Predicting binding affinities of emerging variants of SARS-CoV-2 using spike protein sequencing data: observations, caveats and recommendations.

Predicting protein properties from amino acid sequences is an important problem in biology and pharmacology. Protein-protein interactions among SARS-CoV-2 spike protein, human receptors and antibodies are key determinants of the potency of this virus and its ability to evade the human immune response. As a rapidly evolving virus, SARS-CoV-2 has already developed into many variants with considerable variation in virulence among these variants. Utilizing the proteomic data of SARS-CoV-2 to predict its viral characteristics will, therefore, greatly aid in disease control and prevention. In this paper, we review and compare recent successful prediction methods based on long short-term memory (LSTM), transformer, convolutional neural network (CNN) and a similarity-based topological regression (TR) model and offer recommendations about appropriate predictive methodology depending on the similarity between training and test datasets. We compare the effectiveness of these models in predicting the binding affinity and expression of SARS-CoV-2 spike protein sequences. We also explore how effective these predictive methods are when trained on laboratory-created data and are tasked with predicting the binding affinity of the in-the-wild SARS-CoV-2 spike protein sequences obtained from the GISAID datasets. We observe that TR is a better method when the sample size is small and test protein sequences are sufficiently similar to the training sequence. However, when the training sample size is sufficiently large and prediction requires extrapolation, LSTM embedding and CNN-based predictive model show superior performance.

Adaptation, spread and transmission of SARS-CoV-2 in farmed minks and associated humans in the Netherlands.

In the first wave of the COVID-19 pandemic (April 2020), SARS-CoV-2 was detected in farmed minks and genomic sequencing was performed on mink farms and farm personnel. Here, we describe the outbreak and use sequence data with Bayesian phylodynamic methods to explore SARS-CoV-2 transmission in minks and humans on farms. High number of farm infections (68/126) in minks and farm workers (>50% of farms) were detected, with limited community spread. Three of five initial introductions of SARS-CoV-2 led to subsequent spread between mink farms until November 2020. Viruses belonging to the largest cluster acquired an amino acid substitution in the receptor binding domain of the Spike protein (position 486), evolved faster and spread longer and more widely. Movement of people and distance between farms were statistically significant predictors of virus dispersal between farms. Our study provides novel insights into SARS-CoV-2 transmission between mink farms and highlights the importance of combining genetic information with epidemiological information when investigating outbreaks at the animal-human interface.

Boosting the analysis of protein interfaces with multiple interface string alignments: Illustration on the spikes of coronaviruses.

We introduce multiple interface string alignment (MISA), a visualization tool to display coherently various sequence and structure based statistics at protein-protein interfaces (SSE elements, buried surface area, ΔASA , B factor values, etc). The amino acids supporting these annotations are obtained from Voronoi interface models. The benefit of MISA is to collate annotated sequences of (homologous) chains found in different biological contexts, that is, bound with different partners or unbound. The aggregated views MISA/SSE, MISA/BSA, MISA/ΔASA, and so forth, make it trivial to identify commonalities and differences between chains, to infer key interface residues, and to understand where conformational changes occur upon binding. As such, they should prove of key relevance for knowledge-based annotations of protein databases such as the Protein Data Bank. Illustrations are provided on the receptor binding domain of coronaviruses, in complex with their cognate partner or (neutralizing) antibodies. MISA computed with a minimal number of structures complement and enrich findings previously reported. The corresponding package is available from the Structural Bioinformatics Library (http://sbl.inria.frand https://sbl.inria.fr/doc/Multiple_interface_string_alignment-user-manual.html).

Mutational Hotspot in the SARS-CoV-2 Spike Protein N-Terminal Domain Conferring Immune Escape Potential.

