Dipartimento di Scienze Farmaceutiche Pietro Pratesi, Università degli Studi di Milano, Via Mangiagalli 25, 20133 Milan, Italy.
Anal Chim Acta. 2011 Jan 31;685(2):153-61. doi: 10.1016/j.aca.2010.11.025. Epub 2010 Nov 19.
The challenging search of ligands for the amyloidogenic protein β(2)-microglobulin led us to set up an integrated strategy that combines analytical techniques and molecular modelling. Using a chemical library composed of 90 sulphonated molecules and a novel MS screening approach, we initially single out a few new binders. To check for anti-amyloid activity, the best hit obtained was thoroughly studied by docking analysis, affinity and refolding experiments by capillary electrophoresis and in vitro fibrillogenesis Thioflavin T test. Correlative analysis of the overall results obtained from the MS screening led to develop an equation able to identify the key factors of the affinity for β(2)-microglobulin and to predict the affinity for novel derivatives. The proposed equation was then used for a virtual screening of a large compound database. Studies on the new hit thus retrieved confirm the predictive potential of both the equation on affinity and of docking analysis on anti-amyloid activity.
针对淀粉样蛋白β(2)-微球蛋白配体的挑战性搜索促使我们建立了一种集成策略，该策略结合了分析技术和分子建模。使用由 90 个磺化分子组成的化学文库和一种新的 MS 筛选方法，我们最初筛选出了一些新的结合物。为了检查抗淀粉样活性，通过对接分析、毛细管电泳的亲和力和复性实验以及体外纤维形成 Thioflavin T 试验对获得的最佳命中物进行了深入研究。对 MS 筛选获得的整体结果进行相关分析，开发出一种能够识别与β(2)-微球蛋白亲和力关键因素的方程，并预测新型衍生物的亲和力。然后，使用该方程对大型化合物数据库进行虚拟筛选。对新命中物的研究证实了该方程在亲和力和对接分析在抗淀粉样活性方面的预测潜力。
