SARS Coronavirus-2 (SARS-CoV-2) pandemic has become a global issue which has raised the concern of scientific community to design and discover a countermeasure against this deadly virus. So far, the pandemic has caused the death of hundreds of thousands of people upon infection and spreading. To date, no effective vaccine is available which can combat the infection caused by this virus. Therefore, this study was conducted to design possible epitope-based subunit vaccines against the SARS-CoV-2 virus using the approaches of reverse vaccinology and immunoinformatics. Upon continual computational experimentation, three possible vaccine constructs were designed and one vaccine construct was selected as the best vaccine based on molecular docking study which is supposed to effectively act against the SARS-CoV-2. Thereafter, the molecular dynamics simulation and in silico codon adaptation experiments were carried out in order to check biological stability and find effective mass production strategy of the selected vaccine. This study should contribute to uphold the present efforts of the researches to secure a definitive preventative measure against this lethal disease.

Wuhan Novel Coronavirus (2019-nCoV) outbreak has become global pandemic which has raised the concern of scientific community to deign and discover a definitive cure against this deadly virus which has caused deaths of numerous infected people upon infection and spreading. To date, there is no antiviral therapy or vaccine is available which can effectively combat the infection caused by this virus. This study was conducted to design possible epitope-based subunit vaccines against the 2019-nCoV using the approaches of reverse vaccinology and immunoinformatics. Upon continual computational experimentation three possible vaccine constructs were designed and one vaccine construct was selected as the best vaccine based on molecular docking study which is supposed to effectively act against the Wuhan Novel Coronavirus. Later, molecular dynamics simulation and in silico codon adaptation experiments were carried out in order to check biological stability and find effective mass production strategy of the selected vaccine. Hopefully, this study will contribute to uphold the present efforts of the researches to secure a definitive treatment against this nasty virus.

In this study Curcumin and their different analogues have been analyzed as the inhibitors of signaling proteins i.e., Cycloxygenase-2 (COX-2), Inhibitor of Kappaβ Kinase (IKK) and TANK binding kinase-1 (TBK-1) of Toll Like Receptor 4 (TLR4) pathway involved in inflammation using computational tools. Multiple analogues showed better binding affinity than the approved drugs for the respective targets. Upon continuous computational exploration 6-Gingerol, Yakuchinone A and Yakuchinone B were identified as the best inhibitors of COX-2, IKK and TBK-1 respectively. Then their drug like potentialities were analyzed in different experiments where they also performed sound and similar. Hopefully, this study will uphold the efforts of researchers to identify anti-inflammatory drugs from natural sources.

Background Alzheimer’s disease (AD) is a progressive neurodegenerative age-related dementia that results in memory loss of elderly people. Many hypotheses have been formally articulated till now to decipher the pathogenesis of this disease. According to the compelling amyloidogenic hypothesis, β-secretase is a key regulatory enzyme in AD development and is therefore considered as one of the major targets for the development of drugs to treat AD. In this study, 40 plant-derived phytocompounds, proven to have β-secretase inhibitory activity in different laboratory experiments, were evaluated using computational approaches in order to identify the best possible β-secretase inhibitor(s). Results Amentoflavone (IFD score: − 7.842 Kcal/mol), Bilobetin (IFD score: − 7.417 Kcal/mol), and Ellagic acid (IFD score: − 6.923 Kcal/mol) showed highest β-secretase inhibitory activities with high binding affinity among all the selected phytocompounds and interacted with key amino acids, i.e., Asp32, Tyr71, and Asp228 in the catalytic site of β-secretase. Moreover, these three molecules exhibited promising results in different drug potential assessment experiments and displayed signs of correlation with significant pharmacological and biological activities. Conclusion Amentoflavone, Biolbetin, and Ellagic acid could be investigated further in developing β-secretase-dependent drug for the effective treatment of AD. However, additional in vivo and in vitro experiments might be required to strengthen the findings of this experiment.

In this study Curcumin and their different analogues have been analyzed as the inhibitors of signaling proteins i.e., Cycloxygenase-2 (COX-2), Inhibitor of Kappaβ Kinase (IKK) and TANK binding kinase-1 (TBK-1) of Toll Like Receptor 4 (TLR4) pathway involved in inflammation using computational tools. Multiple analogues showed better binding affinity than the approved drugs for the respective targets. Upon continuous computational exploration 6-Gingerol, Yakuchinone A and Yakuchinone B were identified as the best inhibitors of COX-2, IKK and TBK-1 respectively. Then their drug like potentialities were analyzed in different experiments where they also performed sound and similar. Hopefully, this study will uphold the efforts of researchers to identify anti-inflammatory drugs from natural sources.

Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can complement geotechnical measurements to save project money and time. However, correlations between geotechnical and seismic wave velocity are crucial in order to maximize the benefit of geophysical information. In this work, artificial neural networks (ANNs) models are developed to forecast geotechnical parameters from seismic wave velocity. Specifically, published seismic wave velocity, liquid limit, plastic limit, water content, and dry density from field and laboratory measurements are used to develop ANN models. Due to the small number of data, models are developed with and without the validation step in order to use more data for training. The results indicate that the performance of the models is improved by using more data for training. For example, predicting seismic wave velocity using more data for training improves the R2 value from 0.50 to 0.78 and reduces the ASE from 0.0174 to 0.0075, and MARE from 30.75 to 18.53. The benefit of adding velocity as an input parameter for predicting water content and dry density is assessed by comparing models with and without velocity. Models incorporating the velocity information show better predictability in most cases. For example, predicting water content using field data including the velocity improves the R2 from 0.75 to 0.85 and reduces the ASE from 0.0087 to 0.0051, and MARE from 10.68 to 7.78. A comparison indicates that ANN models outperformed multilinear regression models. For example, predicting seismic wave velocity using field plus lab data has an ANN derived R2 value that is 81.39% higher than regression model.

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