Epitope-based chimeric peptide vaccine design against S, M and E proteins of SARS-CoV-2, the etiologic agent of COVID-19 pandemic: an in silico approach

Immunoinformatics and reverse vaccinomic approaches for effective design

The emergence of mutagenic strains of severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) worst hit the world which already suffered from the Coronavirus disease-2019 (COVID-19) pandemic for 2 years. Due to recent advances in vaccinomics, many vaccine candidates are available but their efficacy against a mutant version of SARS-CoV-2 has remained uncertain. The immune-informatics-based reverse vaccinomic approaches have shown promising investigations recently for the development of cost-effective vaccinomics candidates in a very short period of time. The strategic vaccine development of selected epitopes using artificial intelligence for both B- and T-cells is a very crucial step in this process. This approach provides a highly effective and immunogenic vaccine that offers immunological safety against autoimmunity and other adverse effects over ethnicities, pregnant women, and vulnerable age groups. Several researchers have developed effective vaccine candidates using computational vaccinology and the immune-informatics approach. In this process, a unique peptide sequence of viral proteins such as Nucleocapsid, spike, envelope protein was identified by various in silico tools which are acting as immunological epitopes against TLRs, T-cells, and B-cells. While the conventional immunological vaccine studies take years for vaccine candidature, the immunoinformatics approach is a time-efficient way for the next generation research to study host-pathogen interactions and vaccine development. It is also cost-effective and leads to a better understanding of disease pathogenesis, diagnosis, and immunological response. Owing to the advantage of immunoinformatics-based vaccine approaches the present chapter aimed to discuss vaccine development using immunoinformatics approaches. Besides, the current challenges and future aspects have also been discussed herewith.

Genomic characterization of the dominating Beta variant carrying vaccinated (Oxford-AstraZeneca) and non-vaccinated COVID-19 patient samples in Bangladesh: A metagenomics and whole genome approach

Bangladesh experiences a second wave of COVID-19 since March 2021, despite the nationwide vaccination drive with ChAdOx1 (Oxford-AstraZeneca) vaccine from early February 2021. Here, we characterized 19 nasopharyngeal swab (NPS) samples from COVID-19 suspect patients using genomic and metagenomic approach. Screening for SARS-CoV-2 by RT-PCR and metagenomic sequencing revealed 17 samples of COVID-19 positive (vaccinated=10, non-vaccinated=7) and 2 samples of COVID-19 negative. We did not find any significant correlation between associated factors including vaccination status, age or sex of the patients, diversity or abundance of the co-infected organisms/pathogens, and the abundance of SARS-CoV-2. Though the first wave of the pandemic was dominated by clade 20B, Beta,V2 (South African variant) dominated the second wave (January 2021 to May 2021), while the third wave (May 2021 to September 2021) was responsible for Delta variants of the epidemic in Bangladesh including both vaccinated and unvaccinated infections. Noteworthy, the RBD region of S protein of all the isolates harbored similar substitutions including K417N, E484K and N501Y that signify the Beta, while D614G, D215G, D80A, A67V, L18F and A701V substitutions were commonly found in the non-RBD region of Spike proteins. ORF7b and ORF3a genes underwent a positive selection (dN/dS ratio 1.77 and 1.24, respectively), while the overall S protein of the Bangladeshi SARS-CoV-2 isolates underwent negative selection pressure (dN/dS=0.621). Furthermore, we found different bacterial co-infection like Streptococcus agalactiae, Neisseria meningitidis, Elizabethkingia anophelis, Stenotrophomonas maltophilia, Klebsiella pneumoni and Pseudomonas plecoglossicida, expressing a number of antibiotic resistance genes such as tetA and tetM. Overall, this approach provides valuable insights on the SARS-CoV-2 genomes and microbiome composition from both vaccinated and non-vaccinated patients in Bangladesh. This article is protected by copyright. All rights reserved.

Pandemic strategies with computational and structural biology against COVID-19: A retrospective

The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic, has dominated all aspects of life since of 2020. Research studies on the virus and exploration of therapeutic and preventive strategies has been moving at rapid rates to control the pandemic. In the field of bioinformatics or computational and structural biology, recent research strategies have used multiple disciplines to compile large datasets to uncover statistical correlations and significance, visualize and model proteins, perform molecular dynamics simulations, and employ the help of artificial intelligence and machine learning to harness computational processing power to further the research on COVID-19, including drug screening, drug design, vaccine development, prognosis prediction, and outbreak prediction. These recent developments should help us better understand the viral disease and develop the much-needed therapies and strategies for the management of COVID-19.

The critical role of mesenchymal stromal/stem cell therapy in COVID-19 patients: An updated review

New coronavirus disease 2019 (COVID-19), as a pandemic disaster, has drawn the attention of researchers in various fields to discover suitable therapeutic approaches for the management of COVID-19 patients. Currently, there are many worries about the rapid spread of COVID-19; there is no approved treatment for this infectious disease, despite many efforts to develop therapeutic procedures for COVID-19. Emerging evidence shows that mesenchymal stromal/stem cell (MSC) therapy can be a suitable option for the management of COVID-19. These cells have many biological features (including the potential of differentiation, high safety and effectiveness, secretion of trophic factors and immunoregulatory features) that make them suitable for the treatment of various diseases. However, some studies have questioned the positive role of MSC therapy in the treatment of COVID-19. Accordingly, in this paper, we will focus on the therapeutic impacts of MSCs and their critical role in cytokine storm of COVID-19 patients.

EpiCurator: an immunoinformatic workflow to predict and prioritize SARS-CoV-2 epitopes

The ongoing coronavirus 2019 (COVID-19) pandemic, triggered by the emerging SARS-CoV-2 virus, represents a global public health challenge. Therefore, the development of effective vaccines is an urgent need to prevent and control virus spread. One of the vaccine production strategies uses the in silico epitope prediction from the virus genome by immunoinformatic approaches, which assist in selecting candidate epitopes for in vitro and clinical trials research. This study introduces the EpiCurator workflow to predict and prioritize epitopes from SARS-CoV-2 genomes by combining a series of computational filtering tools. To validate the workflow effectiveness, SARS-CoV-2 genomes retrieved from the GISAID database were analyzed. We identified 11 epitopes in the receptor-binding domain (RBD) of Spike glycoprotein, an important antigenic determinant, not previously described in the literature or published on the Immune Epitope Database (IEDB). Interestingly, these epitopes have a combination of important properties: recognized in sequences of the current variants of concern, present high antigenicity, conservancy, and broad population coverage. The RBD epitopes were the source for a multi-epitope design to in silico validation of their immunogenic potential. The multi-epitope overall quality was computationally validated, endorsing its efficiency to trigger an effective immune response since it has stability, high antigenicity and strong interactions with Toll-Like Receptors (TLR). Taken together, the findings in the current study demonstrated the efficacy of the workflow for epitopes discovery, providing target candidates for immunogen development.

Development of synthetic antigen vaccines for COVID-19

The current pandemic called COVID-19 caused by the SARS-CoV-2 virus brought the need for the search for fast alternatives to both control and fight the SARS-CoV-2 infection. Therefore, a race for a vaccine against COVID-19 took place, and some vaccines have been approved for emergency use in several countries in a record time. Ongoing prophylactic research has sought faster, safer, and precise alternatives by redirecting knowledge of other vaccines, and/or the development of new strategies using available tools, mainly in the areas of genomics and bioinformatics. The current review highlights the development of synthetic antigen vaccines, focusing on the usage of bioinformatics tools for the selection and construction of antigens on the different vaccine constructions under development, as well as strategies to optimize vaccines for COVID-19.

Elhefnawi M, Ali MA. In Silico and In Vivo Evaluation of SARS-CoV-2 Predicted Epitopes-Based Candidate Vaccine

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2, the causative agent of coronavirus disease (COVID-19)) has caused relatively high mortality rates in humans throughout the world since its first detection in late December 2019, leading to the most devastating pandemic of the current century. Consequently, SARS-CoV-2 therapeutic interventions have received high priority from public health authorities. Despite increased COVID-19 infections, a vaccine or therapy to cover all the population is not yet available. Herein, immunoinformatics and custommune tools were used to identify B and T-cells epitopes from the available SARS-CoV-2 sequences spike (S) protein. In the in silico predictions, six B cell epitopes QTGKIADYNYK, TEIYQASTPCNGVEG, LQSYGFQPT, IRGDEVRQIAPGQTGKIADYNYKLPD, FSQILPDPSKPSKRS and PFAMQMAYRFNG were cross-reacted with MHC-I and MHC-II T-cells binding epitopes and selected for vaccination in experimental animals for evaluation as candidate vaccine(s) due to their high antigenic matching and conserved score. The selected six peptides were used individually or in combinations to immunize female Balb/c mice. The immunized mice raised reactive antibodies against SARS-CoV-2 in two different short peptides located in receptor binding domain and S2 region. In combination groups, an additive effect was demonstrated in-comparison with single peptide immunized mice. This study provides novel epitope-based peptide vaccine candidates against SARS-CoV-2.

Microbial Metabolites: The Emerging Hotspot of Antiviral Compounds as Potential Candidates to Avert Viral Pandemic Alike COVID-19

The present global COVID-19 pandemic caused by the noble pleomorphic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created a vulnerable situation in the global healthcare and economy. In this pandemic situation, researchers all around the world are trying their level best to find suitable therapeutics from various sources to combat against the SARS-CoV-2. To date, numerous bioactive compounds from different sources have been tested to control many viral diseases. However, microbial metabolites are advantageous for drug development over metabolites from other sources. We herein retrieved and reviewed literatures from PubMed, Scopus and Google relevant to antiviral microbial metabolites by searching with the keywords "antiviral microbial metabolites," "microbial metabolite against virus," "microorganism with antiviral activity," "antiviral medicine from microbial metabolite," "antiviral bacterial metabolites," "antiviral fungal metabolites," "antiviral metabolites from microscopic algae' and so on. For the same purpose, the keywords "microbial metabolites against COVID-19 and SARS-CoV-2" and "plant metabolites against COVID-19 and SARS-CoV-2" were used. Only the full text literatures available in English and pertinent to the topic have been included and those which are not available as full text in English and pertinent to antiviral or anti-SARS-CoV-2 activity were excluded. In this review, we have accumulated microbial metabolites that can be used as antiviral agents against a broad range of viruses including SARS-CoV-2. Based on this concept, we have included 330 antiviral microbial metabolites so far available to date in the data bases and were previously isolated from fungi, bacteria and microalgae. The microbial source, chemical nature, targeted viruses, mechanism of actions and IC50/EC50 values of these metabolites are discussed although mechanisms of actions of many of them are not yet elucidated. Among these antiviral microbial metabolites, some compounds might be very potential against many other viruses including coronaviruses. However, these potential microbial metabolites need further research to be developed as effective antiviral drugs. This paper may provide the scientific community with the possible secret of microbial metabolites that could be an effective source of novel antiviral drugs to fight against many viruses including SARS-CoV-2 as well as the future viral pandemics.

Major Insights in Dynamics of Host Response to SARS-CoV-2: Impacts and Challenges

The coronavirus disease 2019 (COVID-19), a pandemic declared by the World Health Organization on March 11, 2020, is caused by the infection of highly transmissible species of a novel coronavirus called severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). As of July 25, 2021, there are 194,372,584 cases and 4,167,937 deaths with high variability in clinical manifestations, disease burden, and post-disease complications among different people around the globe. Overall, COVID-19 is manifested as mild to moderate in almost 90% of the cases and only the rest 10% of the cases need hospitalization. However, patients with older age and those having different comorbidities have made worst the pandemic scenario. The variability of pathological consequences and clinical manifestations of COVID-19 is associated with differential host-SARS-CoV-2 interactions, which are influenced by the factors that originated from the SARS-CoV-2 and the host. These factors usually include the genomic attributes and virulent factors of the SARS-CoV-2, the burden of coinfection with other viruses and bacteria, age and gender of the individuals, different comorbidities, immune suppressions/deficiency, genotypes of major histocompatibility complex, and blood group antigens and antibodies. We herein retrieved and reviewed literatures from PubMed, Scopus, and Google relevant to clinical complications and pathogenesis of COVID-19 among people of different age, sex, and geographical locations; genomic characteristics of SARS-CoV-2 including its variants, host response under different variables, and comorbidities to summarize the dynamics of the host response to SARS-CoV-2 infection; and host response toward approved vaccines and treatment strategies against COVID-19. After reviewing a large number of published articles covering different aspects of host response to SARS-CoV-2, it is clear that one aspect from one region is not working with the scenario same to others, as studies have been done separately with a very small number of cases from a particular area/region of a country. Importantly, to combat such a pandemic as COVID-19, a conclusive understanding of the disease dynamics is required. This review emphasizes on the identification of the factors influencing the dynamics of host responses to SARS-CoV-2 and offers a future perspective to explore the molecular insights of COVID-19.

