scholarly journals SARS-CoV2 spike protein displays biologically significant similarities with paramyxovirus surface proteins; a bioinformatics study

2020 ◽  
Author(s):  
Ehsan Ahmadi ◽  
Mohammad Reza Zabihi ◽  
Ramin Hosseinzadeh ◽  
Farshid Noorbakhsh
Keyword(s):  
Surface Proteins ◽  
Spike Protein ◽  
T Cell Epitopes ◽  
Phylogenetic Relations ◽  
Research Priority ◽  
Ssrna Virus ◽  
Immune Epitope ◽  
Recent Emergence ◽  
Bioinformatics Study ◽  
Immune Epitope Database

AbstractRecent emergence of SARS-CoV2 and associated COVID-19 pandemic has posed a great challenge for the scientific community. Understanding various aspects of SARS-CoV2 biology, virulence and pathogenesis as well as determinants of immune response have become a global research priority. In this study, we performed bioinformatic analyses on SAR-CoV2 protein sequences, trying to unravel biologically important similarities between this newly emerged virus with other RNA viruses. Comparing the proteome of SARS-CoV2 with major positive and negative strand ssRNA viruses showed significant homologies between SARS-CoV2 spike protein with pathogenic paramyxovirus fusion proteins. This ‘spike-fusion’ homology was not limited to SARS-CoV2 and it existed for some other pathogenic coronaviruses; nonetheless, SARS-CoV2 spike-fusion homology was orders of magnitude stronger than homologies observed for other known coronaviruses. Moreover, this homology did not seem to be a consequence of general ssRNA virus phylogenetic relations. We also explored potential immunological significance of this spike-fusion homology. Spike protein epitope analysis using experimentally verified data deposited in Immune Epitope Database (IEDB) revealed that the majority of spike’s T cell epitopes as well as many B cell and MHC binding epitopes map within the spike-fusion homology region. Overall, our data indicate that there might be a relation between SARS-CoV2 and paramyxoviruses at the level of their surface proteins and this relation could be of crucial immunological importance.

scholarly journals SARS-CoV-2 spike protein displays sequence similarities with paramyxovirus surface proteins; a bioinformatics study

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260360
Author(s):  
Ehsan Ahmadi ◽  
Mohammad Reza Zabihi ◽  
Ramin Hosseinzadeh ◽  
Leila Mohamed Khosroshahi ◽  
Farshid Noorbakhsh
Keyword(s):  
Fusion Proteins ◽  
Surface Proteins ◽  
Spike Protein ◽  
Multiple Sequence ◽  
B Cell Epitopes ◽  
Immune Epitope ◽  
Recent Emergence ◽  
Bioinformatics Study ◽  
Sequence Similarities ◽  
Immune Epitope Database

Recent emergence of SARS-CoV-2 and associated COVID-19 pandemic have posed a great challenge for the scientific community. In this study, we performed bioinformatic analyses on SARS-CoV-2 protein sequences, trying to unravel potential molecular similarities between this newly emerged pathogen with non-coronavirus ssRNA viruses. Comparing the proteins of SARS-CoV-2 with non-coronavirus positive and negative strand ssRNA viruses revealed multiple sequence similarities between SARS-CoV-2 and non-coronaviruses, including similarities between RNA-dependent RNA-polymerases and helicases (two highly-conserved proteins). We also observed similarities between SARS-CoV-2 surface (i.e. spike) protein with paramyxovirus fusion proteins. This similarity was restricted to a segment of spike protein S2 subunit which is involved in cell fusion. We next analyzed spike proteins from SARS-CoV-2 “variants of concern” (VOCs) and “variants of interests” (VOIs) and found that some of these variants show considerably higher spike-fusion similarity with paramyxoviruses. The ‘spike-fusion’ similarity was also observed for some pathogenic coronaviruses other than SARS-CoV-2. Epitope analysis using experimentally verified data deposited in Immune Epitope Database (IEDB) revealed that several B cell epitopes as well as T cell and MHC binding epitopes map within the spike-fusion similarity region. These data indicate that there might be a degree of convergent evolution between SARS-CoV-2 and paramyxovirus surface proteins which could be of pathogenic and immunological importance.

