The HLA SARS-CoV-2 Research Group, Department of Veterans Affairs Health Care System, Brain Sciences Center (11B), Minneapolis VAHCS, One Veterans Drive, Minneapolis, MN, 55417, USA.
Department of Neuroscience, University of Minnesota Medical School, Minneapolis, MN, 55455, USA.
Sci Rep. 2022 May 16;12(1):8074. doi: 10.1038/s41598-022-11956-5.
There is widespread concern about the clinical effectiveness of current vaccines in preventing Covid-19 caused by SARS-CoV-2 Variants of Concern (Williams in Lancet Respir Med 29:333-335, 2021; Hayawi in Vaccines 9:1305, 2021), including those identified at present (Alpha, Beta, Gamma, Delta, Omicron) and possibly new ones arising in the future. It would be valuable to be able to predict vaccine effectiveness for any variant. Here we offer such an estimate of predicted vaccine effectiveness for any SARS-CoV-2 variant based on the amount of overlap of in silico high binding affinity of the variant and Wildtype spike glycoproteins to a pool of frequent Human Leukocyte Antigen Class II molecules which are necessary for initiating antibody production (Blum et al. in Annu Rev Immunol 31:443-473, 2013). The predictive model was strong (r = 0.910) and statistically significant (P = 0.013).
人们普遍担心当前疫苗在预防由 SARS-CoV-2 关注变异株（Williams 在 Lancet Respir Med 29:333-335, 2021；Hayawi 在 Vaccines 9:1305, 2021）引起的新冠病毒的临床效果，包括目前已确定的（Alpha、Beta、Gamma、Delta、Omicron）和未来可能出现的新变异株。如果能够预测任何变异株的疫苗效力，这将是非常有价值的。在这里，我们根据变异株与野生型刺突糖蛋白在计算上的高结合亲和力与一组常见的人类白细胞抗原 II 类分子的重叠程度，对任何 SARS-CoV-2 变异株的预测疫苗效力进行了评估，这些分子是启动抗体产生所必需的（Blum 等人在 Annu Rev Immunol 31:443-473, 2013）。预测模型具有很强的相关性（r=0.910）和统计学意义（P=0.013）。
