Genome-Based Multi-Antigenic Epitopes Vaccine Construct Designing against Staphylococcus hominis Using Reverse Vaccinology and Biophysical Approaches.

Staphylococcus hominis is a Gram-positive bacterium from the staphylococcus genus; it is also a member of coagulase-negative staphylococci because of its opportunistic nature and ability to cause life-threatening bloodstream infections in immunocompromised patients. Gram-positive and opportunistic bacteria have become a major concern for the medical community. It has also drawn the attention of scientists due to the evaluation of immune evasion tactics and the development of multidrug-resistant strains. This prompted the need to explore novel therapeutic approaches as an alternative to antibiotics. The current study aimed to develop a broad-spectrum, multi-epitope vaccine to control bacterial infections and reduce the burden on healthcare systems. A computational framework was designed to filter the immunogenic potent vaccine candidate. This framework consists of pan-genomics, subtractive proteomics, and immunoinformatics approaches to prioritize vaccine candidates. A total of 12,285 core proteins were obtained using a pan-genome analysis of all strains. The screening of the core proteins resulted in the selection of only two proteins for the next epitope prediction phase. Eleven B-cell derived T-cell epitopes were selected that met the criteria of different immunoinformatics approaches such as allergenicity, antigenicity, immunogenicity, and toxicity. A vaccine construct was formulated using EAAAK and GPGPG linkers and a cholera toxin B subunit. This formulated vaccine construct was further used for downward analysis. The vaccine was loop refined and improved for structure stability through disulfide engineering. For an efficient expression, the codons were optimized as per the usage pattern of the E coli (K12) expression system. The top three refined docked complexes of the vaccine that docked with the MHC-I, MHC-II, and TLR-4 receptors were selected, which proved the best binding potential of the vaccine with immune receptors; this was followed by molecular dynamic simulations. The results indicate the best intermolecular bonding between immune receptors and vaccine epitopes and that they are exposed to the host's immune system. Finally, the binding energies were calculated to confirm the binding stability of the docked complexes. This work aimed to provide a manageable list of immunogenic and antigenic epitopes that could be used as potent vaccine candidates for experimental in vivo and in vitro studies.

High prevalence of an alpha variant lineage with a premature stop codon in ORF7a in Iraq, winter 2020-2021.

Since the first reported case of coronavirus disease 2019 (COVID-19) in China, SARS-CoV-2 has been spreading worldwide. Genomic surveillance of SARS-CoV-2 has had a critical role in tracking the emergence, introduction, and spread of new variants, which may affect transmissibility, pathogenicity, and escape from infection or vaccine-induced immunity. As anticipated, the rapid increase in COVID-19 infections in Iraq in February 2021 is due to the introduction of variants of concern during the second wave of the COVID-19 pandemic. To understand the molecular epidemiology of SARS-CoV-2 during the second wave in Iraq (2021), we sequenced 76 complete SARS-CoV-2 genomes using NGS technology and identified genomic mutations and proportions of circulating variants among these. Also, we performed an in silico study to predict the effect of the truncation of NS7a protein (ORF7a) on its function. We detected nine different lineages of SARS-CoV-2. The B.1.1.7 lineage was predominant (80.20%) from February to May 2021, while only one B.1.351 strain was detected. Interestingly, the phylogenetic analysis showed that multiple strains of the B.1.1.7 lineage clustered closely with those from European countries. A notable frequency (43.33%) of stop codon mutation (NS7a Q62stop) was detected among the B.1.1.7 lineage sequences. In silico analysis of NS7a with Q62stop found that this stop codon had no considerable effect on the function of NS7a. This work provides molecular epidemiological insights into the spread variants of SARS-CoV-2 in Iraq, which are most likely imported from Europe.

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review.

The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.

Computational Determination of Potential Multiprotein Targeting Natural Compounds for Rational Drug Design Against SARS-COV-2.

SARS-CoV-2 caused the current COVID-19 pandemic and there is an urgent need to explore effective therapeutics that can inhibit enzymes that are imperative in virus reproduction. To this end, we computationally investigated the MPD3 phytochemical database along with the pool of reported natural antiviral compounds with potential to be used as anti-SARS-CoV-2. The docking results demonstrated glycyrrhizin followed by azadirachtanin, mycophenolic acid, kushenol-w and 6-azauridine, as potential candidates. Glycyrrhizin depicted very stable binding mode to the active pocket of the Mpro (binding energy, -8.7 kcal/mol), PLpro (binding energy, -7.9 kcal/mol), and Nucleocapsid (binding energy, -7.9 kcal/mol) enzymes. This compound showed binding with several key residues that are critical to natural substrate binding and functionality to all the receptors. To test docking prediction, the compound with each receptor was subjected to molecular dynamics simulation to characterize the molecule stability and decipher its possible mechanism of binding. Each complex concludes that the receptor dynamics are stable (Mpro (mean RMSD, 0.93 Å), PLpro (mean RMSD, 0.96 Å), and Nucleocapsid (mean RMSD, 3.48 Å)). Moreover, binding free energy analyses such as MMGB/PBSA and WaterSwap were run over selected trajectory snapshots to affirm intermolecular affinity in the complexes. Glycyrrhizin was rescored to form strong affinity complexes with the virus enzymes: Mpro (MMGBSA, -24.42 kcal/mol and MMPBSA, -10.80 kcal/mol), PLpro (MMGBSA, -48.69 kcal/mol and MMPBSA, -38.17 kcal/mol) and Nucleocapsid (MMGBSA, -30.05 kcal/mol and MMPBSA, -25.95 kcal/mol), were dominated mainly by vigorous van der Waals energy. Further affirmation was achieved by WaterSwap absolute binding free energy that concluded all the complexes in good equilibrium and stability (Mpro (mean, -22.44 kcal/mol), PLpro (mean, -25.46 kcal/mol), and Nucleocapsid (mean, -23.30 kcal/mol)). These promising findings substantially advance our understanding of how natural compounds could be shaped to counter SARS-CoV-2 infection.