Global efforts are being made to monitor the evolution of SARS-CoV-2, aiming for early identification of genotypes providing increased infectivity or virulence. However, viral lineage-focused tracking might fail in early detection of advantageous mutations emerging independently across phylogenies. Here, the emergence patterns of Spike mutations were investigated in sequences deposited in local and global databases to identify mutational hotspots across phylogenies and we evaluated their impact on SARS-CoV-2 evolution. We found a striking increase in the frequency of recruitment of diverse substitutions at a critical residue (W152), positioned in the N-terminal domain (NTD) of the Spike protein, observed repeatedly across independent phylogenetic and geographical contexts. These mutations might have an impact on the evasion of neutralizing antibodies. Finally, we found that NTD is a region exhibiting particularly high frequency of mutation recruitments, suggesting an evolutionary path in which the virus maintains optimal efficiency of ACE2 binding combined with the flexibility facilitating the immune escape. We conclude that adaptive mutations, frequently present outside of the receptor-binding domain, can emerge in virtually any SARS-CoV-2 lineage and at any geographical location. Therefore, surveillance should not be restricted to monitoring defined lineages alone.

ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features.

MOTIVATION: The Estimation of Model Accuracy problem is a cornerstone problem in the field of Bioinformatics. As of CASP14, there are 79 global QA methods, and a minority of 39 residue-level QA methods with very few of them working on protein complexes. Here, we introduce ZoomQA, a novel, single-model method for assessing the accuracy of a tertiary protein structure/complex prediction at residue level, which have many applications such as drug discovery. ZoomQA differs from others by considering the change in chemical and physical features of a fragment structure (a portion of a protein within a radius $r$ of the target amino acid) as the radius of contact increases. Fourteen physical and chemical properties of amino acids are used to build a comprehensive representation of every residue within a protein and grade their placement within the protein as a whole. Moreover, we have shown the potential of ZoomQA to identify problematic regions of the SARS-CoV-2 protein complex. RESULTS: We benchmark ZoomQA on CASP14, and it outperforms other state-of-the-art local QA methods and rivals state of the art QA methods in global prediction metrics. Our experiment shows the efficacy of these new features and shows that our method is able to match the performance of other state-of-the-art methods without the use of homology searching against databases or PSSM matrices. AVAILABILITY: http://zoomQA.renzhitech.com.

Revelation of Potent Epitopes Present in Unannotated ORF Antigens of SARS-CoV-2 for Epitope-Based Polyvalent Vaccine Design Using Immunoinformatics Approach.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) kills thousands of people worldwide every day, thus necessitating rapid development of countermeasures. Immunoinformatics analyses carried out here in search of immunodominant regions in recently identified SARS-CoV-2 unannotated open reading frames (uORFs) have identified eight linear B-cell, one conformational B-cell, 10 CD4+ T-cell, and 12 CD8+ T-cell promising epitopes. Among them, ORF9b B-cell and T-cell epitopes are the most promising followed by M.ext and ORF3c epitopes. ORF9b40-48 (CD8+ T-cell epitope) is found to be highly immunogenic and antigenic with the highest allele coverage. Furthermore, it has overlap with four potent CD4+ T-cell epitopes. Structure-based B-cell epitope prediction has identified ORF9b61-68 to be immunodominant, which partially overlaps with one of the linear B-cell epitopes (ORF9b65-69). ORF3c CD4+ T-cell epitopes (ORF3c2-16, ORF3c3-17, and ORF3c4-18) and linear B-cell epitope (ORF3c14-22) have also been identified as the candidate epitopes. Similarly, M.ext and 7a.iORF1 (overlap with M and ORF7a) proteins have promising immunogenic regions. By considering the level of antigen expression, four ORF9b and five M.ext epitopes are finally shortlisted as potent epitopes. Mutation analysis has further revealed that the shortlisted potent uORF epitopes are resistant to recurrent mutations. Additionally, four N-protein (expressed by canonical ORF) epitopes are found to be potent. Thus, SARS-CoV-2 uORF B-cell and T-cell epitopes identified here along with canonical ORF epitopes may aid in the design of a promising epitope-based polyvalent vaccine (when connected through appropriate linkers) against SARS-CoV-2. Such a vaccine can act as a bulwark against SARS-CoV-2, especially in the scenario of emergence of variants with recurring mutations in the spike protein.

CATH: increased structural coverage of functional space.

