tetano
Editor, Senior Moderator
Microbes Infect. 2020 Apr 14. pii: S1286-4579(20)30053-8. doi: 10.1016/j.micinf.2020.04.002. [Epub ahead of print]
Comparative computational analysis of SARS-CoV-2 nucleocapsid protein epitopes in taxonomically related coronaviruses.
Tilocca B[SUP]1[/SUP], Soggiu A[SUP]2[/SUP], Sanguinetti M[SUP]3[/SUP], Musella V[SUP]1[/SUP], Britti D[SUP]1[/SUP], Bonizzi L[SUP]2[/SUP], Urbani A[SUP]4[/SUP], Roncada P[SUP]5[/SUP].
Author information
Abstract
Several research lines are currently ongoing to address the multitude of facets of the pandemic COVID-19. In line with the One-Health concept, extending the target of the studies to the animals which humans are continuously interacting with may favor a better understanding of the SARS-CoV-2 biology and pathogenetic mechanisms; thus, helping to adopt the most suitable containment measures. The last two decades have already faced severe manifestations of the coronavirus infection in both humans and animals, thus, circulating epitopes from previous outbreaks might confer partial protection from SARS-CoV-2 infections. In the present study, we provide an in-silico survey of the major nucleocapsid protein epitopes and compare them with the homologues of taxonomically-related coronaviruses with tropism for animal species that are closely inter-related with the human beings population all over the world. Protein sequence alignment provides evidence of high sequence homology for some of the investigated proteins. Moreover, structural epitope mapping by homology modelling revealed a potential immunogenic value also for specific sequences scoring a lower identity with SARS-CoV-2 nucleocapsid proteins. These evidence provide a molecular structural rationale for a potential role in conferring protection from SARS-CoV-2 infection and identifying potential candidates for the development of diagnostic tools and prophylactic-oriented strategies.
Copyright ? 2020 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.
KEYWORDS:
COVID-19; SARS-CoV-2; coronavirus; diagnosis; personalized medicine
PMID:32302675DOI:10.1016/j.micinf.2020.04.002
Comparative computational analysis of SARS-CoV-2 nucleocapsid protein epitopes in taxonomically related coronaviruses.
Tilocca B[SUP]1[/SUP], Soggiu A[SUP]2[/SUP], Sanguinetti M[SUP]3[/SUP], Musella V[SUP]1[/SUP], Britti D[SUP]1[/SUP], Bonizzi L[SUP]2[/SUP], Urbani A[SUP]4[/SUP], Roncada P[SUP]5[/SUP].
Author information
Abstract
Several research lines are currently ongoing to address the multitude of facets of the pandemic COVID-19. In line with the One-Health concept, extending the target of the studies to the animals which humans are continuously interacting with may favor a better understanding of the SARS-CoV-2 biology and pathogenetic mechanisms; thus, helping to adopt the most suitable containment measures. The last two decades have already faced severe manifestations of the coronavirus infection in both humans and animals, thus, circulating epitopes from previous outbreaks might confer partial protection from SARS-CoV-2 infections. In the present study, we provide an in-silico survey of the major nucleocapsid protein epitopes and compare them with the homologues of taxonomically-related coronaviruses with tropism for animal species that are closely inter-related with the human beings population all over the world. Protein sequence alignment provides evidence of high sequence homology for some of the investigated proteins. Moreover, structural epitope mapping by homology modelling revealed a potential immunogenic value also for specific sequences scoring a lower identity with SARS-CoV-2 nucleocapsid proteins. These evidence provide a molecular structural rationale for a potential role in conferring protection from SARS-CoV-2 infection and identifying potential candidates for the development of diagnostic tools and prophylactic-oriented strategies.
Copyright ? 2020 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.
KEYWORDS:
COVID-19; SARS-CoV-2; coronavirus; diagnosis; personalized medicine
PMID:32302675DOI:10.1016/j.micinf.2020.04.002