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic.

While the world has experience with many different types of infectious diseases, the current crisis related to the spread of COVID-19 has challenged epidemiologists and public health experts alike, leading to a rapid search for, and development of, new and innovative solutions to combat its spread. The transmission of this virus has infected more than 18.92 million people as of August 6, 2020, with over half a million deaths across the globe; the World Health Organization (WHO) has declared this a global pandemic. A multidisciplinary approach needs to be followed for diagnosis, treatment and tracking, especially between medical and computer sciences, so, a common ground is available to facilitate the research work at a faster pace. With this in mind, this survey paper aimed to explore and understand how and which different technological tools and techniques have been used within the context of COVID-19. The primary contribution of this paper is in its collation of the current state-of-the-art technological approaches applied to the context of COVID-19, and doing this in a holistic way, covering multiple disciplines and different perspectives. The analysis is widened by investigating Artificial Intelligence (AI) approaches for the diagnosis, anticipate infection and mortality rate by tracing contacts and targeted drug designing. Moreover, the impact of different kinds of medical data used in diagnosis, prognosis and pandemic analysis is also provided. This review paper covers both medical and technological perspectives to facilitate the virologists, AI researchers and policymakers while in combating the COVID-19 outbreak.

A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images.

Corona virus disease (COVID-19) acknowledged as a pandemic by the WHO and mankind all over the world is vulnerable to this virus. Alternative tools are needed that can help in diagnosis of the coronavirus. Researchers of this article investigated the potential of machine learning methods for automatic diagnosis of corona virus with high accuracy from X-ray images. Two most commonly used classifiers were selected: logistic regression (LR) and convolutional neural networks (CNN). The main reason was to make the system fast and efficient. Moreover, a dimensionality reduction approach was also investigated based on principal component analysis (PCA) to further speed up the learning process and improve the classification accuracy by selecting the highly discriminate features. The deep learning-based methods demand large amount of training samples compared to conventional approaches, yet adequate amount of labelled training samples was not available for COVID-19 X-ray images. Therefore, data augmentation technique using generative adversarial network (GAN) was employed to further increase the training samples and reduce the overfitting problem. We used the online available dataset and incorporated GAN to have 500 X-ray images in total for this study. Both CNN and LR showed encouraging results for COVID-19 patient identification. The LR and CNN models showed 95.2-97.6% overall accuracy without PCA and 97.6-100% with PCA for positive cases identification, respectively.

Promising terpenes as SARS-CoV-2 spike receptor-binding domain (RBD) attachment inhibitors to the human ACE2 receptor: Integrated computational approach.

The spike protein receptor binding domain (S-RBD) is a necessary corona-viral protein for binding and entry of coronaviruses (COVs) into the host cells. Hence, it has emerged as an attractive antiviral drug target. Therefore, present study was aimed to target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) S-RBD with novel bioactive compounds to retrieve potential candidates that could serve as anti-coronavirus disease 2019 (COVID-19) drugs. In this paper, computational approaches were employed, especially the structure-based virtual screening followed by molecular dynamics (MD) simulation as well as binding energy analysis for the computational identification of specific terpenes from the medicinal plants, which can block SARS-CoV-2 S-RBD binding to Human angiotensin-converting enzyme 2 (H-ACE2) and can act as potent anti-COVID-19 drugs after further advancements. The screening of focused terpenes inhibitors database composed of ~1000 compounds with reported therapeutic potential resulted in the identification of three candidate compounds, NPACT01552, NPACT01557 and NPACT00631. These three compounds established conserved interactions, which were further explored through all-atom MD simulations, free energy calculations, and a residual energy contribution estimated by MM-PB(GB)SA method. All these compounds showed stable conformation and interacted well with the hot-spot residues of SARS-CoV-2 S-RBD. Conclusively, the reported SARS-CoV-2 S-RBD specific terpenes could serve as seeds for developing potent anti-COVID-19 drugs. Importantly, the experimentally tested glycyrrhizin (NPACT00631) against SARS-CoV could be used further in the fast-track drug development process to help curb COVID-19.