CATH (https://www.cathdb.info) identifies domains in protein structures from wwPDB and classifies these into evolutionary superfamilies, thereby providing structural and functional annotations. There are two levels: CATH-B, a daily snapshot of the latest domain structures and superfamily assignments, and CATH+, with additional derived data, such as predicted sequence domains, and functionally coherent sequence subsets (Functional Families or FunFams). The latest CATH+ release, version 4.3, significantly increases coverage of structural and sequence data, with an addition of 65,351 fully-classified domains structures (+15%), providing 500 238 structural domains, and 151 million predicted sequence domains (+59%) assigned to 5481 superfamilies. The FunFam generation pipeline has been re-engineered to cope with the increased influx of data. Three times more sequences are captured in FunFams, with a concomitant increase in functional purity, information content and structural coverage. FunFam expansion increases the structural annotations provided for experimental GO terms (+59%). We also present CATH-FunVar web-pages displaying variations in protein sequences and their proximity to known or predicted functional sites. We present two case studies (1) putative cancer drivers and (2) SARS-CoV-2 proteins. Finally, we have improved links to and from CATH including SCOP, InterPro, Aquaria and 2DProt.

Sequence Characterization and Molecular Modeling of Clinically Relevant Variants of the SARS-CoV-2 Main Protease.

The SARS-CoV-2 main protease (Mpro) is essential to viral replication and cleaves highly specific substrate sequences, making it an obvious target for inhibitor design. However, as for any virus, SARS-CoV-2 is subject to constant neutral drift and selection pressure, with new Mpro mutations arising over time. Identification and structural characterization of Mpro variants is thus critical for robust inhibitor design. Here we report sequence analysis, structure predictions, and molecular modeling for seventy-nine Mpro variants, constituting all clinically observed mutations in this protein as of April 29, 2020. Residue substitution is widely distributed, with some tendency toward larger and more hydrophobic residues. Modeling and protein structure network analysis suggest differences in cohesion and active site flexibility, revealing patterns in viral evolution that have relevance for drug discovery.

BCG Vaccine Derived Peptides Induce SARS-CoV-2 T Cell Cross-Reactivity.

Epidemiological studies and clinical trials suggest Bacillus Calmette-Guérin (BCG) vaccine has protective effects against coronavirus disease 2019 (COVID-19). There are now over 30 clinical trials evaluating if BCG vaccination can prevent or reduce the severity of COVID-19. However, the mechanism by which BCG vaccination can induce severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific T cell responses is unknown. Here, we identify 8 novel BCG-derived peptides with significant sequence homology to either SARS-CoV-2 NSP3 or NSP13-derived peptides. Using an in vitro co-culture system, we show that human CD4+ and CD8+ T cells primed with a BCG-derived peptide developed enhanced reactivity to its corresponding homologous SARS-CoV-2-derived peptide. As expected, HLA differences between individuals meant that not all persons developed immunogenic responses to all 8 BCG-derived peptides. Nevertheless, all of the 20 individuals that were primed with BCG-derived peptides developed enhanced T cell reactivity to at least 7 of 8 SARS-CoV-2-derived peptides. These findings provide an in vitro mechanism that may account, in part, for the epidemiologic observation that BCG vaccination confers some protection from COVID-19.

Ferret Interferon (IFN)-Inducible Transmembrane Proteins Are Upregulated by both IFN-α and Influenza Virus Infection.

The current fears of a future influenza pandemic have resulted in an increased emphasis on the development and testing of novel therapeutic strategies against the virus. Fundamental to this is the ferret model of influenza infection, which is critical in examining pathogenesis and treatment. Nevertheless, a precise evaluation of the efficacy of any treatment strategy in ferrets is reliant on understanding the immune response in this model. Interferon-inducible transmembrane proteins (IFITMs) are interferon-stimulated proteins shown to be critically important in the host immune response against viral infections. These proteins confer intrinsic innate immunity to pH-dependent viruses such as influenza viruses and can inhibit cytosolic entry of such viruses to limit the severity of infection following interferon upregulation. Mutations in IFITM genes in humans have been identified as key risk factors for worsened disease progression, particularly in the case of avian influenza viruses such as H7N9. While the IFITM genes of humans and mice have been well characterized, no studies have been conducted to classify the IFITM locus and interferon-driven upregulation of IFITMs in ferrets. Here, we show the architecture of the ferret IFITM locus and its synteny to the IFITM locus of other mammalian and avian species. Furthermore, we show that ferret IFITM1, -2, and -3 are functionally responsive to both interferon-α (IFN-α) and influenza virus stimulation. Thus, we show that ferret IFITMs exhibit interferon-stimulated properties similar to those shown in other species, furthering our knowledge of the innate immune response in the ferret model of human influenza virus infections. IMPORTANCE IFITM proteins can prevent the entry of several pH-dependent viruses, including high-consequence viruses such as HIV, influenza viruses, and SARS-coronaviruses. Mutations in these genes have been associated with worsened disease outcomes with mutations in their IFITM genes, highlighting these genes as potential disease risk factors. Ferrets provide a valuable tool to model infectious diseases; however, there is a critical shortage of information regarding their interferon-stimulated genes. We identified the putative ferret IFITM genes and mapped their complete gene locus. Thus, our study fills a critical gap in knowledge and supports the further use of the ferret model to explore the importance of IFITMs in these important diseases.

Structural Basis and Function of the N Terminus of SARS-CoV-2 Nonstructural Protein 1.

Nonstructural protein 1 (Nsp1) of severe acute respiratory syndrome coronaviruses (SARS-CoVs) is an important pathogenic factor that inhibits host protein translation by means of its C terminus. However, its N-terminal function remains elusive. Here, we determined the crystal structure of the N terminus (amino acids [aa] 11 to 125) of SARS-CoV-2 Nsp1 at a 1.25-Å resolution. Further functional assays showed that the N terminus of SARS-CoVs Nsp1 alone loses the ability to colocalize with ribosomes and inhibit protein translation. The C terminus of Nsp1 can colocalize with ribosomes, but its protein translation inhibition ability is significantly weakened. Interestingly, fusing the C terminus of Nsp1 with enhanced green fluorescent protein (EGFP) or other proteins in place of its N terminus restored the protein translation inhibitory ability to a level equivalent to that of full-length Nsp1. Thus, our results suggest that the N terminus of Nsp1 is able to stabilize the binding of the Nsp1 C terminus to ribosomes and act as a nonspecific barrier to block the mRNA channel, thus abrogating host mRNA translation.

Online biophysical predictions for SARS-CoV-2 proteins.

BACKGROUND: The SARS-CoV-2 virus, the causative agent of COVID-19, consists of an assembly of proteins that determine its infectious and immunological behavior, as well as its response to therapeutics. Major structural biology efforts on these proteins have already provided essential insights into the mode of action of the virus, as well as avenues for structure-based drug design. However, not all of the SARS-CoV-2 proteins, or regions thereof, have a well-defined three-dimensional structure, and as such might exhibit ambiguous, dynamic behaviour that is not evident from static structure representations, nor from molecular dynamics simulations using these structures. MAIN: We present a website ( https://bio2byte.be/sars2/ ) that provides protein sequence-based predictions of the backbone and side-chain dynamics and conformational propensities of these proteins, as well as derived early folding, disorder, ß-sheet aggregation, protein-protein interaction and epitope propensities. These predictions attempt to capture the inherent biophysical propensities encoded in the sequence, rather than context-dependent behaviour such as the final folded state. In addition, we provide the biophysical variation that is observed in homologous proteins, which gives an indication of the limits of their functionally relevant biophysical behaviour. CONCLUSION: The https://bio2byte.be/sars2/ website provides a range of protein sequence-based predictions for 27 SARS-CoV-2 proteins, enabling researchers to form hypotheses about their possible functional modes of action.

Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan.

A mysterious outbreak of atypical pneumonia in late 2019 was traced to a seafood wholesale market in Wuhan of China. Within a few weeks, a novel coronavirus tentatively named as 2019 novel coronavirus (2019-nCoV) was announced by the World Health Organization. We performed bioinformatics analysis on a virus genome from a patient with 2019-nCoV infection and compared it with other related coronavirus genomes. Overall, the genome of 2019-nCoV has 89% nucleotide identity with bat SARS-like-CoVZXC21 and 82% with that of human SARS-CoV. The phylogenetic trees of their orf1a/b, Spike, Envelope, Membrane and Nucleoprotein also clustered closely with those of the bat, civet and human SARS coronaviruses. However, the external subdomain of Spike's receptor binding domain of 2019-nCoV shares only 40% amino acid identity with other SARS-related coronaviruses. Remarkably, its orf3b encodes a completely novel short protein. Furthermore, its new orf8 likely encodes a secreted protein with an alpha-helix, following with a beta-sheet(s) containing six strands. Learning from the roles of civet in SARS and camel in MERS, hunting for the animal source of 2019-nCoV and its more ancestral virus would be important for understanding the origin and evolution of this novel lineage B betacoronavirus. These findings provide the basis for starting further studies on the pathogenesis, and optimizing the design of diagnostic, antiviral and vaccination strategies for this emerging infection.

