Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloEstimating the Binding of Sars-CoV-2 Peptides to HLA Class I in Human Subpopulations Using Artificial Neural Networks
Anno di pubblicazione2020
Formato
  • Elettronico
  • Cartaceo
Autore/iLa Porta C.A.M.; Zapperi S.
Affiliazioni autori1: Center for Complexity and Biosystems, Department of Environmental Science and Policy, University of Milan, via Celoria 26, Milano, 20133, Italy / 1: CNR - Consiglio Nazionale delle Ricerche, Istituto di Biofisica, via Celoria 26, Milano, 20133, Italy / 2 Center for Complexity and Biosystems, Department of Physics, University of Milan, via Celoria 16, Milano, 20133, Italy / 2: CNR - Consiglio Nazionale delle Ricerche, Istituto di Chimica della Materia Condensata e di Tecnologie per l'Energia, via R. Cozzi 53, Milano, 20125, Italy
Autori CNR e affiliazioni
  • STEFANO ZAPPERI
  • CATERINA ANNA MARIA LA PORTA
Lingua/e
  • inglese
AbstractEpidemiological studies show that SARS-CoV-2 infection leads to severe symptoms only in a fraction of patients, but the determinants of individual susceptibility to the virus are still unknown. The major histocompatibility complex (MHC) class I exposes viral peptides in all nucleated cells and is involved in the susceptibility to many human diseases. Here, we use artificial neural networks to analyze the binding of SARS-CoV-2 peptides with polymorphic human MHC class I molecules. In this way, we identify two sets of haplotypes present in specific human populations: the first displays weak binding with SARS-CoV-2 peptides, while the second shows strong binding and T cell propensity. Our work offers a useful support to identify the individual susceptibility to COVID-19 and illustrates a mechanism underlying variations in the immune response to SARS-CoV-2. A record of this paper's transparent peer review process is included in the Supplemental Information.
Lingua abstractinglese
Altro abstractThe response to SARS-CoV-2 infection differs from person to person, with some patients developing more severe symptoms than others. In this paper, Caterina La Porta and Stefano Zapperi show that the immune recognition of SARS-CoV-2 viral peptides differs widely among individuals and could thus explain why they may respond differently to the virus.
Lingua altro abstractinglese
Pagine da412
Pagine a417.e2
Pagine totali9
RivistaCell systems (Print)
Attiva dal 2015
Editore: Cell Press ; Elsevier - Cambridge, MA ; Philadelphia, PA
Paese di pubblicazione: Paesi Bassi
Lingua: inglese
ISSN: 2405-4712
Titolo chiave: Cell systems (Print)
Titolo proprio: Cell systems
Numero volume della rivista11
Fascicolo della rivista4
DOI10.1016/j.cels.2020.08.011
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85091255964)
  • ISI Web of Science (WOS) (Codice:000582118000010)
Parole chiaveartificial neural network, shaplotypes, HLA, peptides, SARS-CoV-2, T cell propensity
Link (URL, URI)https://www.sciencedirect.com/science/article/pii/S2405471220302957?via%3Dihub
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione13/08/2020
Note/Altre informazioniHighlights: o Binding of SARS-CoV-2 peptides to HLA molecules is computed o Weakly or strongly binding haplotypes are identified in human populations o Results explain variations in the individual immune response to SARS-CoV-2
Strutture CNR
  • IBF — Istituto di biofisica
  • ICMATE — Istituto di Chimica della Materia Condensata e di Tecnologie per l'Energia
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati