Sullivan Mark V, Allabush Francia, Flynn Harriet, Balansethupathy Banushan, Reed Joseph A, Barnes Edward T, Robson Callum, O'Hara Phoebe, Milburn Laura J, Bunka David, Tolley Arron, Mendes Paula M, Tucker James H R, Turner Nicholas W
Leicester School of Pharmacy De Montfort University The Gateway Leicester LE1 9BH UK.
School of Chemical Engineering University of Birmingham Edgbaston Birmingham B15 2TT UK.
Glob Chall. 2023 Mar 20;7(6):2200215. doi: 10.1002/gch2.202200215. eCollection 2023 Jun.
Virus recognition has been driven to the forefront of molecular recognition research due to the COVID-19 pandemic. Development of highly sensitive recognition elements, both natural and synthetic is critical to facing such a global issue. However, as viruses mutate, it is possible for their recognition to wane through changes in the target substrate, which can lead to detection avoidance and increased false negatives. Likewise, the ability to detect specific variants is of great interest for clinical analysis of all viruses. Here, a hybrid aptamer-molecularly imprinted polymer (aptaMIP), that maintains selective recognition for the spike protein template across various mutations, while improving performance over individual aptamer or MIP components (which themselves demonstrate excellent performance). The aptaMIP exhibits an equilibrium dissociation constant of 1.61 nM toward its template which matches or exceeds published examples of imprinting of the spike protein. The work here demonstrates that "fixing" the aptamer within a polymeric scaffold increases its capability to selectivity recognize its original target and points toward a methodology that will allow variant selective molecular recognition with exceptional affinity.
由于新冠疫情,病毒识别已成为分子识别研究的前沿领域。开发高度敏感的天然和合成识别元件对于应对这一全球性问题至关重要。然而,随着病毒变异,其识别可能会因靶标底物的变化而减弱,这可能导致检测逃避和假阴性增加。同样,检测特定变体的能力对于所有病毒的临床分析都非常重要。在此,一种混合适配体-分子印迹聚合物(aptaMIP),它对刺突蛋白模板在各种突变情况下都能保持选择性识别,同时比单个适配体或MIP组件(它们本身表现出优异性能)性能更优。aptaMIP对其模板的平衡解离常数为1.61 nM,与已发表的刺突蛋白印迹示例相当或更高。此处的工作表明,将适配体“固定”在聚合物支架内可提高其选择性识别原始靶标的能力,并指向一种能够实现具有卓越亲和力的变体选择性分子识别的方法。