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.

Development of Patient-Derived Human Monoclonal Antibodies Against Nucleocapsid Protein of Severe Acute Respiratory Syndrome Coronavirus 2 for Coronavirus Disease 2019 Diagnosis.

The pandemic caused by emerging Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) presents a global public health threat. Illustrating human antibody responding to viral antigen could potentially provide valuable information for basic research and clinical diagnosis. The antibody can be used as a complement to the viral detection for the rapid diagnosis of infected patients. Compared with spike protein (SP), nucleocapsid protein (NP) is normally conserved and highly immunogenic in many coronavirus members. As a major antigen, NP is a potential target for the diagnosis of SARS-CoV-2 infection. Here, we constructed a combinatorial fragment of antigen-binding (Fab)antibody phage library based on peripheral blood-derived from five coronavirus disease 2019 (COVID-19) infected donors. From the library, 159 Fab antibodies were obtained and identified by panning with NP. Among them, 16 antibodies were evaluated for their binding properties and epitopes recognition. Among these 16 antibodies, two well-paired antibodies were finally screened out for SARS-CoV-2 diagnosis by double-antibody sandwich enzyme-linked immunosorbent assay (ELISA) method. Our works may provide a potential resource for the clinical diagnosis of SARS-CoV-2 infection.

Toward COVID-19 Therapeutics: A Viewpoint from the Nonprotein Amino Acid Based Synthetic Peptide Design Approach.

Cell entry, the fundamental step in cross-species transmission of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), is initiated by the recognition of the host cell angiotensin-converting enzyme-2 (ACE2) receptor by the receptor-binding domain (RBD) of the spike protein of SARS-CoV-2. To date, several peptides have been proposed against SARS-CoV-2 both as inhibitor agents or as detection tools that can also be attached to the surfaces of nanoparticle carriers. But owing to their natural amino acid sequences, such peptides cannot be considered as efficient therapeutic candidates from a biostability point of view. This discussion demonstrates the design strategy of synthetic nonprotein amino acid substituted peptides with enhanced biostability and binding affinity, the implication of which can make those peptides potential therapeutic agents for inhibition and simple detection tools.

Critical Sequence Hotspots for Binding of Novel Coronavirus to Angiotensin Converter Enzyme as Evaluated by Molecular Simulations.

The novel coronavirus (nCOV-2019) outbreak has put the world on edge, causing millions of cases and hundreds of thousands of deaths all around the world, as of June 2020, let alone the societal and economic impacts of the crisis. The spike protein of nCOV-2019 resides on the virion's surface mediating coronavirus entry into host cells by binding its receptor binding domain (RBD) to the host cell surface receptor protein, angiotensin converter enzyme (ACE2). Our goal is to provide a detailed structural mechanism of how nCOV-2019 recognizes and establishes contacts with ACE2 and its difference with an earlier severe acute respiratory syndrome coronavirus (SARS-COV) in 2002 via extensive molecular dynamics (MD) simulations. Numerous mutations have been identified in the RBD of nCOV-2019 strains isolated from humans in different parts of the world. In this study, we investigated the effect of these mutations as well as other Ala-scanning mutations on the stability of the RBD/ACE2 complex. It is found that most of the naturally occurring mutations to the RBD either slightly strengthen or have the same binding affinity to ACE2 as the wild-type nCOV-2019. This means that the virus had sufficient binding affinity to its receptor at the beginning of the crisis. This also has implications for any vaccine design endeavors since these mutations could act as antibody escape mutants. Furthermore, in silico Ala-scanning and long-timescale MD simulations highlight the crucial role of the residues at the interface of RBD and ACE2 that may be used as potential pharmacophores for any drug development endeavors. From an evolutional perspective, this study also identifies how the virus has evolved from its predecessor SARS-COV and how it could further evolve to become even more infectious.

