La Porta Caterina A M, Zapperi Stefano
Center for Complexity and Biosystems, University of Milan, Milan, Italy.
Department of Environmental Science and Policy, University of Milan, Milan, Italy.
Front Digit Health. 2021 Jul 9;3:704411. doi: 10.3389/fdgth.2021.704411. eCollection 2021.
The spread of the current Sars-Cov-2 pandemics leads to the development of mutations that are constantly monitored because they could affect the efficacy of vaccines. Three recently identified mutated strains, known as variants of concern, are rapidly spreading worldwide. Here, we study possible effects of these mutations on the immune response to Sars-Cov-2 infection using NetTepi a computational method based on artificial neural networks that considers binding and stability of peptides obtained by proteasome degradation for widely represented HLA class I alleles present in human populations as well as the T-cell propensity of viral peptides that measures their immune response. Our results show variations in the number of potential highly ranked peptides ranging between 0 and 20% depending on the specific HLA allele. The results can be useful to design more specific vaccines.
当前新冠病毒大流行的传播导致了突变的出现，这些突变受到持续监测，因为它们可能影响疫苗的效力。最近发现的三种突变株，即所谓的关注变体，正在全球迅速传播。在此，我们使用NetTepi研究这些突变对新冠病毒感染免疫反应的可能影响，NetTepi是一种基于人工神经网络的计算方法，它考虑了蛋白酶体降解获得的肽段对于人群中广泛存在的HLA I类等位基因的结合和稳定性，以及测量病毒肽免疫反应的T细胞倾向。我们的结果表明，根据特定的HLA等位基因，潜在高排名肽段的数量变化在0%至20%之间。这些结果对于设计更具特异性的疫苗可能有用。