An in-depth in silico and immunoinformatics approach for designing a potential multi-epitope construct for the effective development of vaccine to combat against SARS-CoV-2 encompassing variants of concern and interest

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the latest of the several viral pathogens that have acted as a threat to human health around the world. Thus, to prevent COVID-19 and control the outbreak, the development of vaccines against SARS-CoV-2 is one of the most important strategies at present. The study aimed to design a multi-epitope vaccine (MEV) against SARS-CoV-2. For the development of a more effective vaccine, 1549 nucleotide sequences were taken into consideration, including the variants of concern (B.1.1.7, B.1.351, P.1 and, B.1.617.2) and variants of interest (B.1.427, B.1.429, B.1.526, B.1.617.1 and P.2). A total of 11 SARS-CoV-2 proteins (S, N, E, M, ORF1ab polyprotein, ORF3a, ORF6, ORF7a, ORF7b, ORF8, ORF10) were targeted for T-cell epitope prediction and S protein was targeted for B-cell epitope prediction. MEV was constructed using linkers and adjuvant beta-defensin. The vaccine construct was verified, based on its antigenicity, physicochemical properties, and its binding potential, with toll-like receptors (TLR2, TLR4), ACE2 receptor and B cell receptor. The selected vaccine construct showed considerable binding with all the receptors and a significant immune response, including elevated antibody titer and B cell population along with augmented activity of T_H cells, Tc cells and NK cells. Thus, immunoinformatics and in silico-based approaches were used for constructing MEV which is capable of eliciting both innate and adaptive immunity. In conclusion, the vaccine construct developed in this study has all the potential for the development of a next-generation vaccine which may in turn effectively combat the new variants of SARS-CoV-2 identified so far. However, in vitro and animal studies are warranted to justify our findings for its utility as probable preventive measure.

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2

It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing, and development, and motivate researchers in harnessing AI potentials in the fight against COVID-19.

The impact of curcumin derived polyphenols on the structure and flexibility COVID-19 main protease binding pocket: a molecular dynamics simulation study

The newly occurred SARS-CoV-2 caused a leading pandemic of coronavirus disease (COVID-19). Up to now it has infected more than one hundred sixty million and killed more than three million people according to 14 May 2021 World Health Organization report. So far, different types of studies have been conducted to develop an anti-viral drug for COVID-19 with no success yet. As part of this, silico were studied to discover and introduce COVID-19 antiviral drugs and results showed that protease inhibitors could be very effective in controlling. This study aims to investigate the binding affinity of three curcumin derived polyphenols against COVID-19 the main protease (Mpro), binding pocket, and identification of important residues for interaction. In this study, molecular modeling, auto-dock coupled with molecular dynamics simulations were performed to analyze the conformational, and stability of COVID-19 binding pocket with diferuloylmethane, demethoxycurcumin, and bisdemethoxycurcumin. All three compounds have shown binding affinity −39, −89 and −169.7, respectively. Demethoxycurcumin and bisdemethoxycurcumin showed an optimum binding affinity with target molecule and these could be one of potential ligands for COVID-19 therapy. And also, COVID-19 main protease binding pocket binds with the interface region by one hydrogen bond. Moreover, the MD simulation parameters indicated that demethoxycurcumin and bisdemethoxycurcumin were stable during the simulation run. These findings can be used as a baseline to develop therapeutics with curcumin derived polyphenols against COVID-19.

Temporal landscape of mutational frequencies in SARS-CoV-2 genomes of Bangladesh: possible implications from the ongoing outbreak in Bangladesh

Along with intrinsic evolution, adaptation to selective pressure in new environments might have resulted in the circulatory SARS-CoV-2 strains in response to the geoenvironmental conditions of a country and the demographic profile of its population. With this target, the current study traced the evolutionary route and mutational frequency of 198 Bangladesh-originated SARS-CoV-2 genomic sequences available in the GISAID platform over a period of 13 weeks as of 14 July 2020. The analyses were performed using MEGA X, Swiss Model Repository, Virus Pathogen Resource and Jalview visualization. Our analysis identified that majority of the circulating strains strikingly differ from both the reference genome and the first sequenced genome from Bangladesh. Mutations in nonspecific proteins (NSP2-3, NSP-12(RdRp), NSP-13(Helicase)), S-Spike, ORF3a, and N-Nucleocapsid protein were common in the circulating strains with varying degrees and the most unique mutations (UM) were found in NSP3 (UM-18). But no or limited changes were observed in NSP9, NSP11, Envelope protein (E) and accessory factors (NSP7a, ORF 6, ORF7b) suggesting the possible conserved functions of those proteins in SARS-CoV-2 propagation. However, along with D614G mutation, more than 20 different mutations in the Spike protein were detected basically in the S2 domain. Besides, mutations in SR-rich region of N protein and P323L in RDRP were also present. However, the mutation accumulation showed a significant association (p = 0.003) with sex and age of the COVID-19-positive cases. So, identification of these mutational accumulation patterns may greatly facilitate vaccine development deciphering the age and the sex-dependent differential susceptibility to COVID-19.

A systematic review on AI/ML approaches against COVID-19 outbreak

A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.

Microbial co-infections in COVID-19: Associated microbiota and underlying mechanisms of pathogenesis

The novel coronavirus infectious disease-2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has traumatized the whole world with the ongoing devastating pandemic. A plethora of microbial domains including viruses (other than SARS-CoV-2), bacteria, archaea and fungi have evolved together, and interact in complex molecular pathogenesis along with SARS-CoV-2. However, the involvement of other microbial co-pathogens and underlying molecular mechanisms leading to extortionate ailment in critically ill COVID-19 patients has yet not been extensively reviewed. Although, the incidence of co-infections could be up to 94.2% in laboratory-confirmed COVID-19 cases, the fate of co-infections among SARS-CoV-2 infected hosts often depends on the balance between the host's protective immunity and immunopathology. Predominantly identified co-pathogens of SARS-CoV-2 are bacteria such as Streptococcus pneumoniae, Staphylococcus aureus, Klebsiella pneumoniae, Haemophilus influenzae, Mycoplasma pneumoniae, Acinetobacter baumannii, Legionella pneumophila and Clamydia pneumoniae followed by viruses including influenza, coronavirus, rhinovirus/enterovirus, parainfluenza, metapneumovirus, influenza B virus, and human immunodeficiency virus. The cross-talk between co-pathogens (especially lung microbiomes), SARS-CoV-2 and host is an important factor that ultimately increases the difficulty of diagnosis, treatment, and prognosis of COVID-19. Simultaneously, co-infecting microbiotas may use new strategies to escape host defense mechanisms by altering both innate and adaptive immune responses to further aggravate SARS-CoV-2 pathogenesis. Better understanding of co-infections in COVID-19 is critical for the effective patient management, treatment and containment of SARS-CoV-2. This review therefore necessitates the comprehensive investigation of commonly reported microbial co-pathogens amid COVID-19, their transmission pattern along with the possible mechanism of co-infections and outcomes. Thus, identifying the possible co-pathogens and their underlying molecular mechanisms during SARS-CoV-2 pathogenesis may shed light in developing diagnostics, appropriate curative and preventive interventions for suspected SARS-CoV-2 respiratory infections in the current pandemic.

