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Identification of evolutionarily stable sites across the SARS-CoV-2 proteome.
Wang, Chen; Konecki, Daniel M; Marciano, David C; Govindarajan, Harikumar; Williams, Amanda M; Wastuwidyaningtyas, Brigitta; Bourquard, Thomas; Katsonis, Panagiotis; Lichtarge, Olivier.
  • Wang C; Baylor College of Medicine.
  • Konecki DM; Baylor College of Medicine.
  • Marciano DC; Baylor College of Medicine.
  • Govindarajan H; Baylor College of Medicine.
  • Williams AM; Baylor College of Medicine.
  • Wastuwidyaningtyas B; Baylor College of Medicine.
  • Bourquard T; Baylor College of Medicine.
  • Katsonis P; Baylor College of Medicine.
  • Lichtarge O; Baylor College of Medicine.
Res Sq ; 2020 Oct 20.
Article in English | MEDLINE | ID: covidwho-895824
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ABSTRACT
Since the first recognized case of COVID-19, more than 30 million people have been infected worldwide. Despite global efforts in drug and vaccine development to fight the disease, there is currently no vaccine or drug cure for COVID-19, though some drugs reduce severity and hasten recovery. Here we interrogate the evolutionary history of the entire SARS-CoV-2 proteome to identify functional sites that can inform the search for treatments. Combining this information with the mutations observed in the current COVID-19 outbreak, we systematically and comprehensively define evolutionarily stable sites that are useful drug targets. Several experimentally-validated effective drugs interact with these proposed target sites. In addition, the same evolutionary information can prioritize cross reactive antigens that are useful in directing multi-epitope vaccine strategies to illicit broadly neutralizing immune responses to the betacoronavirus family. Although the results are focused on SARS-CoV-2, these approaches are based upon evolutionary principles and are agnostic to organism or infective agent.

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Randomized controlled trials Topics: Vaccines Language: English Year: 2020 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Randomized controlled trials Topics: Vaccines Language: English Year: 2020 Document Type: Article