Kovács Dániel, Bodor Andrea
ELTE, Eötvös Loránd University, Institute of Chemistry, Analytical and BioNMR Laboratory Pázmány Péter sétány 1/A Budapest 1117 Hungary
Eötvös Loránd University, Hevesy György PhD School of Chemistry Pázmány Péter sétány 1/A Budapest 1117 Hungary.
RSC Adv. 2023 Mar 31;13(15):10182-10203. doi: 10.1039/d3ra00977g. eCollection 2023 Mar 27.
In studying secondary structural propensities of proteins by nuclear magnetic resonance (NMR) spectroscopy, secondary chemical shifts (SCSs) serve as the primary atomic scale observables. For SCS calculation, the selection of an appropriate random coil chemical shift (RCCS) dataset is a crucial step, especially when investigating intrinsically disordered proteins (IDPs). The scientific literature is abundant in such datasets, however, the effect of choosing one over all the others in a concrete application has not yet been studied thoroughly and systematically. Hereby, we review the available RCCS prediction methods and to compare them, we conduct statistical inference by means of the nonparametric sum of ranking differences and comparison of ranks to random numbers (SRD-CRRN) method. We try to find the RCCS predictors best representing the general consensus regarding secondary structural propensities. The existence and the magnitude of resulting differences on secondary structure determination under varying sample conditions (temperature, pH) are demonstrated and discussed for globular proteins and especially IDPs.
在通过核磁共振（NMR）光谱研究蛋白质的二级结构倾向时，二级化学位移（SCS）是主要的原子尺度可观测值。对于SCS计算，选择合适的随机卷曲化学位移（RCCS）数据集是关键步骤，特别是在研究内在无序蛋白质（IDP）时。科学文献中有大量此类数据集，然而，在具体应用中选择一个数据集而非其他数据集的影响尚未得到全面系统的研究。在此，我们回顾了可用的RCCS预测方法，并通过非参数排名差异总和与排名与随机数比较（SRD-CRRN）方法进行统计推断来比较它们。我们试图找到最能代表关于二级结构倾向的普遍共识的RCCS预测器。对于球状蛋白质，特别是IDP，展示并讨论了在不同样品条件（温度、pH）下二级结构测定中产生的差异的存在和大小。