Genome-Wide Analysis of Codon Usage Patterns of SARS-CoV-2 Virus Reveals Global Heterogeneity of COVID-19

The ongoing outbreak of coronavirus disease COVID-19 is significantly implicated by global heterogeneity in the genome organization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The causative agents of global heterogeneity in the whole genome of SARS-CoV-2 are not well characterized due to the lack of comparative study of a large enough sample size from around the globe to reduce the standard deviation to the acceptable margin of error. To better understand the SARS-CoV-2 genome architecture, we have performed a comprehensive analysis of codon usage bias of sixty (60) strains to get a snapshot of its global heterogeneity. Our study shows a relatively low codon usage bias in the SARS-CoV-2 viral genome globally, with nearly all the over-preferred codons' A.U. ended. We concluded that the SARS-CoV-2 genome is primarily shaped by mutation pressure; however, marginal selection pressure cannot be overlooked. Within the A/U rich virus genomes of SARS-CoV-2, the standard deviation in G.C. (42.91% ± 5.84%) and the GC3 value (30.14% ± 6.93%) points towards global heterogeneity of the virus. Several SARS-CoV-2 viral strains were originated from different viral lineages at the exact geographic location also supports this fact. Taking all together, these findings suggest that the general root ancestry of the global genomes are different with different genome's level adaptation to host. This research may provide new insights into the codon patterns, host adaptation, and global heterogeneity of SARS-CoV-2.

Immunoinformatics-guided design of a multi-epitope vaccine based on the structural proteins of severe acute respiratory syndrome coronavirus 2

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulting in a contagious respiratory tract infection that has become a global burden since the end of 2019. Notably, fewer patients infected with SARS-CoV-2 progress from acute disease onset to death compared with the progression rate associated with two other coronaviruses, SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV). Several research organizations and pharmaceutical industries have attempted to develop successful vaccine candidates for the prevention of COVID-19. However, increasing evidence indicates that the SARS-CoV-2 genome undergoes frequent mutation; thus, an adequate analysis of the viral strain remains necessary to construct effective vaccines. The current study attempted to design a multi-epitope vaccine by utilizing an approach based on the SARS-CoV-2 structural proteins. We predicted the antigenic T- and B-lymphocyte responses to four structural proteins after screening all structural proteins according to specific characteristics. The predicted epitopes were combined using suitable adjuvants and linkers, and a secondary structure profile indicated that the vaccine shared similar properties with the native protein. Importantly, the molecular docking analysis and molecular dynamics simulations revealed that the constructed vaccine possessed a high affinity for toll-like receptor 4 (TLR4). In addition, multiple descriptors were obtained from the simulation trajectories, including the root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), and radius of gyration (R g), demonstrating the rigid nature and inflexibility of the vaccine and receptor molecules. In addition, codon optimization, based on Escherichia coli K12, was used to determine the GC content and the codon adaptation index (CAI) value, which further followed for the incorporation into the cloning vector pET28+(a). Collectively, these findings suggested that the constructed vaccine could be used to modulate the immune reaction against SARS-CoV-2.

Diversity and genomic determinants of the microbiomes associated with COVID-19 and non-COVID respiratory diseases

The novel coronavirus disease 2019 (COVID-19) is a rapidly emerging and highly transmissible disease caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Understanding the microbiomes associated with the upper respiratory tract infection (URTI), chronic obstructive pulmonary disease (COPD) and COVID-19 diseases has clinical interest. We hypothesize that microbiome diversity and composition, and their genomic features are associated with different pathological conditions of these human respiratory tract diseases. To test this hypothesis, we analyzed 21 RNASeq metagenomic data including eleven COVID-19 (BD = 6 and China = 5), six COPD (UK = 6) and four URTI (USA = 4) samples to unravel the microbiome diversity and related genomic metabolic functions. The metagenomic data mapped to 534 bacterial, 60 archaeal and 61 viral genomes with distinct variation in the microbiome composition across the samples (COVID-19 > COPD > URTI). Notably, 94.57%, 80.0% and 24.59% bacterial, archaeal and viral genera shared between the COVID-19 and non-COVID samples, respectively. However, the COVID-19 related samples had sole association with 16 viral genera other than SARS-CoV-2. Strain-level virome profiling revealed 660 and 729 strains in COVID-19 and non-COVID samples, respectively, and of them 34.50% strains shared between the conditions. Functional annotation of the metagenomic data identified the association of several biochemical pathways related to basic metabolism (amino acid and energy), ABC transporters, membrane transport, virulence, disease and defense, regulation of virulence, programmed cell death, and primary immunodeficiency. We also detected 30 functional gene groups/classes associated with resistance to antibiotics and toxic compounds (RATC) in both COVID-19 and non-COVID microbiomes. Furthermore, we detected comparatively higher abundance of cobalt-zinc-cadmium resistance (CZCR) and multidrug resistance to efflux pumps (MREP) genes in COVID-19 metagenome. The profiles of microbiome diversity and associated microbial genomic features found in both COVID-19 and non-COVID (COPD and URTI) samples might be helpful in developing microbiome-based diagnostics and therapeutics for COVID-19 and non-COVID respiratory diseases. However, future studies might be carried out to explore the microbiome dynamics and the cross-talk between host and microbiomes employing larger volume of samples from different ethnic groups and geoclimatic conditions.

Current and prospective computational approaches and challenges for developing COVID-19 vaccines

SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.

Unraveling the molecular crosstalk between Atherosclerosis and COVID-19 comorbidity

Background

Corona virus disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus −2 (SARS-CoV-2) has created ruckus throughout the world. Growing epidemiological studies have depicted atherosclerosis as a comorbid factor of COVID-19. Though both these diseases are triggered via inflammatory rage that leads to injury of healthy tissues, the molecular linkage between them and their co-influence in causing fatality is not yet understood.

