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奇异值分解辅助的伪主成分分析:一种从时间分辨数据中加速并改进最佳动力学模型确定的新方法。

SVD-aided pseudo principal-component analysis: A new method to speed up and improve determination of the optimum kinetic model from time-resolved data.

作者信息

Oang Key Young, Yang Cheolhee, Muniyappan Srinivasan, Kim Jeongho, Ihee Hyotcherl

机构信息

Department of Chemistry, Inha University , Incheon 22212, South Korea.

出版信息

Struct Dyn. 2017 Apr 5;4(4):044013. doi: 10.1063/1.4979854. eCollection 2017 Jul.

Abstract

Determination of the optimum kinetic model is an essential prerequisite for characterizing dynamics and mechanism of a reaction. Here, we propose a simple method, termed as singular value decomposition-aided pseudo principal-component analysis (SAPPA), to facilitate determination of the optimum kinetic model from time-resolved data by bypassing any need to examine candidate kinetic models. We demonstrate the wide applicability of SAPPA by examining three different sets of experimental time-resolved data and show that SAPPA can efficiently determine the optimum kinetic model. In addition, the results of SAPPA for both time-resolved X-ray solution scattering (TRXSS) and transient absorption (TA) data of the same protein reveal that global structural changes of protein, which is probed by TRXSS, may occur more slowly than local structural changes around the chromophore, which is probed by TA spectroscopy.

摘要

确定最佳动力学模型是表征反应动力学和机理的重要前提。在此,我们提出一种简单的方法,称为奇异值分解辅助伪主成分分析(SAPPA),通过绕过检查候选动力学模型的任何需求,便于从时间分辨数据中确定最佳动力学模型。我们通过检查三组不同的实验时间分辨数据来证明SAPPA的广泛适用性,并表明SAPPA可以有效地确定最佳动力学模型。此外,针对同一蛋白质的时间分辨X射线溶液散射(TRXSS)和瞬态吸收(TA)数据的SAPPA结果表明,由TRXSS探测的蛋白质整体结构变化可能比由TA光谱探测的发色团周围局部结构变化发生得更慢。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95f4/5382018/a7afe9346347/SDTYAE-000004-044013_1-g001.jpg

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