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A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets.
Russo, Giulia; Di Salvatore, Valentina; Sgroi, Giuseppe; Parasiliti Palumbo, Giuseppe Alessandro; Reche, Pedro A; Pappalardo, Francesco.
Afiliación
  • Russo G; Department of Drug and Health Sciences, University of Catania, Catania, Italy.
  • Di Salvatore V; Department of Drug and Health Sciences, University of Catania, Catania, Italy.
  • Sgroi G; Department of Drug and Health Sciences, University of Catania, Catania, Italy.
  • Parasiliti Palumbo GA; Department of Drug and Health Sciences, University of Catania, Catania, Italy.
  • Reche PA; Department of Drug and Health Sciences, University of Catania, Catania, Italy.
  • Pappalardo F; Department of Drug and Health Sciences, University of Catania, Catania, Italy.
Brief Bioinform ; 23(1)2022 01 17.
Article en En | MEDLINE | ID: mdl-34607353
ABSTRACT
The COVID-19 pandemic has highlighted the need to come out with quick interventional solutions that can now be obtained through the application of different bioinformatics software to actively improve the success rate. Technological advances in fields such as computer modeling and simulation are enriching the discovery, development, assessment and monitoring for better prevention, diagnosis, treatment and scientific evidence generation of specific therapeutic strategies. The combined use of both molecular prediction tools and computer simulation in the development or regulatory evaluation of a medical intervention, are making the difference to better predict the efficacy and safety of new vaccines. An integrated bioinformatics pipeline that merges the prediction power of different software that act at different scales for evaluating the elicited response of human immune system against every pathogen is proposed. As a working example, we applied this problem solving protocol to predict the cross-reactivity of pre-existing vaccination interventions against SARS-CoV-2.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Simulación por Computador / Programas Informáticos / Biología Computacional / Pandemias / Vacunas contra la COVID-19 / SARS-CoV-2 / COVID-19 Tipo de estudio: Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Italia

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Simulación por Computador / Programas Informáticos / Biología Computacional / Pandemias / Vacunas contra la COVID-19 / SARS-CoV-2 / COVID-19 Tipo de estudio: Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Italia