Computational basis of SARS-CoV 2 main protease inhibition: an insight from molecular dynamics simulation based findings.

The coronavirus disease 2019 (COVID-19) pandemic is caused by newly discovered severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). One of the striking targets amongst all the proteins in coronavirus is the main protease (Mpro), as it plays vital biological roles in replication and maturation of the virus, and hence the potential target. The aim of this study is to repurpose the Food and Drug Administration (FDA) approved molecules via computer-aided drug designing against Mpro (PDB ID: 6Y2F) of SARS CoV-2 due to its high x-ray resolution of 1.95 Å as compared to other published Mprostructures. High Through Virtual Screening (HTVS) of 2456 FDA approved drugs using structure-based docking were analyzed. Molecular Dynamics simulations were performed to check the overall structural stability (RMSD), Cα fluctuations (RMSF) and protein-ligand interactions. Further, trajectory analysis was performed to assess the binding quality by exploiting the protein-residue motion cross correlation (DCCM) and binding free energy (MM/GBSA). Tenofovir, an antiretroviral for HIV-proteases and Terlipressin, a vasoconstrictor show stable RMSD, RMSF, better MM/GBSA with good cross correlation as compared to the Apo and O6K. Moreover, the results show concurrence with Nelfinavir, Lopinavir and Ritonavir which have shown significant inhibition in in vitro studies. Therefore, we conclude that Tenofovir and Terlipresssin might also show protease inhibition but are still open to clinical validation in case of SARS-CoV 2 treatment.Communicated by Ramaswamy H. Sarma.

Analysis of the spread of COVID-19 in Ukraine using Google Trends tools

The paper is devoted to the analysis of the spread of the COVID-19 pandemic in Ukraine based on finding the correlation between search terms in Google search engine and laboratory-confirmed cases. Statistics were obtained from open sources. The analysis was performed on matrices based on the Pearson correlation coefficient. To do this, we analyzed 25 typical search phrases, and after grouping them-7 remained. The data were reduced to the same discreteness. Correlation matrices were calculated for each wave of the pandemic and for altogether. As a result, the correlation between search phrases and laboratory-confirmed cases was observed only in the second and third waves of the pandemic. Moreover, in the first wave, the preconditions for its occurrence were found;in the second-Pearson's correlation coefficient was 0.74, and in the third wave, it decreased to 0.57. Other correlations that are specific to each pandemic wave are also analyzed. Additionally, it was proved that polynomials of the 6th degree most effectively restore lost data. © 2022 IEEE.

The Impact of Covid-19 on Online Final Exam Scores Among Computer Science Students

The main goal of this research is to identify the impact of COVID-19 on online final exam scores among Computer Science students. The correlation matrix we used indicates the interrelationships among learning outcomes and student profile, type of classes and student online behaviour. Six courses were taken under consideration: Practical Algorithms, Discrete Mathematics, Software Engineering, Programming, Team Projects and Artificial Intelligence. A total of 4,988 final exam results were examined. After a deep analysis of the literature on the topic, we expected two scenarios. The first scenario constituted a decline in passing grades due to challenges such as: learning platform failures, poor internet connections or poorer quality of lessons due to teachers' lack of online competence. We hypothesized the second scenario as extraordinary student performance compared to their prior exams, but due to their dishonesty. The results of the study revealed that neither of the scenarios took place. It turned out that the challenges that seemed to be the most difficult ultimately did not matter. The present study finds that there is not a significant difference in the students' final exam performance between their online and traditional courses. Our strategy as described in this article has demonstrated a smooth transition from traditional to online teaching and assessment in terms of the final assessment. © 2022, Academic Conferences and Publishing International Limited. All rights reserved.

Factors influencing plasma donation behavior of COVID-19 recovered patients in Bangladesh: A pilot study.

