Molecular docking analysis of ten plant products for the inhibition of spike glycoprotein and prospective use as anti-COVID compounds

The threat of the COVID-19 pandemic has persisted unabated over the past two years. The current response to maintaining public health has been guided by the vaccination of the population. The success of this policy has been mixed COVID coming back in each region in waves driven by new variants. Given that boosted immune response to COVID-19 owing to the vaccine also having an expiration time, it is important to look at alternative options to protect against COVID-19. In this regard, bioactive substances commonly found in food or food additives present a viable option to shield against consequential COVID-19 infection. We investigate 10 bioactive plant products for possible antiviral use against the SARS-CoV-2 virus, which causes COVID-19 infection. We test these compounds by in silico docking to the Spike glycoprotein, one of the major determinants of COVID-19 infection. The AutoDock Vina software was used to scan and score the docking sites on a Spike protein ectodomain model. The top twenty hits were saved for each of the ten compounds and then the common and unique docking sites were delineated noting the putative binding affinity in each case. The results show that all ten plant products are high-affinity binders to the Spike protein, the S2 domain being the primary binding site. Very few binding interactions are found on the receptor binding domain, which means that topically used of these molecules such as in nasal spray would not be effective. In the ingestible form, the compounds can bind to the Spike molecule and disable it from driving virus-host fusion, its main function. It can thereby limit the cell-to-cell spread of the virus thus enforcing localization and clearance by the host immune system.

Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning.

Genomic data analysis is a fundamental system for monitoring pathogen evolution and the outbreak of infectious diseases. Based on bioinformatics and deep learning, this study was designed to identify the genomic variability of SARS-CoV-2 worldwide and predict the impending mutation rate. Analysis of 259044 SARS-CoV-2 isolates identified 3334545 mutations with an average of 14.01 mutations per isolate. Globally, single nucleotide polymorphism (SNP) is the most prevalent mutational event. The prevalence of C > T (52.67%) was noticed as a major alteration across the world followed by the G > T (14.59%) and A > G (11.13%). Strains from India showed the highest number of mutations (48) followed by Scotland, USA, Netherlands, Norway, and France having up to 36 mutations. D416G, F106F, P314L, UTR:C241T, L93L, A222V, A199A, V30L, and A220V mutations were found as the most frequent mutations. D1118H, S194L, R262H, M809L, P314L, A8D, S220G, A890D, G1433C, T1456I, R233C, F263S, L111K, A54T, A74V, L183A, A316T, V212F, L46C, V48G, Q57H, W131R, G172V, Q185H, and Y206S missense mutations were found to largely decrease the structural stability of the corresponding proteins. Conversely, D3L, L5F, and S97I were found to largely increase the structural stability of the corresponding proteins. Multi-nucleotide mutations GGG > AAC, CC > TT, TG > CA, and AT > TA have come up in our analysis which are in the top 20 mutational cohort. Future mutation rate analysis predicts a 17%, 7%, and 3% increment of C > T, A > G, and A > T, respectively in the future. Conversely, 7%, 7%, and 6% decrement is estimated for T > C, G > A, and G > T mutations, respectively. T > G\A, C > G\A, and A > T\C are not anticipated in the future. Since SARS-CoV-2 is mutating continuously, our findings will facilitate the tracking of mutations and help to map the progression of the COVID-19 intensity worldwide.

Correlation of COVID-19 with Parkinson's disease and life expectancy indicates female population predominance in future

Neurological diseases are prevalent in the populations from the developed nations including Europe and North America, while South America also shows a high prevalence of Parkinson's disease (PD). Although. PD is among the most prevalent neurodegenerative conditions, its cause remains largely unknown. Changing age structure and marked demographic shifts, with progressively larger percentage of their populations entering old age, has been seen in many countries. Females have been found to have a higher life expectancy and longevity than males at any age. We compiled the freely available data on COVID-19, and statistics on life expectancy, and ageing population from the United Nations. The XY-scatter plots with regression analysis were used to assess the correlation between case fatality rate (CFR)/deaths and life expectancy of various countries. We infer that the SARS-00V-2 mediated infections mostly affected the elderly people of age 60+years, who accounted for approximately 50% (20-88.45%) of the total deaths by COVID-19. However, females were found to be 1.66 times less prone to COVID-19-induced deaths compared to the males. The X Y-scatter plot showed no correlation between life expectancy and CFR or deaths due to COVID-19. Similar patterns of CFR/deaths by COVID-19 and PD prevalence were also observed in Europe and America. All the factual data including increased susceptibility of males to COVID-19 and PD, along with relatively less life expectancy than females, indicate that the world may virtually be heading towards a predominantly female older population. However, caution may be exercised in interpreting the results of this preliminary study that may be affected by incorrect or biased reporting on COVID-19 data, regional variations in the infectivity by new mutant strains of SARC-COV-2, and the prevalence and epidemiology of PD and COVID-19 might also affect the associated risk factors for PD in certain population.

A formally verified authentication protocol in secure framework for mobile healthcare during COVID-19-like pandemic

Existing schemes in the realm of mobile healthcare (also, e-Healthcare) based on cloud and IoMT (Internet of Medical Things) do not ensure end-to-end security and are not compliant with HIPAA (Health Insurance Portability and Accountability Act). It is also very difficult often for these schemes to obtain evidence from the cloud in case of security breaches. In addition to these issues, mobile healthcare applications are prone to various types of attacks and formal proof is often unavailable. In this work, we propose our community cloud framework in an IoMT setting that ensures end-to-end security and circumvents many of the existing negative aspects using the Trusted Platform Module (TPM). We provide necessary proofs using BAN logic and Scyther tool. Also, we show that the energy consumption and the costs of communication and computation for our proposed protocol are far less than that of the existing protocols. We have implemented our protocol using Kotlin language in Android Studio ensuring all the required security properties. © 2020 Informa UK Limited, trading as Taylor & Francis Group.