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Polarized Raman spectroscopy for enhanced quantification of protein concentrations in an aqueous mixture

机译:偏振拉曼光谱法可增强定量分析水性混合物中蛋白质的浓度

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Raman spectroscopy (RS) for selective quantification of protein species in mixed solutions holds enormous potential for advancing protein detection technology to significantly faster, cheaper, and less technically demanding platforms. However, even with powerful computational methods such as nonlinear least squares regression, protein quantification in such complex systems suffers from relatively poor accuracy, especially in comparison with established methods. In this work, a combination of the expanded set of spectral information provided by polarized Raman spectroscopy (PRS) that is otherwise unavailable in conventional RS was, to our knowledge, explored to enhance the quantitative accuracy and robustness of protein quantification for the first time. A mixture containing two proteins, lysozyme and alpha-amylase, was used as a model system to demonstrate enhanced quantitative accuracy and robustness of selective protein quantification using PRS. The concentrations of lysozyme and alpha-amylase in mixtures were estimated using data obtained from both traditional RS and PRS. A new method was developed to select highly sensitive peaks for accurate concentration estimation to take advantage of additional spectra offered by PRS. The root-mean squared errors (RMSE) of estimation using traditional RS and PRS were compared. A drastic improvement in RMSE was observed from traditional RS to PRS, where the RMSEs of alpha-amylase and lysozyme concentrations decreased by 11 and 7 times, respectively. Therefore, this technique is a successful demonstration in achieving greater accuracy and reproducibility in the estimation of protein concentration in a mixture, and it could play a significant role in future multiplexed protein quantification platforms. Copyright (C) 2015 John Wiley & Sons, Ltd.
机译:拉曼光谱(RS)用于选择性定量混合溶液中的蛋白质种类,具有将蛋白质检测技术发展到更快,更便宜,技术要求更低的平台的巨大潜力。但是,即使采用了强大的计算方法(例如非线性最小二乘回归),在这种复杂系统中进行蛋白质定量也存在相对较差的准确性,尤其是与已建立的方法相比。在这项工作中,据我们所知,我们首次探索了极化拉曼光谱(PRS)提供的扩展光谱信息集的组合,而这在传统RS中是无法获得的,这是第一次探索来提高蛋白质定量的定量准确性和鲁棒性。包含两种蛋白质(溶菌酶和α-淀粉酶)的混合物用作模型系统,以证明使用PRS的选择性蛋白质定量分析的定量准确性和耐用性增强。使用从传统RS和PRS获得的数据估算混合物中溶菌酶和α-淀粉酶的浓度。开发了一种新方法来选择高度敏感的峰以进行准确的浓度估算,以利用PRS提供的其他光谱。比较了使用传统RS和PRS进行估计的均方根误差(RMSE)。从传统RS到PRS,RMSE显着改善,α-淀粉酶和溶菌酶浓度的RMSE分别降低了11倍和7倍。因此,该技术是成功地证明在混合物中蛋白质浓度的估计中具有更高的准确性和可重复性,并且可以在未来的多重蛋白质定量平台中发挥重要作用。版权所有(C)2015 John Wiley&Sons,Ltd.

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