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10.1016/j.cels.2020.08.011

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suck abstract from ncbi


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pmid32916095      Cell+Syst 2020 ; 11 (4): 412-417.e2
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  • Estimating the Binding of Sars-CoV-2 Peptides to HLA Class I in Human Subpopulations Using Artificial Neural Networks #MMPMID32916095
  • La Porta CAM; Zapperi S
  • Cell Syst 2020[Oct]; 11 (4): 412-417.e2 PMID32916095show ga
  • Epidemiological 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.
  • |*Neural Networks, Computer[MESH]
  • |*Polymorphism, Genetic[MESH]
  • |Betacoronavirus/*immunology[MESH]
  • |Haplotypes[MESH]
  • |Histocompatibility Antigens Class I/chemistry/genetics/*immunology[MESH]
  • |Humans[MESH]
  • |Peptides/chemistry/*immunology[MESH]
  • |Protein Binding[MESH]
  • |SARS-CoV-2[MESH]


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