Methods

We have retrieved the data of differentially expressed genes (DEGs) for both atherosclerosis and COVID-19 from publicly available microarray and RNA-Seq datasets. We then reconstructed the protein-protein interaction networks (PPIN) for these diseases from protein-protein interaction data of corresponding DEGs. Using RegNetwork and TRRUST, we mapped the transcription factors (TFs) in atherosclerosis and their targets (TGs) in COVID-19 PPIN.

Results

From the atherosclerotic PPIN, we have identified 6 hubs (TLR2, TLR4, EGFR, SPI1, MYD88 and IRF8) as differentially expressed TFs that might control the expression of their 17 targets in COVID-19 PPIN. The important target proteins include IL1B, CCL5, ITGAM, IFIT3, CXCL1, CXCL2, CXCL3 and CXCL8. Consequent functional enrichment analysis of these TGs have depicted inflammatory responses to be overrepresented among the gene sets.

Conclusion

Finally, analyzing the DEGs in cardiomyocytes infected with SARS-CoV-2, we have concluded that MYD88 is a crucial linker of atherosclerosis and COVID-19, the co-existence of which lead to fatal outcomes. Anti-inflammatory therapy targeting MYD88 could be a potent strategy for combating this comorbidity.

Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance

COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspective. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus.

Mutational analysis and assessment of its impact on proteins of SARS-CoV-2 genomes from India

Mutational status of SARS-CoV-2 genomes from India along with their impact on proteins was ascertained through multiple tools including MEGA, Genome Detective, SIFT, PROVEAN and ws-SNPs&GO. Excluding gaps and ambiguous sequences, 493 variable sites (152 parsimony informative and 341 singleton) were observed. NSP3 had the highest incidence of 101 sites followed by S protein (74), NSP12b (43) and ORF3a (31). Average mutations per sample for males and females was 2.56 and 2.88 respectively. Non-uniform geographical distribution of mutations suggests that sequences in some regions are mutating faster than others. There were 281 mutations (198 Neutral and 83 Disease) affecting amino acid sequence. NSP13 has a maximum of 14 Disease variants followed by S protein and ORF3a with 13 each. Disease mutations in genomes from asymptomatic people was mere 11% but those from deceased patients was at 38% indicating contribution of these mutations to the pathophysiology of the SARS-CoV-2.

Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

COVID-19 and Toll-Like Receptor 4 (TLR4): SARS-CoV-2 May Bind and Activate TLR4 to Increase ACE2 Expression, Facilitating Entry and Causing Hyperinflammation

Causes of mortality from COVID-19 include respiratory failure, heart failure, and sepsis/multiorgan failure. TLR4 is an innate immune receptor on the cell surface that recognizes pathogen-associated molecular patterns (PAMPs) including viral proteins and triggers the production of type I interferons and proinflammatory cytokines to combat infection. It is expressed on both immune cells and tissue-resident cells. ACE2, the reported entry receptor for SARS-CoV-2, is only present on ~1-2% of the cells in the lungs or has a low pulmonary expression, and recently, the spike protein has been proposed to have the strongest protein-protein interaction with TLR4. Here, we review and connect evidence for SARS-CoV-1 and SARS-CoV-2 having direct and indirect binding to TLR4, together with other viral precedents, which when combined shed light on the COVID-19 pathophysiological puzzle. We propose a model in which the SARS-CoV-2 spike glycoprotein binds TLR4 and activates TLR4 signalling to increase cell surface expression of ACE2 facilitating entry. SARS-CoV-2 also destroys the type II alveolar cells that secrete pulmonary surfactants, which normally decrease the air/tissue surface tension and block TLR4 in the lungs thus promoting ARDS and inflammation. Furthermore, SARS-CoV-2-induced myocarditis and multiple-organ injury may be due to TLR4 activation, aberrant TLR4 signalling, and hyperinflammation in COVID-19 patients. Therefore, TLR4 contributes significantly to the pathogenesis of SARS-CoV-2, and its overactivation causes a prolonged or excessive innate immune response. TLR4 appears to be a promising therapeutic target in COVID-19, and since TLR4 antagonists have been previously trialled in sepsis and in other antiviral contexts, we propose the clinical trial testing of TLR4 antagonists in the treatment of severe COVID-19. Also, ongoing clinical trials of pulmonary surfactants in COVID-19 hold promise since they also block TLR4.

In silico analysis of epitope-based vaccine candidate against tuberculosis using reverse vaccinology

Tuberculosis (TB) kills more individuals in the world than any other disease, and a threat made direr by the coverage of drug-resistant strains of Mycobacterium tuberculosis (Mtb). Bacillus Calmette-Guérin (BCG) is the single TB vaccine licensed for use in human beings and effectively protects infants and children against severe military and meningeal TB. We applied advanced computational techniques to develop a universal TB vaccine. In the current study, we select the very conserved, experimentally confirmed Mtb antigens, including Rv2608, Rv2684, Rv3804c (Ag85A), and Rv0125 (Mtb32A) to design a novel multi-epitope subunit vaccine. By using the Immune Epitopes Database (IEDB), we predicted different B-cell and T-cell epitopes. An adjuvant (Griselimycin) was also added to vaccine construct to improve its immunogenicity. Bioinformatics tools were used to predict, refined, and validate the 3D structure and then docked with toll-like-receptor (TLR-3) using different servers. The constructed vaccine was used for further processing based on allergenicity, antigenicity, solubility, different physiochemical properties, and molecular docking scores. The in silico immune simulation results showed significant response for immune cells. For successful expression of the vaccine in E. coli, in-silico cloning and codon optimization were performed. This research also sets out a good signal for the design of a peptide-based tuberculosis vaccine. In conclusion, our findings show that the known multi-epitope vaccine may activate humoral and cellular immune responses and maybe a possible tuberculosis vaccine candidate. Therefore, more experimental validations should be exposed to it.

Development of an in silico multi-epitope vaccine against SARS-COV-2 by précised immune-informatics approaches

The coronavirus family has been infecting the human population for the past two decades, but the ongoing coronavirus called SARS-CoV-2 has posed an enigmatic challenge to global public health security. Since last year, the mutagenic quality of this virus is causing changes to its genetic material. To prevent those situations, the FDA approved some emergency vaccines but there is no assurance that these will function properly in the complex human body system. In point of view, a short but efficient effort has made in this study to develop an immune epitope-based therapy for the rapid exploitation of SARS-CoV-2 by applying in silico structural biology and advancing immune information strategies. The antigenic epitopes were screened from the Surface, Membrane, Envelope proteins of SARS-CoV-2 and passed through several immunological filters to determine the best possible one. According to this, 7CD4+, 10CD8+ and 5 B-cell epitopes were found to be prominent, antigenic, immunogenic, and most importantly, highly conserved among 128 Bangladeshi and 110 other infected countries SARS-CoV-2 variants. After that, the selected epitopes and adjuvant were linked to finalize the multi-epitope vaccine by appropriate linkers. The immune simulation disclosed that the engineered vaccine could activate both humoral and innate immune responses. For the prediction of an effective binding, molecular docking was carried out between the vaccine and immunological receptors (TLRs). Strong binding affinity and good docking scores clarified the stringency of the vaccines. Furthermore, MD simulation was performed within the highest binding affinity complex to observe the stability. Codon optimization and other physicochemical properties revealed that the vaccine would be suitable for a higher expression at cloning level. So, monitoring the overall in silico assessment, we anticipated that our engineered vaccine would be a plausible prevention against COVID-19.

The immunology of SARS-CoV-2 infection, the potential antibody based treatments and vaccination strategies

Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a potentially fatal agent for a new emerging viral disease (COVID-19) is of great global public health emergency. Herein, we represented potential antibody-based treatments especially monoclonal antibodies (mAbs) that may exert a potential role in treatment as well as developing vaccination strategies against COVID-19.Areas covered: We used PubMed, Google Scholar, and clinicaltrials.gov search strategies for relevant papers. We demonstrated some agents with potentially favorable efficacy as well as favorable safety. Several therapies are under assessment to evaluate their efficacy and safety for COVID19. However, the development of different strategies such as SARS-CoV-2-based vaccines and antibody therapy are urgently required beside other effective therapies such as plasma, anticoagulants, and immune as well as antiviral therapies. We encourage giving more attention to antibody-based treatments as an immediate strategy. Although there has not been any approved specific vaccine until now, developing vaccination strategies may have a protective effect against COVID-19.Expert opinion: An antiviral mAbs could be a safe and high-quality therapeutic intervention which is greatly recommended for COVID-19. Additionally, the high sequence homology between the SARS-CoV-2 and SARS/MERS viruses could shed light on developing to design a vaccine against SARS-CoV-2.

Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches

Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory coronavirus 2 (SARS-COV-2) is a significant threat to global health security. Till date, no completely effective drug or vaccine is available to cure COVID-19. Therefore, an effective vaccine against SARS-COV-2 is crucially needed. This study was conducted to design an effective multiepitope based vaccine (MEV) against SARS-COV-2. Seven highly antigenic proteins of SARS-COV-2 were selected as targets and different epitopes (B-cell and T-cell) were predicted. Highly antigenic and overlapping epitopes were shortlisted. Selected epitopes indicated significant interactions with the HLA-binding alleles and 99.93% coverage of the world's population. Hence, 505 amino acids long MEV was designed by connecting 16 MHC class I and eleven MHC class II epitopes with suitable linkers and adjuvant. MEV construct was non-allergenic, antigenic, stable and flexible. Furthermore, molecular docking followed by molecular dynamics (MD) simulation analyses, demonstrated a stable and strong binding affinity of MEV with human pathogenic toll-like receptors (TLR), TLR3 and TLR8. Finally, MEV codons were optimized for its in silico cloning into Escherichia coli K-12 system, to ensure its increased expression. Designed MEV in present study could be a potential candidate for further vaccine production process against COVID-19. However, to ensure its safety and immunogenic profile, the proposed MEV needs to be experimentally validated.

Artificial Intelligence in the Fight Against COVID-19: Scoping Review

BACKGROUND

In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts.

OBJECTIVE

This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation.

METHODS

A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data.

RESULTS

We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome-related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine.

CONCLUSIONS

The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.

Mutational insights into the envelope protein of SARS-CoV-2

The ongoing mutations in the structural proteins of SARS-CoV-2 are the major impediment for prevention and control of the COVID-19 disease. Presently we focused on evolution of the envelope (E) protein, one of the most enigmatic and less studied protein among the four structural proteins (S, E, M and N) associated with multitude of immunopathological functions of SARS-CoV-2. In the present study, we comprehensively analyzed 81,818 high quality E protein sequences of SARS-CoV-2 globally available in the GISAID database as of 20 August 2020. Compared to Wuhan reference strain, our mutational analysis explored only 1.2 % (982/81818) mutant strains undergoing a total of 115 unique amino acid (aa) substitutions in the E protein, highlighting the fact that most (98.8 %) of the E protein of SARS-CoV-2 strains are highly conserved. Moreover, we found 58.77 % (134 of 228) nucleotides (nt) positions of SARS-CoV-2 E gene encountering a total of 176 unique nt-level mutations globally, which may affect the efficacy of real time RT-PCR-based molecular detection of COVID-19. Importantly, higher aa variations observed in the C-terminal domain (CTD) of the E protein, particularly at Ser⁵⁵-Phe⁵⁶, Arg⁶⁹ and the C-terminal end (DLLV: 72–75) may alter the binding of SARS-CoV-2 Envelope protein to tight junction-associated PALS1 and thus could play a key role in COVID-19 pathogenesis. Furthermore, this study revealed the V25A mutation in the transmembrane domain which is a key factor for the homopentameric conformation of E protein. Our analysis also observed a triple cysteine motif harboring mutation (L39M, A41S, A41V, C43F, C43R, C43S, C44Y, N45R) which may hinder the binding of E protein with spike glycoprotein. These results therefore suggest the continuous monitoring of the structural proteins including the envelope protein of SARS-CoV-2 since the number of genome sequences from across the world are continuously increasing.

Molecular characterization, pathogen-host interaction pathway and in silico approaches for vaccine design against COVID-19

COVID-19 has forsaken the world because of extremely high infection rates and high mortality rates. At present we have neither medicine nor vaccine to prevent this pandemic. Lockdowns, curfews, isolations, quarantines, and social distancing are the only ways to mitigate their infection. This is badly affecting the mental health of people. Hence, there is an urgent need to address this issue. Coronavirus disease 2019 (COVID-19) is caused by a novel Betacorona virus named SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) which has emerged in the city of Wuhan in China and declared a pandemic by WHO since it affected almost all the countries the world, infected 24,182,030 people and caused 825,798 death as per data are compiled from John Hopkins University (JHU). The genome of SARS-CoV-2 has a single-stranded positive (+) sense RNA of ∼30 kb nucleotides. Phylogenetic analysis reveals that SARS-CoV-2 shares the highest nucleotide sequence similarity (∼79 %) with SARS-CoV. Envelope and nucleocapsids are two evolutionary conserved regions of SARS-CoV-2 having a sequence identity of about 96 % and 89.6 %, respectively as compared to SARS-CoV. The characterization of SARS-CoV-2 is based on polymerase chain reaction (PCR) and metagenomic next-generation sequencing. Transmission of this virus in the human occurs through the respiratory tract and decreases the respiration efficiency of lungs. Humans are generally susceptible to SARS-CoV-2 with an incubation period of 2-14 days. The virus first infects the lower airway and bind with angiotensin-converting enzyme 2 (ACE2) of alveolar epithelial cells. Due to the unavailability of drugs or vaccines, it is very urgent to design potential vaccines or drugs for COVID-19. Reverse vaccinology and immunoinformatic play an important role in designing potential vaccines against SARS-CoV-2. The suitable vaccine selects for SARS-CoV-2 based on binding energy between the target protein and the designed vaccine. The stability and activity of the designed vaccine can be estimated by using molecular docking and dynamic simulation approaches. This review mainly focused on the brief up to date information about COVID-19, molecular characterization, pathogen-host interaction pathways involved during COVID-19 infection. It also covers potential vaccine design against COVID-19 by using various computational approaches. SARS-CoV-2 enters brain tissue through the different pathway and harm human's brain and causes severe neurological disruption.

Spike protein-based epitopes predicted against SARS-CoV-2 through literature mining

BACKGROUND

With the diffusion of SARS-CoV-2 around the world, human health is being threatened. As there is no effective vaccine yet, the development of the vaccine is urgently in progress.

MATERIALS AND METHODS

Immunoinformatics methods were applied to predict epitopes from the Spike protein through mining literature associated with B- and T-cell epitopes prediction published or preprinted since the outbreak of the virus till June 1, 2020. 3D structure of the Spike protein were obtained (PDB ID: 6VSB) for prediction of discontinuous B-cell epitopes and localization of epitopes in the hotspot regions.

RESULTS

Methods provided by the Immune Epitope Database (IEDB) server were the most frequently used to predict epitopes. Sequence alignment of the epitopes extracted from literature with the Spike protein demonstrated that the epitopes in different studies converged to multiple short hotspot regions. There were three hotspot regions found in RBD of the Spike protein harboring B-cell linear epitopes ('RQIAPGQTGKIADYNYKLPD', 'SYGFQPTNGVGYQ' and 'YAWNRKRISNCVA') predicted to have high antigenicity score. Two T-cell epitopes ('KPFERDISTEIYQ' and 'NYNYLYRLFR') predicted to be highly antigenic in the original studies were discovered in the hotspot region. Toxicity and allergenicity analysis confirmed all the five epitopes are of non-toxin, and four of them are of non-allergen. The five epitopes identified in hotspot regions of RBD were found fully exposed based on the 3D structure of the Spike protein.

CONCLUSION

The five epitopes we discovered from literature mining may be potential candidates for diagnostics and vaccine development against SARS-CoV-2.

Genomic exploration light on multiple origin with potential parsimony-informative sites of the severe acute respiratory syndrome coronavirus 2 in Bangladesh

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of beta coronavirus that has spread worldwide within a short period of time and has been responsible for the current COVID-19 pandemic. This novel virus shows high transmission and adaptability frequency into the host with rapid changes in genomic sequences. In this study, we analyzed the complete genome of 41 strains isolated in Bangladesh to understand the evolutionary route and genetic variations of this rapidly evolving virus. The phylogenetics, parsimony informative sites and mutation analyses were performed using MEGA X, Multiple sequence alignment program (MAFFT), and Virus Pathogen Resource. The phylogenetic analysis of the studied genomes along with the reference genome suggested that the viral strains found in Bangladesh might be coming from multiple countries such as France, Germany, India, the USA, and Brazil. After entering into the country, intra-cluster and inter-cluster began to circulate in the 8 individual divisions of Bangladesh. We also identified 26 parsimony-informative sites along with the 9 most important sites for virus evolution. Genome-wide annotations revealed 256 mutations, of which 10 were novel (NSP3, RdRp, Spike) in Bangladeshi strains where I120F(NSP2), P323L(RdRp), D614G (Spike), R203K, G204R(N) are the most prominent. Most importantly, numerous mutations were flourishing in the N protein gene (67) followed by S (45), RdRp (38), NSP2 (34), NSP3 (20), and ORF8 (6) gene. Moreover, nucleotide deletion analysis found nine deletions throughout the genomes including in ORF7a (8), ORF8 (1) with one insertion (G) at 265 positions in only one genome. The underlying mechanism of disease severity, molecular evolution, and epidemiology lie in genomic sequences that are not fully understood yet. Identification of the evolutionary history, parsimony-informative sites and others genetic variations of this deadly virus will facilitate the development of new strategies to control the local transmission and provide deep insight in the identification of potential therapeutic targets for controlling COVID-19.

Immunoinformatic design of a COVID-19 subunit vaccine using entire structural immunogenic epitopes of SARS-CoV-2

Coronavirus disease 2019 (COVID-19) is an acute pneumonic disease, with no prophylactic or specific therapeutical solution. Effective and rapid countermeasure against the spread of the disease’s associated virus, SARS-CoV-2, requires to incorporate the computational approach. In this study, we employed various immunoinformatics tools to design a multi-epitope vaccine polypeptide with the highest potential for activating the human immune system against SARS-CoV-2. The initial epitope set was extracted from the whole set of viral structural proteins. Potential non-toxic and non-allergenic T-cell and B-cell binding and cytokine inducing epitopes were then identified through a priori prediction. Selected epitopes were bound to each other with appropriate linkers, followed by appending a suitable adjuvant to increase the immunogenicity of the vaccine polypeptide. Molecular modelling of the 3D structure of the vaccine construct, docking, molecular dynamics simulations and free energy calculations confirmed that the vaccine peptide had high affinity for Toll-like receptor 3 binding, and that the vaccine-receptor complex was highly stable. As our vaccine polypeptide design captures the advantages of structural epitopes and simultaneously integrates precautions to avoid relevant side effects, it is suggested to be promising for elicitation of an effective and safe immune response against SARS-CoV-2 in vivo.