scholarly journals An Immunoinformatics Study to Predict Epitopes in the Envelope Protein of SARS-CoV-2

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Renu Jakhar ◽  
S. K. Gakhar
Keyword(s):  
Envelope Protein ◽  
Peptide Vaccine ◽  
Binding Energies ◽  
In Vivo Studies ◽  
T Cell Epitopes ◽  
Immune Epitope ◽  
The World ◽  
Immune Epitope Database

COVID-19 is a new viral emergent disease caused by a novel strain of coronavirus. This virus has caused a huge problem in the world as millions of people are affected by this disease. We aimed at designing a peptide vaccine for COVID-19 particularly for the envelope protein using computational methods to predict epitopes inducing the immune system. The envelope protein sequence of SARS-CoV-2 has been retrieved from the NCBI database. The bioinformatics analysis was carried out by using the Immune Epitope Database (IEDB) to predict B- and T-cell epitopes. The predicted HTL and CTL epitopes were docked with HLA alleles and binding energies were evaluated. The allergenicity of predicted epitopes was analyzed, the conservancy analysis was performed, and the population coverage was determined throughout the world. Some overlapped CTL, HTL, and B-cell epitopes were suggested to become a universal candidate for peptide-based vaccine against COVID-19. This vaccine peptide could simultaneously elicit humoral and cell-mediated immune responses. We hope to confirm our findings by adding complementary steps of both in vitro and in vivo studies to support this new universal predicted candidate.

scholarly journals Identification of Novel Candidate CD8+ T Cell Epitopes of the SARS-CoV2 with Homology to Other Seasonal Coronaviruses

Viruses ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 972
Author(s):  
Pradeep Darshana Pushpakumara ◽  
Deshan Madhusanka ◽  
Saubhagya Dhanasekara ◽  
Chandima Jeewandara ◽  
Graham S. Ogg ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Responses ◽  
Cd8 T Cell ◽  
T Cell Epitopes ◽  
Conservation Analysis ◽  
Cell Responses ◽  
Immune Epitope ◽  
Cell Immunity ◽  
High Degree ◽  
Immune Epitope Database

Cross-reactive T cell immunity to seasonal coronaviruses (HCoVs) may lead to immunopathology or protection during SARS-CoV2 infection. To understand the influence of cross-reactive T cell responses, we used IEDB (Immune epitope database) and NetMHCpan (ver. 4.1) to identify candidate CD8+ T cell epitopes, restricted through HLA-A and B alleles. Conservation analysis was carried out for these epitopes with HCoVs, OC43, HKU1, and NL63. 12/18 the candidate CD8+ T cell epitopes (binding score of ≥0.90), which had a high degree of homology (>75%) with the other three HCoVs were within the NSP12 and NSP13 proteins. They were predicted to be restricted through HLA-A*2402, HLA-A*201, HLA-A*206, and HLA-B alleles B*3501. Thirty-one candidate CD8+ T cell epitopes that were specific to SARS-CoV2 virus (<25% homology with other HCoVs) were predominantly identified within the structural proteins (spike, envelop, membrane, and nucleocapsid) and the NSP1, NSP2, and NSP3. They were predominantly restricted through HLA-B*3501 (6/31), HLA-B*4001 (6/31), HLA-B*4403 (7/31), and HLA-A*2402 (8/31). It would be crucial to understand T cell responses that associate with protection, and the differences in the functionality and phenotype of epitope specific T cell responses, presented through different HLA alleles common in different geographical groups, to understand disease pathogenesis.

scholarly journals IEDB-AR: immune epitope database—analysis resource in 2019

2019 ◽  
Vol 47 (W1) ◽  
pp. W502-W506 ◽  
Author(s):  
Sandeep Kumar Dhanda ◽  
Swapnil Mahajan ◽  
Sinu Paul ◽  
Zhen Yan ◽  
Haeuk Kim ◽  
...  
Keyword(s):  
Epitope Prediction ◽  
T Cell Epitopes ◽  
Database Analysis ◽  
Computational Tools ◽  
The Public ◽  
Immune Epitope ◽  
Command Line Tool ◽  
Rest Api ◽  
Machine Image ◽  
Immune Epitope Database

AbstractThe Immune Epitope Database Analysis Resource (IEDB-AR, http://tools.iedb.org/) is a companion website to the IEDB that provides computational tools focused on the prediction and analysis of B and T cell epitopes. All of the tools are freely available through the public website and many are also available through a REST API and/or a downloadable command-line tool. A virtual machine image of the entire site is also freely available for non-commercial use and contains most of the tools on the public site. Here, we describe the tools and functionalities that are available in the IEDB-AR, focusing on the 10 new tools that have been added since the last report in the 2012 NAR webserver edition. In addition, many of the tools that were already hosted on the site in 2012 have received updates to newest versions, including NetMHC, NetMHCpan, BepiPred and DiscoTope. Overall, this IEDB-AR update provides a substantial set of updated and novel features for epitope prediction and analysis.

scholarly journals Epitope Specific Antibodies and T Cell Receptors in the Immune Epitope Database

2018 ◽  
Vol 9 ◽  
Author(s):  
Swapnil Mahajan ◽  
Randi Vita ◽  
Deborah Shackelford ◽  
Jerome Lane ◽  
Veronique Schulten ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptors ◽  
Specific Antibodies ◽  
Cell Receptors ◽  
Immune Epitope ◽  
Immune Epitope Database

scholarly journals Candidate targets for immune responses to 2019-Novel Coronavirus (nCoV): sequence homology- and bioinformatic-based predictions

2020 ◽  
Author(s):  
Alba Grifoni ◽  
John Sidney ◽  
Yun Zhang ◽  
Richard H Scheuermann ◽  
Bjoern Peters ◽  
...  
Keyword(s):  
Immune Responses ◽  
Sequence Similarity ◽  
Rapid Expansion ◽  
Immune Recognition ◽  
T Cell Epitopes ◽  
High Sequence Similarity ◽  
Essential Information ◽  
Viral Spread ◽  
Recent Emergence ◽  
Novel Coronavirus

ABSTRACTEffective countermeasures against the recent emergence and rapid expansion of the 2019-Novel Coronavirus (2019-nCoV) require the development of data and tools to understand and monitor viral spread and immune responses. However, little information about the targets of immune responses to 2019-nCoV is available. We used the Immune Epitope Database and Analysis Resource (IEDB) resource to catalog available data related to other coronaviruses, including SARS-CoV, which has high sequence similarity to 2019-nCoV, and is the best-characterized coronavirus in terms of epitope responses. We identified multiple specific regions in 2019-nCoV that have high homology to SARS virus. Parallel bionformatic predictions identified a priori potential B and T cell epitopes for 2019-nCoV. The independent identification of the same regions using two approaches reflects the high probability that these regions are targets for immune recognition of 2019-nCoV.ONE SENTENCE SUMMARYWe identified potential targets for immune responses to 2019-nCoV and provide essential information for understanding human immune responses to this virus and evaluation of diagnostic and vaccine candidates.

scholarly journals Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19

2020 ◽  
Author(s):  
Anjali Dhall ◽  
Sumeet Patiyal ◽  
Neelam Sharma ◽  
Salman Sadullah Usmani ◽  
Gajendra P S Raghava
Keyword(s):  
Scientific Community ◽  
Prediction Models ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
Independent Validation ◽  
Immune Epitope ◽  
Learning Techniques ◽  
Wide Range ◽  
Immune Epitope Database

Abstract Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https://webs.iiitd.edu.in/raghava/il6pred/).

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