Background and Aim: The COVID-19 pandemic has plagued our lives for more than 2 years, and the preference for convalescent plasma (CP) as a life-saving treatment since CP has proven as a potential therapeutic option for acute COVID-19 patients who were suffering from severe disease. It is important to identify which factors are associated with plasma donation. Therefore, this study aimed to assess the associated factors for CP donation to COVID-19 patients. Methods: A cross-sectional study was conducted online from December 21, 2021 to February 15, 2022 to identify different socio-demographic factors and knowledge related to CP donation. People who recovered from the COVID-19 infections and those who are willing to participate were included in the study. A total of 60 participants were included in the study. The data were analyzed using descriptive statistics, correlation matrix, and factor analysis. Results: The analysis results confirm that 41.67% (n = 25) of the participants aged 26-30 years; among the recovered patients, only about 23% (n = 14) of the participants donated plasma. Though 97% (n = 58) of the participants agreed to donate plasma when it will be needed, however, when someone asked to donate plasma then 76.67% (n = 46) of the patients declined it. Findings depict that gender had a weak positive relationship with ever decline in plasma donation at 5% level of significance and the age of the participants inversely related to plasma donation. Conclusion: Almost all the recovered participants were willing to donate plasma, however, due to a lack of knowledge and misconception, relatively few people actually did. This study reemphasizes the importance of health education to overcome the misconception about plasma donation, which is crucial for the treatment of COVID-19 infection.

Pharmacophore model-aided virtual screening combined with comparative molecular docking and molecular dynamics for identification of marine natural products as SARS-CoV-2 papain-like protease inhibitors.

Targeting SARS-CoV-2 papain-like protease using inhibitors is a suitable approach for inhibition of virus replication and dysregulation of host anti-viral immunity. Engaging all five binding sites far from the catalytic site of PLpro is essential for developing a potent inhibitor. We developed and validated a structure-based pharmacophore model with 9 features of a potent PLpro inhibitor. The pharmacophore model-aided virtual screening of the comprehensive marine natural product database predicted 66 initial hits. This hit library was downsized by filtration through a molecular weight filter of ≤ 500 g/mol. The 50 resultant hits were screened by comparative molecular docking using AutoDock and AutoDock Vina. Comparative molecular docking enables benchmarking docking and relieves the disparities in the search and scoring functions of docking engines. Both docking engines retrieved 3 same compounds at different positions in the top 1 % rank, hence consensus scoring was applied, through which CMNPD28766, aspergillipeptide F emerged as the best PLpro inhibitor. Aspergillipeptide F topped the 50-hit library with a pharmacophore-fit score of 75.916. Favorable binding interactions were predicted between aspergillipeptide F and PLpro similar to the native ligand XR8-24. Aspergillipeptide F was able to engage all the 5 binding sites including the newly discovered BL2 groove, site V. Molecular dynamics for quantification of Cα-atom movements of PLpro after ligand binding indicated that it exhibits highly correlated domain movements contributing to the low free energy of binding and a stable conformation. Thus, aspergillipeptide F is a promising candidate for pharmaceutical and clinical development as a potent SARS-CoV-2 PLpro inhibitor.

Computational investigation of peptidomimetics as potential inhibitors of SARS-CoV-2 spike protein.

Several variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were observed since the outbreak of the global pandemic at the end of 2019. The trimeric spike glycoprotein of the SARS-CoV-2 virus is crucial for the viral access to the host cell by interacting with the human angiotensin converting enzyme 2 (ACE2). Most of the mutations take place in the receptor-binding domain (RBD) of the S1 subunit of the trimeric spike glycoprotein. In this work, we targeted both S1 and S2 subunits of the spike protein in the wild type (WT) and the Omicron variant guided by the interaction of the neutralizing monoclonal antibodies. Virtual screening of two different peptidomimetics databases, ChEMBL and ChemDiv databases, was carried out against both S1 and S2 subunits. The use of these two databases provided diversity and enhanced the chance of finding protein-protein interaction inhibitors (PPIIs). Multi-layered filtration, based on physicochemical properties and docking scores, of nearly 114,000 compounds found in the ChEMBL database and nearly 14,000 compounds in the ChemDiv database was employed. Four peptidomimetics compounds were effective against both the WT and the Omicron S1 subunit with the minimum binding free energy of -25 kcal/mol. Five peptidomimetics compounds were effective against the S2 subunit with the minimum binding free energy of -19 kcal/mol. The dynamical cross-correlation matrix insinuated that the mutations of the RBD in the Omicron variant of the SARS-CoV-2 virus altered the correlated conformational motion of the different regions of the protein.Communicated by Ramaswamy H. Sarma.

