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MOLASS:用于自动处理通过小角X射线散射和紫外可见光谱结合尺寸排阻色谱法获得的矩阵数据的软件。

MOLASS: Software for automatic processing of matrix data obtained from small-angle X-ray scattering and UV-visible spectroscopy combined with size-exclusion chromatography.

作者信息

Yonezawa Kento, Takahashi Masatsuyo, Yatabe Keiko, Nagatani Yasuko, Shimizu Nobutaka

机构信息

Structural Biology Research Center, Institute of Materials Structure Science, High Energy Accelerator Research Organization (KEK), Tsukuba, Ibaraki 305-0801, Japan.

Center for Digital Green-innovation, Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan.

出版信息

Biophys Physicobiol. 2023 Jan 7;20(1):e200001. doi: 10.2142/biophysico.bppb-v20.0001. eCollection 2023.

Abstract

Recent small-angle X-ray scattering (SAXS) for biological macromolecules (BioSAXS) is generally combined with size-exclusion chromatography (SEC-SAXS) at synchrotron facilities worldwide. For SEC-SAXS analysis, the final scattering profile for the target molecule is calculated from a large volume of continuously collected data. It would be ideal to automate this process; however, several complex problems exist regarding data measurement and analysis that have prevented automation. Here, we developed the analytical software MOLASS (Matrix Optimization with Low-rank factorization for Automated analysis of SEC-SAXS) to automatically calculate the final scattering profiles for solution structure analysis of target molecules. In this paper, the strategies for automatic analysis of SEC-SAXS data are described, including correction of baseline-drift using a low percentile method, optimization of peak decompositions composed of multiple scattering components using modified Gaussian fitting against the chromatogram, and rank determination for extrapolation to infinite dilution. In order to easily calculate each scattering component, the Moore-Penrose pseudo-inverse matrix is adopted as a basic calculation. Furthermore, this analysis method, in combination with UV-visible spectroscopy, led to better results in terms of accuracy in peak decomposition. Therefore, MOLASS will be able to smoothly suggest to users an accurate scattering profile for the subsequent structural analysis.

摘要

近期,用于生物大分子的小角X射线散射(BioSAXS)通常在全球范围内的同步加速器设施中与尺寸排阻色谱法(SEC-SAXS)相结合。对于SEC-SAXS分析,目标分子的最终散射图谱是根据大量连续收集的数据计算得出的。实现这一过程的自动化将是理想的;然而,在数据测量和分析方面存在几个复杂问题,阻碍了自动化的实现。在此,我们开发了分析软件MOLASS(用于SEC-SAXS自动分析的低秩分解矩阵优化),以自动计算目标分子溶液结构分析的最终散射图谱。本文描述了SEC-SAXS数据自动分析的策略,包括使用低百分位数方法校正基线漂移、使用针对色谱图的改进高斯拟合优化由多个散射成分组成的峰分解,以及确定外推至无限稀释的秩。为了轻松计算每个散射成分,采用摩尔-彭罗斯伪逆矩阵作为基本计算方法。此外,这种分析方法与紫外可见光谱相结合,在峰分解的准确性方面取得了更好的结果。因此,MOLASS将能够顺利地向用户提供准确的散射图谱,用于后续的结构分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b8/10203098/abc1ac387d10/20_e200001-g001.jpg

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