Institute of Biomedical Chemistry , Moscow 119435 , Russia.
Institute of Semiconductor Physics , Novosibirsk 630090 , Russia.
J Proteome Res. 2019 Dec 6;18(12):4206-4214. doi: 10.1021/acs.jproteome.9b00358. Epub 2019 Oct 28.
This manuscript collects all the efforts of the Russian Consortium, bottlenecks revealed in the course of the C-HPP realization, and ways of their overcoming. One of the main bottlenecks in the C-HPP is the insufficient sensitivity of proteomic technologies, hampering the detection of low- and ultralow-copy number proteins forming the "dark part" of the human proteome. In the frame of MP-Challenge, to increase proteome coverage we suggest an experimental workflow based on a combination of shotgun technology and selected reaction monitoring with two-dimensional alkaline fractionation. Further, to detect proteins that cannot be identified by such technologies, nanotechnologies such as combined atomic force microscopy with molecular fishing and/or nanowire detection may be useful. These technologies provide a powerful tool for single molecule analysis, by analogy with nanopore sequencing during genome analysis. To systematically analyze the functional features of some proteins (CP50 Challenge), we created a mathematical model that predicts the number of proteins differing in amino acid sequence: proteoforms. According to our data, we should expect about 100 000 different proteoforms in the liver tissue and a little more in the HepG2 cell line. The variety of proteins forming the whole human proteome significantly exceeds these results due to post-translational modifications (PTMs). As PTMs determine the functional specificity of the protein, we propose using a combination of gene-centric transcriptome-proteomic analysis with preliminary fractionation by two-dimensional electrophoresis to identify chemically modified proteoforms. Despite the complexity of the proposed solutions, such integrative approaches could be fruitful for MP50 and CP50 Challenges in the framework of the C-HPP.
本文稿汇集了俄罗斯财团的所有努力、C-HPP 实现过程中揭示的瓶颈以及克服这些瓶颈的方法。C-HPP 的主要瓶颈之一是蛋白质组学技术的灵敏度不足,阻碍了低拷贝数和超低拷贝数蛋白质的检测,这些蛋白质构成了人类蛋白质组的“暗区”。在 MP-Challenge 框架内,为了提高蛋白质组的覆盖率,我们建议采用基于组合技术的实验工作流程,包括 shotgun 技术和二维碱性分级与选择反应监测。此外,为了检测无法通过这些技术鉴定的蛋白质,原子力显微镜与分子捕捞和/或纳米线检测相结合的纳米技术可能是有用的。这些技术为单分子分析提供了强大的工具,类似于基因组分析中的纳米孔测序。为了系统地分析某些蛋白质的功能特征(CP50 挑战),我们创建了一个预测氨基酸序列不同的蛋白质数量的数学模型:蛋白质变体。根据我们的数据,我们预计在肝组织中会有大约 100000 种不同的蛋白质变体,在 HepG2 细胞系中则略多一些。由于翻译后修饰(PTMs),形成整个人类蛋白质组的蛋白质种类远远超过这些结果。由于 PTMs 决定了蛋白质的功能特异性,我们建议使用基于基因的转录组-蛋白质组分析与二维电泳初步分级相结合的方法来鉴定化学修饰的蛋白质变体。尽管所提出的解决方案很复杂,但在 C-HPP 的框架内,这种综合方法可能对 MP50 和 CP50 挑战很有成效。