SARS-CoV-2 variants infectivity prediction and therapeutic peptide design using computational approaches Article Swipe
Chandran S. Abhinand
,
Athira A. Prabhakaran
,
Anand Krishnamurthy
,
Rajesh Raju
,
Thottethodi Subrahmanya Keshava Prasad
,
Achuthsankar S. Nair
,
Kallikat N. Rajasekharan
,
Oommen V. Oommen
,
P. R. Sudhakaran
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.6084/m9.figshare.21781386
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.6084/m9.figshare.21781386
The outbreak of severe acute respiratory coronavirus 2 (SARS-CoV-2) has created a public health emergency globally. SARS-CoV-2 enters the human cell through the binding of the spike protein to human angiotensin converting enzyme 2 (ACE2) receptor. Significant changes have been reported in the mutational landscape of SARS-CoV-2 in the receptor binding domain (RBD) of S protein, subsequent to evolution of the pandemic. The present study examines the correlation between the binding affinity of mutated S-proteins and the rate of viral infectivity. For this, the binding affinity of SARS-CoV and variants of SARS-CoV-2 towards ACE2 was computationally determined. Subsequently, the RBD mutations were classified on the basis of the number of strains identified with respect to each mutation and the resulting variation in the binding affinity was computationally examined. The molecular docking studies indicated a significant correlation between the Z-Rank score of mutated S proteins and the rate of infectivity, suitable for predicting SARS-CoV-2 infectivity. Accordingly, a 30-mer peptide was designed and the inhibitory properties were computationally analyzed. Single amino acid-wise mutation was performed subsequently to identify the peptide with the highest binding affinity. Molecular dynamics and free energy calculations were then performed to examine the stability of the peptide-protein complexes. Additionally, selected peptides were synthesized and screened using a colorimetric assay. Together, this study developed a model to predict the rate of infectivity of SARS-CoV-2 variants and propose a potential peptide that can be used as an inhibitor for the viral entry to human. Communicated by Ramaswamy H. Sarma.
Related Topics
Concepts
Infectivity
Coronavirus disease 2019 (COVID-19)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
2019-20 coronavirus outbreak
Computational biology
Virology
Biology
Computer science
Medicine
Virus
Infectious disease (medical specialty)
Internal medicine
Disease
Outbreak
Metadata
- Type
- dataset
- Language
- en
- Landing Page
- https://doi.org/10.6084/m9.figshare.21781386
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394167419
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394167419Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.6084/m9.figshare.21781386Digital Object Identifier
- Title
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SARS-CoV-2 variants infectivity prediction and therapeutic peptide design using computational approachesWork title
- Type
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datasetOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
-
Chandran S. Abhinand, Athira A. Prabhakaran, Anand Krishnamurthy, Rajesh Raju, Thottethodi Subrahmanya Keshava Prasad, Achuthsankar S. Nair, Kallikat N. Rajasekharan, Oommen V. Oommen, P. R. SudhakaranList of authors in order
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https://doi.org/10.6084/m9.figshare.21781386Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.6084/m9.figshare.21781386Direct OA link when available
- Concepts
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Infectivity, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 2019-20 coronavirus outbreak, Computational biology, Virology, Biology, Computer science, Medicine, Virus, Infectious disease (medical specialty), Internal medicine, Disease, OutbreakTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.receptor. | 35 |
| abstract_inverted_index.resulting | 119 |
| abstract_inverted_index.stability | 195 |
| abstract_inverted_index.variation | 120 |
| abstract_inverted_index.S-proteins | 74 |
| abstract_inverted_index.SARS-CoV-2 | 16, 46, 91, 152, 224 |
| abstract_inverted_index.classified | 102 |
| abstract_inverted_index.complexes. | 199 |
| abstract_inverted_index.converting | 31 |
| abstract_inverted_index.identified | 111 |
| abstract_inverted_index.inhibitory | 162 |
| abstract_inverted_index.mutational | 43 |
| abstract_inverted_index.predicting | 151 |
| abstract_inverted_index.properties | 163 |
| abstract_inverted_index.subsequent | 56 |
| abstract_inverted_index.Significant | 36 |
| abstract_inverted_index.angiotensin | 30 |
| abstract_inverted_index.coronavirus | 6 |
| abstract_inverted_index.correlation | 67, 135 |
| abstract_inverted_index.determined. | 96 |
| abstract_inverted_index.infectivity | 222 |
| abstract_inverted_index.respiratory | 5 |
| abstract_inverted_index.significant | 134 |
| abstract_inverted_index.synthesized | 204 |
| abstract_inverted_index.(SARS-CoV-2) | 8 |
| abstract_inverted_index.Accordingly, | 154 |
| abstract_inverted_index.Communicated | 244 |
| abstract_inverted_index.calculations | 188 |
| abstract_inverted_index.colorimetric | 209 |
| abstract_inverted_index.infectivity, | 148 |
| abstract_inverted_index.infectivity. | 80, 153 |
| abstract_inverted_index.subsequently | 173 |
| abstract_inverted_index.Additionally, | 200 |
| abstract_inverted_index.Subsequently, | 97 |
| abstract_inverted_index.computationally | 95, 126, 165 |
| abstract_inverted_index.peptide-protein | 198 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile |