin can be used as an antifungal agent in food or agricultural products, inhibiting conidial germination completely.

Characterization, protein modeling, and molecular docking of factor C from Indonesian horseshoe crab (Tachypleus gigas).

BACKGROUND: Horseshoe crab (Tachypleus gigas) amebocytes are useful biomedical components for endotoxin detection, and their growing needs for biomedical purposes cause the horseshoe crab population to decline. Factor C synthesis via genetic engineering offers a solution to replace natural horseshoe crab's factor C and prevent its excessive harvest from nature. In response to these concerns, this study aimed to characterize the amebocyte lysates and factor C protein modeling of T. gigas originated from Banyuasin South Sumatra Estuary. METHODS AND RESULTS: Sampling of T. gigas was carried out in Banyuasin South Sumatra Estuary, Indonesia. The endotoxin test or TAL (Tachypleus amebocyte lysates) assay was performed using gel coagulation method. Protein characterization of protease enzyme was conducted by protease activity, SDS-PAGE, and zymogram analysis. The cDNA of mitochondrial COI gene was amplified for molecular identification followed by cDNA cloning of factor C. Protein modeling was investigated by molecular docking and molecular dynamic (MD) simulation. Endotoxin test results showed that TAL-35 had endotoxin sensitivity in a range of 0.0156-1 EU/ml, while TAL 36 had a sensitivity between 00,625 and 1 EU/ml. T. gigas amebocytes have protease activity in molecular mass sizes less than 60 kDa, with 367 U/ml for TAL 35 and 430 U/ml for TAL 36. The molecular identification revealed 98.68% identity similarity to T. gigas. The docking results suggested three ligands; i.e., diphosphoryl lipid A, core lipid A, and Kdo2 lipid A can be activators of the factor C protein by binding to the region of the receptor to form a ligand-receptor complex. CONCLUSIONS: Endotoxins can be detected using horseshoe crab amebocytes. The presence of proteases is considered responsible for this ability, as evidenced by casein zymogram results. According to docking and MD analysis, we found that lipopolysaccharides (LPS) participate to the binding site of factor C.

Bioactive Compounds in Garlic (Allium sativum) and Black Garlic as Antigout Agents, Using Computer Simulation.

Uric acid, which causes gout, is the end product of purine catabolism, synthesized by xanthine oxidase, guanine deaminase, adenine deaminase, purine nucleoside phosphorylase, and 5-nucleotidase II. Garlic contains bioactive compounds that have potential as antigout agents. Garlic fermentation to black garlic changes its components, which may affect its beneficial potential. This study aimed to select types of garlic (Indonesian garlic) and imported garlic, and to predict the interaction between their compounds and five target proteins through an in silico approach and a multivariate analysis, namely partial least squares-discriminant analysis (PLS-DA), to determine their different constituents. The target proteins were collected from open-access databases, and the compounds were identified using mass spectrometry data. The PLS-DA score plot succeeded in classifying the samples into three classes, with each class having a discriminatory compound. Based on the in silico studies, we predicted the best binding score of the five target proteins with seven important compounds: alliin, N-acetyl-S-allyl-L-cysteine, ajoene, pyridoxal, pyridoxamine, 4-guanidinobutyric acid, and D-glucosamine. These were mostly found in black garlic, with no different concentrations in the local and imported samples. Through this approach, we concluded that black garlic is a better candidate for antigout treatments, as several compounds were found to have good binding to the target proteins.

Effects of Salt Concentration on the Water and Ion Self-Diffusion Coefficients of a Model Aqueous Sodium-Ion Battery Electrolyte.

The aqueous sodium-ion battery is a promising alternative to the well-known lithium-ion battery owing to the large abundance of sodium ion resources. Although it is safer than the lithium-ion battery, the voltage window of the sodium-ion battery is narrower than that of the lithium-ion battery, thus limiting its practical implementation. Therefore, a highly concentrated electrolyte is required to address this issue. In the present work, the effect of the salt concentration on the transport properties of water molecules is investigated via theoretical analyses at the quantum mechanical level. A molecular dynamics simulation at the quantum mechanical level revealed that as the salt concentration increases, the ion-water interactions became stronger, leading to a lower diffusivity and a lower electronic band gap. These imply that the superconcentrated aqueous-based electrolytes have high potentials for the sodium-ion battery applications.

Predicting of the Coronavirus Disease 2019 (COVID-19) Epidemic Using Estimation of Parameters in the Logistic Growth Model.

The COVID-19 pandemic was impacting the health and economy around the world. All countries have taken measures to control the spread of the epidemic. Because it is not known when the epidemic will end in several countries, then the prediction of the COVID-19 pandemic is a very important challenge. This study has predicted the temporal evolution of the COVID-19 pandemic in several countries using the logistic growth model. This model has analyzed several countries to describe the epidemic situation of these countries. The time interval of the actual data used as a comparison with the prediction results of this model was starting in the firstly confirmed COVID-19 cases to December 2020. This study examined an approach to the complexity spread of the COVID-19 pandemic using the logistic growth model formed from an ordinary differential equation. This model described the time-dependent population growth rate characterized by the three parameters of the analytical solution. The non-linear least-squares method was used to estimate the three parameters. These parameters described the rate growth constant of infected cases and the total number of confirmed cases in the final phase of the epidemic. This model is applied to the spread of the COVID-19 pandemic in several countries. The prediction results show the spread dynamics of COVID-19 infected cases which are characterized by time-dependent dynamics. In this study, the proposed model provides estimates for the model parameters that are good for predicting the COVID-19 pandemic because they correspond to actual data for all analyzed countries. It is based on the coefficient of determination, R2, and the R2 value of more than 95% which is obtained from the non-linear curves for all analyzed countries. It shows that this model has the potential to contribute to better public health policy-making in the prevention of the COVID-19 pandemic.

Forecasting the Long-Term Trends of Coronavirus Disease 2019 (COVID-19) Epidemic Using the Susceptible-Infectious-Recovered (SIR) Model.

A simple model for predicting Coronavirus Disease 2019 (COVID-19) epidemic is presented in this study. The prediction model is presented based on the classic Susceptible-Infectious-Recovered (SIR) model, which has been widely used to describe the epidemic time evolution of infectious diseases. The original version of the Kermack and McKendrick model is used in this study. This included the daily rates of infection spread by infected individuals when these individuals interact with a susceptible population, which is denoted by the parameter ß, while the recovery rates to determine the number of recovered individuals is expressed by the parameter γ. The parameters estimation of the three-compartment SIR model is determined through using a mathematical sequential reduction process from the logistic growth model equation. As the parameters are the basic characteristics of epidemic time evolution, the model is always tested and applied to the latest actual data of confirmed COVID-19 cases. It seems that this simple model is still reliable enough to describe the dynamics of the COVID-19 epidemic, not only qualitatively but also quantitatively with a high degree of correlation between actual data and prediction results. Therefore, it is possible to apply this model to predict cases of COVID-19 in several countries. In addition, the parameter characteristics of the classic SIR model can provide information on how these parameters reflect the efforts by each country to prevent the spread of the COVID-19 outbreak. This is clearly seen from the changes of the parameters shown by the classic SIR model.

Mechanistic insight on the remdesivir binding to RNA-Dependent RNA polymerase (RdRp) of SARS-cov-2.

The RNA-dependent RNA polymerase (RdRp) is a key enzyme which regulates the viral replication of SARS-CoV-2. Remdesivir (RDV) is clinically used drug which targets RdRp, however its mechanism of action remains elusive. This study aims to find out the binding dynamics of active Remdesivir-triphosphate (RDV-TP) to RdRp by means of molecular dynamics (MD) simulation. We built a homology model of RdRp along with RNA and manganese ion using RdRp hepatitis C virus and recent SARS-CoV-2 structures. We determined that the model was stable during the 500 ns MD simulations. We then employed the model to study the binding of RDV-TP to RdRp during three independent 500 ns MD simulations. It was revealed that the interactions of protein and template-primer RNA were dominated by salt bridge interactions with phosphate groups of RNA, while interactions with base pairs of template-primer RNA were minimal. The binding of RDV-TP showed that the position of phosphate groups was at the entry of the NTP channel and it was stabilized by the interactions with K551, R553, and K621, while the adenosine group on RDV-TP was pairing with U2 of the template strand. The manganese ion was located close to D618, D760, and D761, and helps in stabilization of the phosphate groups of RDV-TP. Further we identified three hits from the natural product database that pose similar to RDV-TP while having lower binding energies than that of RDV-TP, and that SN00359915 had binding free energy about three times lower than that of RDV-TP.

Cloning, heterologous expression, and characterization of a novel thioesterase from natural sample.

A novel thioesterse gene was successfully cloned and sequenced directly from natural sample of Domas Hot Spring, West Java, Indonesia. Homological analysis of the sequence showed that the gene appeared high homology to thioesterase genes with the highest to a putative thioesterase gene from uncultured Acidilobus sp. JCHS at 66% identity. However, phylogenetic analysis showed that the protein was separated from the branch with other known thioesterases. The size of the gene is around 500 base pairs, lied into 2 kb DNA fragment from a random PCR amplicon. The gene was overexpressed in Escherichia coli, a dominant band appeared at 17 kDa in SDS-PAGE with expression level at around 32% of total proteins. The activity of the purified protein using acetyl-CoA as substrate showed that the protein exhibited thioesterase activity. Furthermore, the enzyme also showed esterase activity on p-nitrophenyl ester as substrate. Detail characterization of esterolytic activity showed that the enzyme preferred p-nitrophenyl decanoate as substrate. The optimum activity of the enzyme was at 80 °C and pH 8. Activity of the enzyme was maintained after incubation at 80 °C up to 24 h. In addition, the enzyme was favorable on polar organic solvents. All the data obtained suggested that the enzyme is a novel alkaline thermostable thioesterase.

Molecular modeling on porphyrin derivatives as ß5 subunit inhibitor of 20S proteasome.

The ubiquitin-proteasome system plays an important role in protein quality control. Currently, inhibition of the proteasome has been validated as a promising approach in anticancer therapy. The 20S core particle of the proteasome harbors ß5 subunit which is a crucial active site in proteolysis. Targeting proteasome ß5 subunit which is responsible for the chymotrypsin-like activity of small molecules has been regarded as an important way for achieving therapeutics target. In the present study, a series of porphyrin derivatives bearing either pyridine or pyrazole rings as meso-substituents were designed and evaluated as an inhibitor for the ß5 subunit of the proteasome by employing molecular docking and dynamics simulations. The molecular docking was performed with the help of AutoDock 4.2, while molecular dynamics simulation was done using AMBER 14. All compounds bound to the proteasome with similar binding modes, and each porphyrin-proteasome complex was stable during 30 ns MD simulation as indicated by root-mean-square-deviation (RMSD) value. An analysis on protein residue fluctuation of porphyrin binding demonstrates that in all complexes, porphyrin binding produces minor fluctuation on amino acid residues. The molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) free energy calculation shows that the binding affinities of mono-H2PyP, bis-H2PzP, and tetra-H2PyP were comparable with that of the potential inhibitor, HU10. It is noted that the electrostatic interaction increases with the number of meso-substituents, which was favourable for porphyrin binding. The present study shows that both electrostatic and van der Waals interaction are the main force which controls the interaction of porphyrin compounds with the proteasome.