New Computational Approach for Peptide Vaccine Design Against SARS-COV-2
© The Author(s), under exclusive licence to Springer Nature B.V. 2021..
The design for vaccines using in silico analysis of genomic data of different viruses has taken many different paths, but lack of any precise computational approach has constrained them to alignment methods and some alignment-free techniques. In this work, a precise computational approach has been established wherein two new mathematical parameters have been suggested to identify the highly conserved and surface-exposed regions which are spread over a large region of the surface protein of the virus so that one can determine possible peptide vaccine candidates from those regions. The first parameter, w, is the sum of the normalized values of the measure of surface accessibility and the normalized measure of conservativeness, and the second parameter is the area of a triangle formed by a mathematical model named 2D Polygon Representation. This method has been, therefore, used to determine possible vaccine targets against SARS-CoV-2 by considering its surface-situated spike glycoprotein. The results of this model have been verified by a parallel analysis using the older approach of manually estimating the graphs describing the variation of conservativeness and surface-exposure across the protein sequence. Furthermore, the working of the method has been tested by applying it to find out peptide vaccine candidates for Zika and Hendra viruses respectively. A satisfactory consistency of the model results with pre-established results for both the test cases shows that this in silico alignment-free analysis proposed by the model is suitable not only to determine vaccine targets against SARS-CoV-2 but also ready to extend against other viruses.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:27 |
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| Enthalten in: |
International journal of peptide research and therapeutics - 27(2021), 4 vom: 07., Seite 2257-2273 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Biswas, Subhamoy [VerfasserIn] |
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| Links: |
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| Themen: |
Alignment-free sequence analysis |
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| Anmerkungen: |
Date Revised 18.02.2022 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1007/s10989-021-10251-7 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a © The Author(s), under exclusive licence to Springer Nature B.V. 2021. | ||
| 520 | |a The design for vaccines using in silico analysis of genomic data of different viruses has taken many different paths, but lack of any precise computational approach has constrained them to alignment methods and some alignment-free techniques. In this work, a precise computational approach has been established wherein two new mathematical parameters have been suggested to identify the highly conserved and surface-exposed regions which are spread over a large region of the surface protein of the virus so that one can determine possible peptide vaccine candidates from those regions. The first parameter, w, is the sum of the normalized values of the measure of surface accessibility and the normalized measure of conservativeness, and the second parameter is the area of a triangle formed by a mathematical model named 2D Polygon Representation. This method has been, therefore, used to determine possible vaccine targets against SARS-CoV-2 by considering its surface-situated spike glycoprotein. The results of this model have been verified by a parallel analysis using the older approach of manually estimating the graphs describing the variation of conservativeness and surface-exposure across the protein sequence. Furthermore, the working of the method has been tested by applying it to find out peptide vaccine candidates for Zika and Hendra viruses respectively. A satisfactory consistency of the model results with pre-established results for both the test cases shows that this in silico alignment-free analysis proposed by the model is suitable not only to determine vaccine targets against SARS-CoV-2 but also ready to extend against other viruses | ||
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| 650 | 4 | |a In silico drug design | |
| 650 | 4 | |a Peptide vaccines | |
| 650 | 4 | |a SARS-CoV-2 | |
| 650 | 4 | |a Viral epidemics | |
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| 700 | 1 | |a Nandy, Ashesh |e verfasserin |4 aut | |
| 700 | 1 | |a Basak, Subhash C |e verfasserin |4 aut | |
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