SEDFIT中用于沉降速度分析的自动化接口。

IF 4.3 2区 生物学
PLoS Computational Biology Pub Date : 2023-09-05 eCollection Date: 2023-09-01 DOI:10.1371/journal.pcbi.1011454
Peter Schuck, Samuel C To, Huaying Zhao
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引用次数: 0

摘要

沉淀速度分析超速离心(SV-AUC)是研究生物制药工业中颗粒尺寸分布的不可或缺的工具,例如,用于表征蛋白质治疗剂和疫苗产品。特别是,SEDFIT软件中的扩散-去卷积沉降系数分布分析由于其相对较高的分辨率和灵敏度而得到了广泛的应用。然而,缺乏与良好生产规范(GMP)兼容的合适软件阻碍了SV-AUC在这种监管环境中的使用。为了解决这个问题,我们为SEDFIT创建了一个接口,这样它就可以作为一个自动生成的模块,通过命令行参数输入受控数据,并在文件中输出关键结果。该接口可以集成在定制的GMP兼容软件中,也可以集成在为复制或相关样品提供文档和荟萃分析的脚本中,例如,以简化对大系列实验数据的分析,例如蛋白质相互作用研究中的结合等温线分析。为了测试和演示这种方法,我们提供了一个MATLAB脚本mlSEDFIT。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An automated interface for sedimentation velocity analysis in SEDFIT.

An automated interface for sedimentation velocity analysis in SEDFIT.

An automated interface for sedimentation velocity analysis in SEDFIT.

An automated interface for sedimentation velocity analysis in SEDFIT.

Sedimentation velocity analytical ultracentrifugation (SV-AUC) is an indispensable tool for the study of particle size distributions in biopharmaceutical industry, for example, to characterize protein therapeutics and vaccine products. In particular, the diffusion-deconvoluted sedimentation coefficient distribution analysis, in the software SEDFIT, has found widespread applications due to its relatively high resolution and sensitivity. However, a lack of suitable software compatible with Good Manufacturing Practices (GMP) has hampered the use of SV-AUC in this regulatory environment. To address this, we have created an interface for SEDFIT so that it can serve as an automatically spawned module with controlled data input through command line parameters and output of key results in files. The interface can be integrated in custom GMP compatible software, and in scripts that provide documentation and meta-analyses for replicate or related samples, for example, to streamline analysis of large families of experimental data, such as binding isotherm analyses in the study of protein interactions. To test and demonstrate this approach we provide a MATLAB script mlSEDFIT.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology 生物-生化研究方法
CiteScore
7.10
自引率
4.70%
发文量
820
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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