The analysis of cross-correlation between Istanbul Stock Exchange and major stock markets and indices: An empirical analysis using Random Matrix Theory

This study attempts to investigate the cross-correlation between stocks listed under the XU100 index of Borsa Istanbul with several ratios and indices of the stock markets worldwide by using the Random Matrix Theory approach through a correlation matrix. In addition, Eigenvector Analysis, Network Analysis, Dimension Reduction will be carried out to investigate cross-correlation between markets. It was found that XU100, which is an index that includes 100 stocks highest in volume, has a distinguishing behavior compared to other indices and rates in terms of eigenvalue and related eigenvector structures. Furthermore, mean-value portfolio analysis showed that the empirical correlation matrix underestimates the portfolio risks than the correlation matrix obtained by filtering the noise. Coronavirus pandemic also affected Borsa Istanbul by breaking periodic behavior of volatility and correlation cycle. © 2022 John Wiley & Sons, Ltd.

Review of Big Data Analytics and Machine Learning Models for Contract Tracking and Detecting of COVID-19 Pandemic Cases

The pandemic COVID-19 is an infectious disease discovered first in China on December 19 and spread very fast over countries in the world where millions of people are infected. This is a deadly virus, which affects each facet of everyday lives. The novel leading Big Data applications have provoked in several areas are utilized in outbreak prediction, tracking of virus spread and prevent by the diagnosis of COVID-19. In addition, the techniques of Machine Learning (ML) have been employed commonly for various domains, which are already a huge market to ML-aided diagnostic systems in COVID-19 monitoring, predicting of virus spread and diagnosis or treatment of COVID-19 to determine the potential cure. Hence, this research focused on maintaining its significance in leading to the outbreak of COVID-19 and in mitigating the serious possessions of COVID-19. Initially, this paper has presented an outline of COVID-19 followed by the application of big data and ML towards fighting against COVID-19. Subsequently, it highlights the problems and challenges related to advance d solutions which help in finding out the advantage and disadvantages of recent techniques for controlling an efficient contract tracking and generating an outbreak of the COVID-19 situation. In this paper correlation matrix tool is proposed to identify the disease with minimal features. Only the test is being used to evaluate the condition because symptoms are many and inaccurate. The detection of disease is much enhanced by combining a machine learning predictive model with a correlation matrix tool. A correlation matrix is a technique that is used in the analysation of certain attributes. The correlation value between the feature values is determined, which improves the accuracy of the output. © 2022 IEEE.

Nature's therapy for COVID-19: Targeting the vital non-structural proteins (NSP) from SARS-CoV-2 with phytochemicals from Indian medicinal plants.

Background: Containing COVID-19 is still a global challenge. It has affected the "normal" world by targeting its economy and health sector. The effect is shifting of focus of research from life threatening diseases like cancer. Thus, we need to develop a medical solution at the earliest. The purpose of this present work was to understand the efficacy of 22 rationally screened phytochemicals from Indian medicinal plants obtained from our previous work, following drug-likeness properties, against 6 non-structural-proteins (NSP) from SARS-CoV-2. Methods: 100 ns molecular dynamics simulations were performed, and relative binding free energies were computed by MM/PBSA. Further, principal component analysis, dynamic cross correlation and hydrogen bond occupancy were analyzed to characterize protein-ligand interactions. Biological pathway enrichment analysis was also carried out to elucidate the therapeutic targets of the phytochemicals in comparison to SARS-CoV-2. Results: The potential binding modes and favourable molecular interaction profile of 9 phytochemicals, majorly from Withania somnifera with lowest free binding energies, against the SARS-CoV-2 NSP targets were identified. It was understood that phytochemicals and 2 repurposed drugs with steroidal moieties in their chemical structures formed stable interactions with the NSPs. Additionally, human target pathway analysis for SARS-CoV-2 and phytochemicals showed that cytokine mediated pathway and phosphorylation pathways were with the most significant p-value. Conclusions: To summarize this work, we suggest a global approach of targeting multiple proteins of SARS-CoV-2 with phytochemicals as a natural alternative therapy for COVID-19. We also suggest that these phytochemicals need to be tested experimentally to confirm their efficacy.

