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In-silico design of a multi-epitope for developing sero-diagnosis detection of SARS-CoV-2 using spike glycoprotein and nucleocapsid antigens.

ABSTRACT: COVID-19 is a pandemic disease caused by novel corona virus, SARS-CoV-2, initially originated from China. In response to this serious life-threatening disease, designing and developing more accurate and sensitive tests are crucial. The aim of this study is designing a multi-epitope of spike and nucleocapsid antigens of COVID-19 virus by bioinformatics methods. The sequences of nucleotides obtained from the NCBI Nucleotide Database. Transmembrane structures of proteins were predicted by TMHMM Server and the prediction of signal peptide of proteins was performed by Signal P Server. B-cell epitopes' prediction was performed by the online prediction server of IEDB server. Beta turn structure of linear epitopes was also performed using the IEDB server. Conformational epitope prediction was performed using the CBTOPE and eventually, eight antigenic epitopes with high physicochemical properties were selected, and then, all eight epitopes were blasted using the NCBI website. The analyses revealed that α-helices, extended strands, β-turns, and random coils were 28.59%, 23.25%, 3.38%, and 44.78% for S protein, 21.24%, 16.71%, 6.92%, and 55.13% for N Protein, respectively. The S and N protein three-dimensional structure was predicted using the prediction I-TASSER server. In the current study, bioinformatics tools were used to design a multi-epitope peptide based on the type of antigen and its physiochemical properties and SVM method (Machine Learning) to design multi-epitopes that have a high avidity against SARS-CoV-2 antibodies to detect infections by COVID-19.

SUBMITTER: Javadi Mamaghani A

PROVIDER: S-EPMC8614630 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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In-silico design of a multi-epitope for developing sero-diagnosis detection of SARS-CoV-2 using spike glycoprotein and nucleocapsid antigens.

Javadi Mamaghani Amirreza A Arab-Mazar Zahra Z Heidarzadeh Siamak S Ranjbar Mohammad Mehdi MM Molazadeh Shima S Rashidi Sama S Niazpour Farzad F Naghi Vishteh Mohadeseh M Bashiri Homayoon H Bozorgomid Arezoo A Behniafar Hamed H Ashrafi Mohammad M

Network modeling and analysis in health informatics and bioinformatics 20211125 1

COVID-19 is a pandemic disease caused by novel corona virus, SARS-CoV-2, initially originated from China. In response to this serious life-threatening disease, designing and developing more accurate and sensitive tests are crucial. The aim of this study is designing a multi-epitope of spike and nucleocapsid antigens of COVID-19 virus by bioinformatics methods. The sequences of nucleotides obtained from the NCBI Nucleotide Database. Transmembrane structures of proteins were predicted by TMHMM Ser ...[more]

PMID: 34849326

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Project description:Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of Johne's disease (JD), a chronic intestinal inflammatory disease of cattle and other ruminants. JD has a high herd prevalence and causes serious animal health problems and significant economic loss in domesticated ruminants throughout the world. Since serological detection of MAP infected animals during the early stages of infection remains challenging due to the low sensitivity of extant assays, we screened 180 well-characterized serum samples using a whole proteome microarray from Mycobacterium tuberculosis (MTB), a close relative of MAP. Based on extensive testing of serum and milk samples, fecal culture and qPCR for direct detection of MAP, the samples were previously assigned to one of 4 groups: negative low exposure (n = 30, NL); negative high exposure (n = 30, NH); fecal positive, ELISA negative (n = 60, F+E-); and fecal positive, ELISA positive (n = 60, F+E+). Of the 740 reactive proteins, several antigens were serologically recognized early but not late in infection, suggesting a complex and dynamic evolution of the MAP humoral immune response during disease progression. Ordinal logistic regression models identified a subset of 47 candidate proteins with significantly different normalized intensity values (p<0.05), including 12 in the NH and 23 in F+E- groups, suggesting potential utility for the early detection of MAP infected animals. Next, the diagnostic utility of four MAP orthologs (MAP1569, MAP2942c, MAP2609, and MAP1272c) was assessed and reveal moderate to high diagnostic sensitivities (range 48.3% to 76.7%) and specificity (range 96.7% to 100%), with a combined 88.3% sensitivity and 96.7% specificity. Taken together, the results of our analyses have identified several candidate MAP proteins of potential utility for the early detection of MAP infection, as well individual MAP proteins that may serve as the foundation for the next generation of well-defined serological diagnosis of JD in cattle.

Dataset Information

In-silico design of a multi-epitope for developing sero-diagnosis detection of SARS-CoV-2 using spike glycoprotein and nucleocapsid antigens.

Publications

In-silico design of a multi-epitope for developing sero-diagnosis detection of SARS-CoV-2 using spike glycoprotein and nucleocapsid antigens.

Similar Datasets

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