Bapp Carolin, Mustafa Ahmed Z, Cao Cheng, Wanless Erica J, Stenzel Martina H, Chapman Robert
School of Environmental and Life Sciences, University of Newcastle Callaghan NSW 2308 Australia
Centre for Advanced Macromolecular Design, School of Chemistry, UNSW Sydney Kensington NSW 2052 Australia
Chem Sci. 2025 Jul 1. doi: 10.1039/d5sc04391c.
Using polymers for protein encapsulation can enhance stability in processing environments and prolong activity and half-life . However, finding the best polymer structure for a target protein can be difficult, labour- and cost-intensive. In this study we introduce a high throughput screening approach to identify strong polymer-protein interactions by use of Förster Resonance Energy Transfer (FRET), enabling a rapid read out. We iteratively screened a total of 288 polymers containing varying hydrophilic, hydrophobic, anionic and cationic monomers against a panel of eight different enzymes (glucose oxidase, uricase, manganese peroxidase, bovine serum albumin, carbonic anhydrase, lysozyme, trypsin and casein). By optimisation of the assay conditions it was possible to read out strongly binding polymers at protein concentrations down to 0.1 μM. We were able to use the screening data to locate moderately selective polymer binders in most cases, and elucidate general trends in polymer design that lead to strong binding. Interestingly, these trends are not consistent across proteins, underscoring the value of a screening approach for identification of the best polymers. We applied this technique to identify lead polymers suitable for encapsulation of the important therapeutic protein TNF-related apoptosis-inducing ligand (TRAIL), at a concentration of 0.25 μM (5 μg mL). This approach should be valuable in the design of polymers for either selective protein binding, or for universal protein repulsion, particularly where the protein is too expensive to work with at high concentrations and large volumes.
使用聚合物对蛋白质进行封装可以提高其在加工环境中的稳定性,并延长其活性和半衰期。然而,为目标蛋白质找到最佳的聚合物结构可能很困难,且耗费人力和成本。在本研究中,我们引入了一种高通量筛选方法,通过使用Förster共振能量转移(FRET)来识别聚合物与蛋白质之间的强相互作用,从而实现快速读数。我们对总共288种含有不同亲水、疏水、阴离子和阳离子单体的聚合物,针对一组八种不同的酶(葡萄糖氧化酶、尿酸酶、锰过氧化物酶、牛血清白蛋白、碳酸酐酶、溶菌酶、胰蛋白酶和酪蛋白)进行了迭代筛选。通过优化检测条件,在蛋白质浓度低至0.1 μM时就能够读出强结合聚合物。在大多数情况下,我们能够利用筛选数据找到中等选择性的聚合物结合剂,并阐明聚合物设计中导致强结合的一般趋势。有趣的是,这些趋势在不同蛋白质之间并不一致,这突出了筛选方法对于识别最佳聚合物的价值。我们应用该技术,以0.25 μM(5 μg/mL)的浓度鉴定出适合封装重要治疗性蛋白质肿瘤坏死因子相关凋亡诱导配体(TRAIL)的先导聚合物。这种方法在设计用于选择性蛋白质结合或通用蛋白质排斥的聚合物时应该很有价值,特别是当蛋白质在高浓度和大量使用时成本过高时。
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