Word Clustering Using Graphical Lasso-Guided PCA for Trend Analysis of COVID-19

This paper proposes a word clustering method using graphical lasso-guided principal component analysis (PCA) for trend analysis of coronavirus disease (COVID-19). We define changes in daily frequencies of words on Twitter as trends, and clustering denotes to find similar trends. There is a problem that trends based on indirect correlations degrade the clustering performance. To address this problem, we newly develop graphical lasso-guided PCA. Specifically, graphical lasso is able to obtain a partial correlation matrix (a graph that represents direct correlations between trends). By calculating loadings of PCA to the partial correlation matrix (authority scores calculated by a hyperlink-induced topic search algorithm), accurate clustering becomes feasible. We conducted experiments by collecting Japanese tweets about COVID-19 from March 1, 2020 to April 30, 2020. The results show that our graphical lasso-guided PCA can distinguish two clusters before and after a state of emergency, unlike comparative method using indirect correlations. © 2021 IEEE.

Network Autoregressive Model for the Prediction of COVID-19 Considering the Disease Interaction in Neighboring Countries.

Predicting the way diseases spread in different societies has been thus far documented as one of the most important tools for control strategies and policy-making during a pandemic. This study is to propose a network autoregressive (NAR) model to forecast the number of total currently infected cases with coronavirus disease 2019 (COVID-19) in Iran until the end of December 2021 in view of the disease interactions within the neighboring countries in the region. For this purpose, the COVID-19 data were initially collected for seven regional nations, including Iran, Turkey, Iraq, Azerbaijan, Armenia, Afghanistan, and Pakistan. Thenceforth, a network was established over these countries, and the correlation of the disease data was calculated. Upon introducing the main structure of the NAR model, a mathematical platform was subsequently provided to further incorporate the correlation matrix into the prediction process. In addition, the maximum likelihood estimation (MLE) was utilized to determine the model parameters and optimize the forecasting accuracy. Thereafter, the number of infected cases up to December 2021 in Iran was predicted by importing the correlation matrix into the NAR model formed to observe the impact of the disease interactions in the neighboring countries. In addition, the autoregressive integrated moving average (ARIMA) was used as a benchmark to compare and validate the NAR model outcomes. The results reveal that COVID-19 data in Iran have passed the fifth peak and continue on a downward trend to bring the number of total currently infected cases below 480,000 by the end of 2021. Additionally, 20%, 50%, 80% and 95% quantiles are provided along with the point estimation to model the uncertainty in the forecast.

Structural and conformational analysis of SARS CoV 2 N-CTD revealing monomeric and dimeric active sites during the RNA-binding and stabilization: Insights towards potential inhibitors for N-CTD.

The advent of SARS-CoV-2 has become a universal health issue with no appropriate cure available to date. The coronavirus nucleocapsid (N) protein combines viral genomic RNA into a ribonucleoprotein and protects the viral genome from the host's nucleases. Structurally, the N protein comprises two independent domains: the N-terminal domain (NTD) for RNA-binding and C-terminal domain (CTD) involved in RNA-binding, protein dimerization, and nucleocapsid stabilization. The present study explains the structural aspects associated with the involvement of nucleocapsid C-terminal domain in the subunit assembly that helps the RNA binding and further stabilizing the virus assembly by protecting RNA from the hosts exonucleases degradation. The molecular dynamics (MD) simulations of the N-CTD and RNA complex suggests two active sites (site I: a monomer) and (site II: a dimer) with structural stability (RMSD: ~2 Å), Cα fluctuations (RMSF: ~3 Å) and strong protein-ligand interactions were estimated through the SiteMap module of Schrodinger. Virtual screening of 2456 FDA-approved drugs using structure-based docking identified top two leads distinctively against Site-I (monomer): Ceftaroline fosamil (MM-GBSA = -47.12 kcal/mol) and Cefoperazone (-45.84 kcal/mol); and against Site-II (dimer): Boceprevir, (an antiviral protease inhibitor, -106.78 kcal/mol) and Ceftaroline fosamil (-99.55 kcal/mol). The DCCM and PCA of drugs Ceftaroline fosamil (PC1+PC2 = 71.9%) and Boceprevir (PC1 +PC2 = 61.6%) show significant correlated residue motions which suggests highly induced conformational changes in the N-CTD dimer. Therefore, we propose N-CTD as a druggable target with two active binding sites (monomer and dimer) involved in specific RNA binding and stability. The RNA binding site with Ceftaroline fosamil binding can prevent viral assembly and can act as an antiviral for coronavirus.