Willmitzer Department, Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany.
School of BioSciences, University of Melbourne, Parkville, VC 3010 Australia.
BMC Bioinformatics. 2021 Dec 20;22(1):605. doi: 10.1186/s12859-021-04510-z.
Upon environmental stimuli, ribosomes are surmised to undergo compositional rearrangements due to abundance changes among proteins assembled into the complex, leading to modulated structural and functional characteristics. Here, we present the ComplexOme-Structural Network Interpreter ([Formula: see text]), a computational method to allow testing whether ribosomal proteins (rProteins) that exhibit abundance changes under specific conditions are spatially confined to particular regions within the large ribosomal complex.
[Formula: see text] translates experimentally determined structures into graphs, with nodes representing proteins and edges the spatial proximity between them. In its first implementation, [Formula: see text] considers rProteins and ignores rRNA and other objects. Spatial regions are defined using a random walk with restart methodology, followed by a procedure to obtain a minimum set of regions that cover all proteins in the complex. Structural coherence is achieved by applying weights to the edges reflecting the physical proximity between purportedly contacting proteins. The weighting probabilistically guides the random-walk path trajectory. Parameter tuning during region selection provides the option to tailor the method to specific biological questions by yielding regions of different sizes with minimum overlaps. In addition, other graph community detection algorithms may be used for the [Formula: see text] workflow, considering that they yield different sized, non-overlapping regions. All tested algorithms result in the same node kernels under equivalent regions. Based on the defined regions, available abundance change information of proteins is mapped onto the graph and subsequently tested for enrichment in any of the defined spatial regions. We applied [Formula: see text] to the cytosolic ribosome structures of Saccharomyces cerevisiae, Oryctolagus cuniculus, and Triticum aestivum using datasets with available quantitative protein abundance change information. We found that in yeast, substoichiometric rProteins depleted from translating polysomes are significantly constrained to a ribosomal region close to the tRNA entry and exit sites.
[Formula: see text] offers a computational method to partition multi-protein complexes into structural regions and a statistical approach to test for spatial enrichments of any given subsets of proteins. [Formula: see text] is applicable to any multi-protein complex given appropriate structural and abundance-change data. [Formula: see text] is publicly available as a GitHub repository https://github.com/MSeidelFed/COSNet_i and can be installed using the python installer pip.
据推测,核糖体在环境刺激下会因组装到复合物中的蛋白质丰度变化而发生组成重排,从而导致结构和功能特性的调节。在这里,我们提出了 ComplexOme-Structural Network Interpreter([公式:见正文]),这是一种计算方法,可以测试在特定条件下丰度发生变化的核糖体蛋白(r 蛋白)是否在大核糖体复合物内的特定区域被空间限制。
[公式:见正文]将实验确定的结构转换为图,其中节点表示蛋白质,边表示它们之间的空间接近度。在其第一个实现中,[公式:见正文]考虑 r 蛋白,忽略 rRNA 和其他对象。使用随机游走带重启方法定义空间区域,然后使用一种获取覆盖复合物中所有蛋白质的最小区域集的过程。通过应用权重来反映据称接触的蛋白质之间的物理接近度,从而实现结构一致性。权重概率引导随机游走路径轨迹。在区域选择期间进行参数调整,可以通过产生具有最小重叠的不同大小的区域来为特定的生物学问题定制方法。此外,可以使用其他图社区检测算法用于[公式:见正文]工作流程,因为它们会产生不同大小、不重叠的区域。在等效区域下,所有测试的算法都会产生相同的节点核。基于定义的区域,将蛋白质的可用丰度变化信息映射到图上,然后测试其在任何定义的空间区域中的富集情况。我们将[公式:见正文]应用于酿酒酵母、兔和小麦的细胞质核糖体结构,使用具有可用定量蛋白质丰度变化信息的数据集。我们发现,在酵母中,从翻译多核糖体中耗尽的亚化学计量 r 蛋白显著限制在接近 tRNA 进入和离开位点的核糖体区域。
[公式:见正文]提供了一种将多蛋白复合物划分为结构区域的计算方法和一种测试任何给定蛋白质子集空间富集的统计方法。[公式:见正文]适用于任何具有适当结构和丰度变化数据的多蛋白复合物。[公式:见正文]作为一个 GitHub 存储库公开可用,网址为 https://github.com/MSeidelFed/COSNet_i,可以使用 python 安装程序 pip 进行安装。