微生物与人类疾病和健康密切相关。了解微生物群落的组成和功能需要广泛的研究。最近,元蛋白质组学已成为对微生物进行全面和深入研究的重要方法。然而,样品处理方面的主要挑战,质谱数据采集,由于微生物群落样本的复杂性和高度异质性,数据分析限制了元蛋白质组学的发展。在元蛋白质组学分析中,优化不同类型样品的预处理方法,采用不同的微生物分离,富集,提取,和裂解方案通常是必要的。类似于单物种蛋白质组学,元蛋白质组学的质谱数据采集模式包括数据依赖采集(DDA)和数据独立采集(DIA).DIA可以从样品中收集全面的肽信息,并具有未来开发的巨大潜力。然而,DIA的数据分析受到元蛋白质组样本复杂性的挑战,这阻碍了元蛋白质组的更深覆盖。数据分析中最重要的步骤是构建蛋白质序列数据库。数据库的大小和完整性不仅强烈影响识别的数量,而且还在物种和功能层面进行分析。当前元蛋白质组数据库构建的金标准是基于元基因组测序的蛋白质序列数据库。基于迭代数据库搜索的公共数据库过滤方法已被证明具有很强的实用价值。以肽为中心的DIA数据分析方法是主流的数据分析策略。深度学习和人工智能的发展将极大地促进精度,覆盖范围,和元蛋白质组学分析的速度。在下游生物信息学分析方面,一系列可以对蛋白质进行物种注释的注释工具,肽,和基因水平已经在最近几年发展,以确定微生物群落的组成。与其他组学方法相比,微生物群落的功能分析是元蛋白质组学的独特功能。元蛋白质组学已成为微生物群落多组学分析的重要组成部分,在覆盖深度方面具有巨大的发展潜力,检测灵敏度,和数据分析的完整性。
Microorganisms are closely associated with human diseases and health. Understanding the composition and function of microbial communities requires extensive research.
Metaproteomics has recently become an important method for throughout and in-depth study of microorganisms. However, major challenges in terms of sample processing, mass spectrometric data acquisition, and data analysis limit the development of
metaproteomics owing to the complexity and high heterogeneity of microbial community samples. In metaproteomic analysis, optimizing the preprocessing method for different types of samples and adopting different microbial isolation, enrichment, extraction, and lysis schemes are often necessary. Similar to those for single-species proteomics, the mass spectrometric data acquisition modes for
metaproteomics include data-dependent acquisition (DDA) and data-independent acquisition (DIA). DIA can collect comprehensive peptide information from a sample and holds great potential for future development. However, data analysis for DIA is challenged by the complexity of metaproteome samples, which hinders the deeper coverage of metaproteomes. The most important step in data analysis is the construction of a protein sequence database. The size and completeness of the database strongly influence not only the number of identifications, but also analyses at the species and functional levels. The current gold standard for metaproteome database construction is the metagenomic sequencing-based protein sequence database. A public database-filtering method based on an iterative database search has been proven to have strong practical value. The peptide-centric DIA data analysis method is a mainstream data analysis strategy. The development of deep learning and artificial intelligence will greatly promote the accuracy, coverage, and speed of metaproteomic analysis. In terms of downstream bioinformatics analysis, a series of annotation tools that can perform species annotation at the protein, peptide, and gene levels has been developed in recent years to determine the composition of microbial communities. The functional analysis of microbial communities is a unique feature of
metaproteomics compared with other omics approaches.
Metaproteomics has become an important component of the multi-omics analysis of microbial communities, and has great development potential in terms of depth of coverage, sensitivity of detection, and completeness of data analysis.