ComplexFinder: A software package for the analysis of native protein complex fractionation experiments.
ComplexFinder: A software package for the analysis of native protein complex fractionation experiments.
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ComplexFinder：用于分析天然蛋白质复合物分离实验的软件包
DOI：
10.1016/j.bbabio.2021.148444
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Langer
中科院分区：
文献类型：
--
作者：
Langer
蛋白质复合物的鉴定和单一蛋白质在不同复合物中的定量分布是阐明细胞机制以及生物学和临床相关性的基础。最近引入的方法，复合体分析，结合分馏技术，分离天然蛋白质复合物与高分辨率质谱，并允许以公正的方式识别蛋白质复合物。由于质谱仪器的最新进展，分析时间可以显着减少，同时数千种蛋白质的覆盖率保持不变，这导致数据采集速率增加，并减少了启动此类复杂实验的负担。因此，需要开发新的计算管道来分析这种全面的复杂的配置文件。通常，潜在的复杂地层是通过相关分析组装的。然而，这种分析的主要挑战是，蛋白质可以存在于不同组成的多个复合物中。因此，相同复合物的蛋白质的信号谱可能显示出高的局部相似性，但在所有获得的级分上相关性很差。在这里，我们描述了Complexplane;一个基于Python的计算管道，它可以基于机器学习预测新的蛋白质-蛋白质相互作用，并结合了许多信号分布之间的距离测量。重要的是，每个信号轮廓由类峰模型的集合表示。这些模型允许计算局部相似性，从而实现生物条件之间的以峰为中心的比较和特定复合物的组成的估计。从预测的蛋白质-蛋白质相互作用，蛋白质连接网络的构建，这是用来组装蛋白质到大分子复合物纳入峰中心的信息。Complexplant能够利用各种LC-MS/MS定量策略（包括无标记、SILAC、TMT以及pulseSILAC）对复杂组分析数据进行以峰为中心的分析。源代码可在https://github.com/hnolcol/ComplexFinder上免费获得。
Identification of protein complexes and quantitative distribution of a single protein across different complexes are fundamental to unravel cellular mechanisms and of biological and clinical relevance. A recently introduced method, complexome profiling, combines fractionation techniques to separate native protein complexes with high-resolution mass spectrometry and allows to identify protein complexes in an unbiased manner. Due to recent advances in mass spectrometry instrumentation, the analysis time can be reduced dramatically while the coverage of thousands of proteins remains constant, which leads to an increased data acquisition rate and reduces the burden to initiate such complex experiments. Therefore, the development of novel computational pipelines for the analysis of such comprehensive complexome profiles is required. Usually, potential complex formations are assembled by correlation analysis. However, a major challenge in such an analysis is, that a protein can occur in multiple complexes of varying composition. Hence, signal profiles of proteins of the same complex might show high local similarities but do correlate poorly over all acquired fractions. Here, we describe ComplexFinder; a python-based computational pipeline that enables machine-learning based prediction of novel protein-protein interactions incorporating numerous measures of distance between signal profiles. Importantly, each signal profile is represented by an ensemble of peak-like models. These models allow the calculation of local similarities, enabling peak-centric comparison between biological conditions and the estimation of the composition of specific complexes. From the predicted protein-protein interactions, a protein connectivity network is constructed, which is used to assemble proteins into macromolecular complexes incorporating peak-centric information. ComplexFinder enables the peak-centric analysis of complexome profiling data utilizing various LC-MS/MS quantification strategies including label-free, SILAC, TMT as well as pulseSILAC.The source code is freely available at https://github.com/hnolcol/ComplexFinder.
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