Evolutionary Analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Reveals Genomic Divergence with Implications for Universal Vaccine Efficacy

Coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the pressing contemporary public health challenges. Investigations into the genomic structure of SARS-CoV-2 may inform ongoing vaccine development efforts and/or provide insights into vaccine efficacy to fight against COVID-19. Evolutionary analysis of 540 genomes spanning 20 different countries/territories was conducted and revealed an increase in the genomic divergence across successive generations. The ancestor of the phylogeny was found to be the isolate from the 2019/2020 Wuhan outbreak. Its transmission was outlined across 20 countries/territories as per genomic similarity. Our results demonstrate faster evolving variations in the genomic structure of SARS-CoV-2 when compared to the isolates from early stages of the pandemic. Genomic alterations were predominantly located and mapped onto the reported vaccine candidates of structural genes, which are the main targets for vaccine candidates. S protein showed 34, N protein 25, E protein 2, and M protein 3 amino acid variations in 246 genomes among 540. Among identified mutations, 23 in S protein, 1 in E, 2 from M, and 7 from N protein were mapped with the reported vaccine candidates explaining the possible implications on universal vaccines. Hence, potential target regions for vaccines would be ideally chosen from the structural regions of the genome that lack high variation. The increasing variations in the genome of SARS-CoV-2 together with our observations in structural genes have important implications for the efficacy of a successful universal vaccine against SARS-CoV-2.

Comprehensive annotations of the mutational spectra of SARS-CoV-2 spike protein: a fast and accurate pipeline

Infecting millions of people, the SARS‐CoV‐2 is evolving at an unprecedented rate, demanding advanced and specified analytic pipeline to capture the mutational spectra. In order to explore mutations and deletions in the spike (S) protein — the most‐discussed protein of SARS‐CoV‐2 — we comprehensively analyzed 35,750 complete S protein‐coding sequences through a custom Python‐based pipeline. This GISAID‐collected dataset of until 24 June 2020 covered six continents and five major climate zones. We identified 27,801 (77.77% sequences) mutated strains compared to reference Wuhan‐Hu‐1 wherein 84.40% of these strains mutated by only a single amino acid (aa). An outlier strain (EPI_ISL_463893) from Bosnia and Herzegovina possessed six aa substitutions. We also identified 11 residues with high aa mutation frequency, and each contains four types of aa variations. The infamous D614G variant has spread worldwide with ever‐rising dominance and across regions with different climatic conditions alongside L5F and D936Y mutants, which have been documented throughout all regions and climate zones, respectively. We also found 988 unique aa substitutions spanned across 660 residues, which differed significantly among different continents (p = .003) and climatic zones (p = .021) as inferred with the Kruskal–Wallis test. Besides, 17 in‐frame deletions at four sites adjacent to receptor‐binding‐domain were determined that may have a possible impact on attenuation. This study provides a fast and accurate pipeline for identifying mutations and deletions from the large dataset for coding and also non‐coding sequences as evidenced by the representative analysis on existing S protein data. By using separate multi‐sequence alignment, removing ambiguous sequences and in‐frame stop codons, and utilizing pairwise alignment, this method can derive both synonymous and non‐synonymous mutations (strain_ID reference aa:mutation position:strain aa). We suggest that the pipeline will aid in the evolutionary surveillance of any SARS‐CoV‐2 encoded proteins and will prove to be crucial in tracking the ever‐increasing variation of many other divergent RNA viruses in the future. The code is available at https://github.com/SShaminur/Mutation-Analysis.

Genomic diversity and evolution, diagnosis, prevention, and therapeutics of the pandemic COVID-19 disease

The coronavirus disease 19 (COVID-19) is a highly transmittable and pathogenic viral infection caused by a novel evolutionarily divergent RNA virus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus first emerged in Wuhan, China in December 2019, and subsequently spreaded around the world. Genomic analyses revealed that this zoonotic virus may be evolved naturally but not a purposefully manipulated laboratory construct. However, currently available data are not sufficient to precisely conclude the origin of this fearsome virus. Comprehensive annotations of the whole-genomes revealed hundreds of nucleotides, and amino acids mutations, substitutions and/or deletions at different positions of the ever changing SARS-CoV-2 genome. The spike (S) glycoprotein of SARS-CoV-2 possesses a functional polybasic (furin) cleavage site at the S1-S2 boundary through the insertion of 12 nucleotides. It leads to the predicted acquisition of 3-O-linked glycan around the cleavage site. Although real-time RT-PCR methods targeting specific gene(s) have widely been used to diagnose the COVID-19 patients, however, recently developed more convenient, cheap, rapid, and specific diagnostic tools targeting antigens or CRISPR-Cas-mediated method or a newly developed plug and play method should be available for the resource-poor developing countries. A large number of candidate drugs, vaccines and therapies have shown great promise in early trials, however, these candidates of preventive or therapeutic agents have to pass a long path of trials before being released for the practical application against COVID-19. This review updates current knowledge on origin, genomic evolution, development of the diagnostic tools, and the preventive or therapeutic remedies of the COVID-19. We also discussed the future scopes for research, effective management, and surveillance of the newly emerged COVID-19 disease.

Genome-wide analysis of SARS-CoV-2 virus strains circulating worldwide implicates heterogeneity

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a novel evolutionary divergent RNA virus, is responsible for the present devastating COVID-19 pandemic. To explore the genomic signatures, we comprehensively analyzed 2,492 complete and/or near-complete genome sequences of SARS-CoV-2 strains reported from across the globe to the GISAID database up to 30 March 2020. Genome-wide annotations revealed 1,516 nucleotide-level variations at different positions throughout the entire genome of SARS-CoV-2. Moreover, nucleotide (nt) deletion analysis found twelve deletion sites throughout the genome other than previously reported deletions at coding sequence of the ORF8 (open reading frame), spike, and ORF7a proteins, specifically in polyprotein ORF1ab (n = 9), ORF10 (n = 1), and 3´-UTR (n = 2). Evidence from the systematic gene-level mutational and protein profile analyses revealed a large number of amino acid (aa) substitutions (n = 744), demonstrating the viral proteins heterogeneous. Notably, residues of receptor-binding domain (RBD) showing crucial interactions with angiotensin-converting enzyme 2 (ACE2) and cross-reacting neutralizing antibody were found to be conserved among the analyzed virus strains, except for replacement of lysine with arginine at 378th position of the cryptic epitope of a Shanghai isolate, hCoV-19/Shanghai/SH0007/2020 (EPI_ISL_416320). Furthermore, our results of the preliminary epidemiological data on SARS-CoV-2 infections revealed that frequency of aa mutations were relatively higher in the SARS-CoV-2 genome sequences of Europe (43.07%) followed by Asia (38.09%), and North America (29.64%) while case fatality rates remained higher in the European temperate countries, such as Italy, Spain, Netherlands, France, England and Belgium. Thus, the present method of genome annotation employed at this early pandemic stage could be a promising tool for monitoring and tracking the continuously evolving pandemic situation, the associated genetic variants, and their implications for the development of effective control and prophylaxis strategies.