Abstract:
NMR spectroscopy has played a pivotal role in fragment-based drug discovery by coupling detection of weak ligand-target binding with structural mapping of the binding site. Fragment-based screening by NMR has been successfully applied to many soluble protein targets, but only to a limited number of membrane proteins, despite the fact that many drug targets are membrane proteins. This is partly because of difficulties preparing membrane proteins for NMR-especially human membrane proteins-and because of the inherent complexity associated with solution NMR spectroscopy on membrane protein samples, which require the inclusion of membrane-mimetic agents such as micelles, nanodiscs, or bicelles. Here, we developed a generalizable protocol for fragment-based screening of membrane proteins using NMR. We employed two human membrane protein targets, both in fully protonated detergent micelles: the single-pass C-terminal domain of the amyloid precursor protein, C99, and the tetraspan peripheral myelin protein 22 (PMP22). For both we determined the optimal NMR acquisition parameters, protein concentration, protein-to-micelle ratio, and upper limit to the concentration of D6-DMSO in screening samples. Furthermore, we conducted preliminary screens of a plate-format molecular fragment mixture library using our optimized conditions and were able to identify hit compounds that selectively bound to the respective target proteins. It is hoped that the approaches presented here will be useful in complementing existing methods for discovering lead compounds that target membrane proteins.
摘要:
通过将弱配体-靶标结合的检测与结合位点的结构作图耦合,NMR光谱在基于片段的药物发现中发挥了关键作用。基于片段的NMR筛选已成功应用于许多可溶性蛋白质靶标,但仅限于有限数量的膜蛋白,尽管许多药物靶标是膜蛋白。部分原因是难以制备用于NMR的膜蛋白,尤其是人膜蛋白,并且由于与膜蛋白样品的溶液NMR光谱相关的固有复杂性,这需要包含膜模拟剂,如胶束,纳米圆盘,或bicelles。这里,我们开发了使用NMR进行基于片段的膜蛋白筛选的可推广方案.我们采用了两种人膜蛋白靶标,都在完全质子化的洗涤剂胶束中:淀粉样前体蛋白的单程C末端结构域,C99和四逆子外周髓磷脂蛋白22(PMP22)。对于两者,我们都确定了最佳的NMR采集参数,蛋白质浓度,蛋白质与胶束的比例,和筛选样品中D6-DMSO浓度的上限。此外,我们使用我们优化的条件对平板形式的分子片段混合物文库进行了初步筛选,并且能够鉴定选择性结合各自靶蛋白的命中化合物.希望这里提出的方法将有助于补充现有的发现靶向膜蛋白的先导化合物的方法。