Pfam: The protein families database in 2021.

The Pfam database is a widely used resource for classifying protein sequences into families and domains. Since Pfam was last described in this journal, over 350 new families have been added in Pfam 33.1 and numerous improvements have been made to existing entries. To facilitate research on COVID-19, we have revised the Pfam entries that cover the SARS-CoV-2 proteome, and built new entries for regions that were not covered by Pfam. We have reintroduced Pfam-B which provides an automatically generated supplement to Pfam and contains 136 730 novel clusters of sequences that are not yet matched by a Pfam family. The new Pfam-B is based on a clustering by the MMseqs2 software. We have compared all of the regions in the RepeatsDB to those in Pfam and have started to use the results to build and refine Pfam repeat families. Pfam is freely available for browsing and download at http://pfam.xfam.org/.

Reverse vaccinology assisted designing of multiepitope-based subunit vaccine against SARS-CoV-2.

BACKGROUND: Coronavirus disease 2019 (COVID-19) linked with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cause severe illness and life-threatening pneumonia in humans. The current COVID-19 pandemic demands an effective vaccine to acquire protection against the infection. Therefore, the present study was aimed to design a multiepitope-based subunit vaccine (MESV) against COVID-19. METHODS: Structural proteins (Surface glycoprotein, Envelope protein, and Membrane glycoprotein) of SARS-CoV-2 are responsible for its prime functions. Sequences of proteins were downloaded from GenBank and several immunoinformatics coupled with computational approaches were employed to forecast B- and T- cell epitopes from the SARS-CoV-2 highly antigenic structural proteins to design an effective MESV. RESULTS: Predicted epitopes suggested high antigenicity, conserveness, substantial interactions with the human leukocyte antigen (HLA) binding alleles, and collective global population coverage of 88.40%. Taken together, 276 amino acids long MESV was designed by connecting 3 cytotoxic T lymphocytes (CTL), 6 helper T lymphocyte (HTL) and 4 B-cell epitopes with suitable adjuvant and linkers. The MESV construct was non-allergenic, stable, and highly antigenic. Molecular docking showed a stable and high binding affinity of MESV with human pathogenic toll-like receptors-3 (TLR3). Furthermore, in silico immune simulation revealed significant immunogenic response of MESV. Finally, MEV codons were optimized for its in silico cloning into the Escherichia coli K-12 system, to ensure its increased expression. CONCLUSION: The MESV developed in this study is capable of generating immune response against COVID-19. Therefore, if designed MESV further investigated experimentally, it would be an effective vaccine candidate against SARS-CoV-2 to control and prevent COVID-19.

Designing a novel mRNA vaccine against SARS-CoV-2: An immunoinformatics approach.

SARS-CoV-2 is the deadly virus behind COVID-19, the disease that went on to ravage the world and caused the biggest pandemic 21st century has witnessed so far. On the face of ongoing death and destruction, the urgent need for the discovery of a vaccine against the virus is paramount. This study resorted to the emerging discipline of immunoinformatics in order to design a multi-epitope mRNA vaccine against the spike glycoprotein of SARS-CoV-2. Various immunoinformatics tools were utilized to predict T and B lymphocyte epitopes. The epitopes were channeled through a filtering pipeline comprised of antigenicity, toxicity, allergenicity, and cytokine inducibility evaluation with the goal of selecting epitopes capable of generating both T and B cell-mediated immune responses. Molecular docking simulation between the epitopes and their corresponding MHC molecules was carried out. 13 epitopes, a highly immunogenic adjuvant, elements for proper sub-cellular trafficking, a secretion booster, and appropriate linkers were combined for constructing the vaccine. The vaccine was found to be antigenic, almost neutral at physiological pH, non-toxic, non-allergenic, capable of generating a robust immune response and had a decent worldwide population coverage. Based on these parameters, this design can be considered a promising choice for a vaccine against SARS-CoV-2.