Wang Bangjin, Duan Aihong, Xie Shengming, Zhang Junhui, Yuan Liming, Cao Qiue
Department of Chemistry, Yunnan Normal University Kunming 650500 China.
Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, School of Chemical Science and Technology, Yunnan University Kunming 650091 China.
RSC Adv. 2021 Jul 22;11(41):25524-25529. doi: 10.1039/d1ra00716e. eCollection 2021 Jul 19.
A strategy was designed for the molecular imprinting of magnetic nanoparticles with boric acid affinity (MNPs@MIP) which were then used for the selective recognition and isolation of glycoproteins. FeO nanoparticles were prepared by a solvothermal method and direct silanization by the condensation polymerization of aminopropyltriethoxysilane (APTES). Subsequently, phenylboric acid was functionalized by reductive amination between 2,3-difluoro-4-formyl phenylboric acid (DFFPBA) and the amido group. The resultant FeO@SiO-DFFPBA was then used for the selective adsorption of a glycoprotein template. Finally, a molecularly imprinted layer was covered on the surface nanoparticles by the condensation polymerization of tetraethyl orthosilicate (TEOS). The adsorption capacities of the resultant MNPs@MIP-HRP and MNPs@MIP-OVA to horseradish peroxidase (HRP) or ovalbumin (OVA) were significantly higher than non-imprinted particles (MNPs@NIP). Moreover, the adsorption capacities of MNPs@MIP-HRP and MNPs@MIP-OVA on non-template protein and non-glycoprotein bovine serum albumin (BSA) were significantly lower than those of their respective template proteins, thus indicating that both of the prepared MNPs@MIP exhibited excellent selectivity.
设计了一种用于制备具有硼酸亲和力的磁性纳米颗粒分子印迹材料(MNPs@MIP)的策略,随后将其用于糖蛋白的选择性识别和分离。通过溶剂热法制备FeO纳米颗粒,并通过氨丙基三乙氧基硅烷(APTES)的缩聚反应进行直接硅烷化。随后,通过2,3-二氟-4-甲酰基苯硼酸(DFFPBA)与酰胺基团之间的还原胺化反应对苯硼酸进行功能化。然后将所得的FeO@SiO-DFFPBA用于糖蛋白模板的选择性吸附。最后,通过正硅酸四乙酯(TEOS)的缩聚反应在表面纳米颗粒上覆盖分子印迹层。所得的MNPs@MIP-HRP和MNPs@MIP-OVA对辣根过氧化物酶(HRP)或卵清蛋白(OVA)的吸附能力明显高于非印迹颗粒(MNPs@NIP)。此外,MNPs@MIP-HRP和MNPs@MIP-OVA对非模板蛋白和非糖蛋白牛血清白蛋白(BSA)的吸附能力明显低于它们各自的模板蛋白,因此表明所制备的两种MNPs@MIP均表现出优异的选择性。