The RAS–RAF–MEK–ERK pathway plays a key role in malevolent cell progression in many tumors. The high structural complexity in the upstream kinases limits the treatment progress. Thus, MEK inhibition is a promising strategy since it is easy to inhibit and is a gatekeeper for the many malignant effects of its downstream effector. Even though MEK inhibitors are under investigation in many cancers, drug resistance continues to be the principal limiting factor to achieving cures in patients with cancer. Hence, we accomplished a high-throughput virtual screening to overcome this bottleneck by the discovery of dual-targeting therapy in cancer treatment. Here, a total of 11,808 DrugBank molecules were assessed through high-throughput virtual screening for their activity against MEK. Further, the Glide docking, MLSF and prime-MM/GBSA methods were implemented to extract the potential lead compounds from the database. Two compounds, DB012661 and DB07642, were outperformed in all the screening analyses. Further, the study results reveal that the lead compounds also have a significant binding capability with the co-target PIM1. Finally, the SIE-based free energy calculation reveals that the binding of compounds was majorly affected by the van der Waals interactions with MEK receptor. Overall, the in silico binding efficacy of these lead compounds against both MEK and PIM1 could be of significant therapeutic interest to overcome drug resistance in the near future.
Now that the world’s most important problems are the lack of fossil fuel reserves, we need to find a solution to preserve non-renewable resources and replace them with other renewable resources such as solar power. The next focus is on the rising level of undesired gases, such as carbon dioxide, etc. There is a need to regulate the impact of such greenhouse gases. This paper mainly intentional to situate optimal scheduling and to meet the power demand profile by the utilization of available solar energy resources. Considering the above data we have put forth an attempt to scrutinize the leverage of distributed Generation considering solar energy, alone can vary, when associated together they give a dependable wellspring of vitality as a DG is utilized as reinforcement unit for the crisis. So consolidating a solar powered energy source with a diesel generator provides reliable generation, utilizing independent sunlight based renewable power system for a chose area in Vellore region, Tamil Nadu, India. The principle goal of this paper is to decide the optimal sizing dependent on different designs, moreover reduce the cost parameters such as, total net preset Cost (TNPC), system cost of Energy (COE), unmet electric load, CO2 emissions by utilizing HOMER Software. It is found that the PV/Diesel/converter combination provides optimal results which providing vitality with 0% unmet load at the minimum electricity cost, which is diminished from$ 0.672 to $0.319/Kwh.
All tea plants in India rely on the national grid for their electrical needs and diesel for their thermal energy and transportation, which are encountering high costs, high emissions, and issues of accessibility. In this paper, hybrid renewable systems based on both standalone and grid-connected technologies have been modeled using HOMER Pro software for supplying power to a tea manufacturing plant in a typical rural area in India, namely, Gudalur village (Nilgiris), geographically located at 11°30.2′N and 76°29.5′E, which is presently run by the state grid to meet their energy requirements. The different configurations comprised of Solar PV, biomass, hydro, electrolyzer, boiler, thermal load controller to utilize excess electricity, and waste heat recovery options, and lead-acid batteries were designed to meet 650 kWh/day of electricity for processing units, 101 kWh/day of electricity for general applications, 4,450 kWh/day of thermal energy, and 86.35 kg/day of hydrogen energy. To determine the most feasible system design among various scenarios, several criteria such as NPC, COE, LCOH, and CO2 emission of the system have been investigated. In the case of off-grid hybrid systems, results show the highest NPC of $7.01 M with an LCOE of $1.06/kWh is obtained for the diesel generator/boiler/reformer/TLC system. It is reduced to $1.75 M with an LCOE of −$.420/kWh for the PV/biomass-CHP/hydro/TLC scenario. In a grid-connected system, the maximum NPC of $6.20 M with an LCOE of $0.835/kWh is obtained for a diesel generator/boiler system, and it is reduced to −$10.5 M with an LCOE of −$.240/kWh for the PV/biomass-CHP/hydro/TLC scenario. Additionally, in the off-grid systems, the PV/biomass-CHP/hydro/TLC system has LCOH of $4.27/kg, which is economical with the highest renewable fraction of 93%. The PV/biomass-CHP/hydro/TLC hybrid system has the lowest LCOH of −$64.5/kg with a maximum renewable fraction of 96% in on-grid systems. The findings show that recovering excess electricity and waste heat would increase renewable fraction, decrease the energy cost and emissions from the system, and emphasize the importance of TLC and CHP in HRES. According to the simulation results, the grid-connected system is more cost-effective than a stand-alone system due to the revenue obtained from selling renewable power to the grid.
Our earlier experimental and computational report produced the evidence on anti-viral nature of the compound seselin puri ed from the leaf extracts of Aegle marmelos against Bombyx mori Nuclear Polyhedrosis Virus (BmNPV). In the pandemic situation of COVID-19 caused by SARS-COV-2 virus, an in silico effort to evaluate the potentiality of the seselin has been made to test its e cacy against multiple targets of SARS-COV-2 such as SARS-CoV-2S spike protein, COVID-19 main protease and free enzyme of the SARS-CoV-2 (2019-nCoV) main protease. The ligand, seselin showed the best interaction with receptors SARS-CoV-2S protein, COVID-19 main protease and free enzyme of the SARS-CoV-2 (2019-nCoV) main protease with a binding energy of -6.6 kcal/mol, -6.9 kcal/mol and -6.7 kcal/mol, respectively. Docking analysis with three different receptors identi ed that all the computationally predicted lowest energy complexes were stabilized by intermolecular hydrogen bonds and stacking interactions. The aminoacid residues involved in interactions are THR111 and GLN110 for spike protein, SER1003, ALA958 and THR961 for COVID-19 main protease, and for SARS-CoV-2 (2019-nCoV) main protease, it is THR111, GLN110 and THR292. The outcome of pharmacokinetic analysis suggests that the compound had favourable drugability properties by obeying Lipinski rule of ve with e cient ADME properties and exhibiting high a nity towards the binding site that it was directed to. The results suggest that the seselin has inhibitory potential over multiple SARS-COV-2 targets and holds a high potential to work effectively as a novel drug for COVID-19, if evaluated in experimental set ups in foreseeable future.
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