Deuteros 2.0: peptide-level significance testing of data from hydrogen deuterium exchange mass spectrometry.

Deuteros 2.0: peptide-level significance testing of data from hydrogen deuterium exchange mass spectrometry.
复制标题

DOI:
10.1093/bioinformatics/btaa677
复制
发表时间:
2021-04-19
期刊:
Bioinformatics (Oxford, England)
影响因子:
--
通讯作者:
Politis A
Politis A
中科院分区:
其他
文献类型:
--
作者:
Lau AM;Claesen J;Hansen K;Politis A

文献摘要

参考文献

被引文献

相似文献

氢氘交换质谱(HDX-MS)越来越成为监测蛋白质结构动力学变化的常规方法。差分HDX-MS允许比较蛋白质状态,例如在不存在或存在配体的情况下。这可用于将构象变化归因于结合事件,从而允许绘制整个构象网络。因此,随着更多的州被引入研究系统,必要的跨州比较的数量迅速增加。目前很少有软件包可以提供快速和信息丰富的HDX-MS数据集比较,提供统计分析和高级可视化的软件包更少。根据我们原始软件Deuteros的反馈,我们推出了Deuteros 2.0,该软件经过重新设计,在HDX-MS分析管道中发挥了更大的作用。Deuteros 2.0具有一系列用于反交换校正、数据汇总、肽级统计分析和高级数据绘图功能的设施。Deuteros 2.0可以在Apache 2.0许可下从https://github.com/andymlau/Deuteros_2.0下载Windows和MacOS。
Hydrogen deuterium exchange mass spectrometry (HDX-MS) is becoming increasing routine for monitoring changes in the structural dynamics of proteins. Differential HDX-MS allows comparison of protein states, such as in the absence or presence of a ligand. This can be used to attribute changes in conformation to binding events, allowing the mapping of entire conformational networks. As such, the number of necessary cross-state comparisons quickly increases as additional states are introduced to the system of study. There are currently very few software packages available that offer quick and informative comparison of HDX-MS datasets and even fewer which offer statistical analysis and advanced visualization. Following the feedback from our original software Deuteros, we present Deuteros 2.0 which has been redesigned from the ground up to fulfill a greater role in the HDX-MS analysis pipeline. Deuteros 2.0 features a repertoire of facilities for back exchange correction, data summarization, peptide-level statistical analysis and advanced data plotting features. Deuteros 2.0 can be downloaded for both Windows and MacOS from https://github.com/andymlau/Deuteros_2.0 under the Apache 2.0 license.
DOI: 10.1038/s41592-019-0459-y
发表时间: 2019-07-01
期刊: NATURE METHODS
影响因子: 48
作者:
Masson, Glenn R.;Burke, John E.;Rand, Kasper D.
通讯作者: Rand, Kasper D.
DOI: 10.1093/bioinformatics/btz022
发表时间: 2019-09-01
期刊: BIOINFORMATICS
影响因子: 5.8
作者:
Lau, Andy M. C.;Ahdash, Zainab;Politis, Argyris
通讯作者: Politis, Argyris
DOI: 10.1111/j.2517-6161.1995.tb02031.x
发表时间: 1995-01-01
影响因子: 5.8
作者:
BENJAMINI, Y;HOCHBERG, Y
通讯作者: HOCHBERG, Y
DOI: 10.1002/jcc.20084
发表时间: 2004-10-01
影响因子: 3
作者:
Pettersen, EF;Goddard, TD;Ferrin, TE
通讯作者: Ferrin, TE
DOI: 10.1007/978-1-62703-392-3_11
发表时间: 2013-01-01
期刊: Methods in molecular biology (Clifton, N.J.)
影响因子: --
作者:
Wales, Thomas E;Eggertson, Michael J;Engen, John R
通讯作者: Engen, John R