Centre de Biochimie Structurale. INSERM U1054, CNRS UMR 5048, Université de Montpellier, 29, rue de Navacelles, 34090 Montpellier, France; Department of Pharmacy and Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100 Copenhagen, Denmark.
Department of Pharmacy and Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100 Copenhagen, Denmark.
Structure. 2017 Jan 3;25(1):5-15. doi: 10.1016/j.str.2016.10.013. Epub 2016 Nov 23.
Formation of amyloids is the hallmark of several neurodegenerative pathologies. Structural investigation of these complex transformation processes poses significant experimental challenges due to the co-existence of multiple species. The additive nature of small-angle X-ray scattering (SAXS) data allows for probing the evolution of these mixtures of oligomeric states, but the decomposition of SAXS data into species-specific spectra and relative concentrations is burdened by ambiguity. We present an objective SAXS data decomposition method by adapting the multivariate curve resolution alternating least squares (MCR-ALS) chemometric method. The approach enables rigorous and robust decomposition of synchrotron SAXS data by simultaneously introducing these data in different representations that emphasize molecular changes at different time and structural resolution ranges. The approach has allowed the study of fibrillogenic forms of insulin and the familial mutant E46K of α-synuclein, and is generally applicable to any macromolecular mixture that can be probed by SAXS.
淀粉样蛋白的形成是几种神经退行性病变的标志。由于多种物种的共存,这些复杂转化过程的结构研究具有重大的实验挑战。小角 X 射线散射 (SAXS) 数据的加和性质允许探测这些低聚物状态混合物的演变,但 SAXS 数据分解为具有特定物种的光谱和相对浓度受到歧义的困扰。我们通过适应多元曲线分辨交替最小二乘法 (MCR-ALS) 化学计量学方法,提出了一种客观的 SAXS 数据分解方法。该方法通过同时引入不同表示形式的同步加速器 SAXS 数据,强调在不同时间和结构分辨率范围内的分子变化,从而实现了对 SAXS 数据的严格和稳健的分解。该方法已成功应用于胰岛素的纤维形成形式和α-突触核蛋白的家族突变 E46K 的研究,并且通常适用于可以通过 SAXS 探测的任何大分子混合物。