Zhang Lu, Cao Ruifang, Mao Tiantian, Wang Yuan, Lv Daqing, Yang Liangfu, Tang Yuanyuan, Zhou Mengdi, Ling Yunchao, Zhang Guoqing, Qiu Tianyi, Cao Zhiwei
Department of Gastroenterology, Shanghai 10th People's Hospital and School of Life Sciences and Technology, Tongji University, Shanghai, China.
CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Shanghai, China.
Front Cell Dev Biol. 2021 Sep 20;9:713188. doi: 10.3389/fcell.2021.713188. eCollection 2021.
Since the outbreak of SARS-CoV-2, antigenicity concerns continue to linger with emerging mutants. As recent variants have shown decreased reactivity to previously determined monoclonal antibodies (mAbs) or sera, monitoring the antigenicity change of circulating mutants is urgently needed for vaccine effectiveness. Currently, antigenic comparison is mainly carried out by immuno-binding assays. Yet, an online predicting system is highly desirable to complement the targeted experimental tests from the perspective of time and cost. Here, we provided a platform of SAS (Spike protein Antigenicity for SARS-CoV-2), enabling predicting the resistant effect of emerging variants and the dynamic coverage of SARS-CoV-2 antibodies among circulating strains. When being compared to experimental results, SAS prediction obtained the consistency of 100% on 8 mAb-binding tests with detailed epitope covering mutational sites, and 80.3% on 223 anti-serum tests. Moreover, on the latest South Africa escaping strain (B.1.351), SAS predicted a significant resistance to reference strain at multiple mutated epitopes, agreeing well with the vaccine evaluation results. SAS enables auto-updating from GISAID, and the current version collects 867K GISAID strains, 15.4K unique spike (S) variants, and 28 validated and predicted epitope regions that include 339 antigenic sites. Together with the targeted immune-binding experiments, SAS may be helpful to reduce the experimental searching space, indicate the emergence and expansion of antigenic variants, and suggest the dynamic coverage of representative mAbs/vaccines among the latest circulating strains. SAS can be accessed at https://www.biosino.org/sas.
自严重急性呼吸综合征冠状病毒2（SARS-CoV-2）爆发以来，新出现的突变株引发了对抗原性的持续担忧。由于最近的变异株对先前确定的单克隆抗体（mAb）或血清的反应性降低，因此迫切需要监测循环突变株的抗原性变化以评估疫苗效果。目前，抗原性比较主要通过免疫结合试验进行。然而，从时间和成本的角度来看，非常需要一个在线预测系统来补充针对性的实验测试。在此，我们提供了一个SAS（SARS-CoV-2刺突蛋白抗原性）平台，能够预测新出现变异株的抗性效应以及SARS-CoV-2抗体在循环毒株中的动态覆盖范围。与实验结果相比，SAS预测在8次覆盖详细表位突变位点的mAb结合试验中一致性达100%，在223次抗血清试验中一致性达80.3%。此外，对于最新的南非逃逸株（B.1.351），SAS预测其在多个突变表位对参考株具有显著抗性，这与疫苗评估结果高度吻合。SAS能够从全球共享流感数据倡议组织（GISAID）自动更新，当前版本收集了86.7万个GISAID毒株、1.54万个独特的刺突（S）蛋白变异株以及28个经过验证和预测的表位区域，其中包括339个抗原位点。结合针对性的免疫结合实验，SAS可能有助于缩小实验搜索范围，指示抗原变异株的出现和传播，并提示代表性mAb/疫苗在最新循环毒株中的动态覆盖情况。可通过https://www.biosino.org/sas访问SAS。
