ArticleComputers in biology and medicine2021
Techniques assisting peptide vaccine and peptidomimetic design. Sidechain exposure in the SARS-CoV-2 spike glycoprotein.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Peptides for Vaccine Development.ACS applied bio materials · 2022Review
- Towards faster response against emerging epidemics and prediction of variants of concern.Informatics in medicine unlocked · 2022Review
- VLP-Based COVID-19 Vaccines: An Adaptable Technology against the Threat of New Variants.Vaccines · 2021Review
- Functional Analysis of Human and Feline Coronavirus Cross-Reactive Antibodies Directed Against the SARS-CoV-2 Fusion Peptide.Frontiers in immunology · 2021Article
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1 author.
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Abstract
The aim of the present study is to discuss the design of peptide vaccines and peptidomimetics against SARS-COV-2, to develop and apply a method of protein structure analysis that is particularly appropriate to applying and discussing such design, and also to use that method to summarize some important features of the SARS-COV-2 spike protein sequence. A tool for assessing sidechain exposure in the SARS-CoV-2 spike glycoprotein is described. It extends to assessing accessibility of sidechains by considering several different three-dimensional structure determinations of SARS-CoV-2 and SARS-CoV-1 spike protein. The method is designed to be insensitive to a distance limit for counting neighboring atoms and the results are in good agreement with the physical chemical properties and exposure trends of the 20 naturally occurring sidechains. The spike protein sequence is analyzed with comment regarding exposable character. It includes studies of complexes with antibody elements and ACE2. These indicate changes in exposure at sites remote to those at which the antibody binds. They are of interest concerning design of synthetic peptide vaccines, and for peptidomimetics as a basis of drug discovery. The method was also developed in order to provide linear (one-dimensional) information that can be used along with other bioinformatics data of this kind in data mining and machine learning, potentially as genomic data regarding protein polymorphisms to be combined with more traditional clinical data.
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