Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.

元蛋白质组学是对多生物系统的集体蛋白质组成的研究，为微生物群落的生物多样性以及微生物与其宿主或环境之间复杂的功能相互作用提供了深入的见解。因此，宏蛋白质组学已成为微生物学和相关医学应用等各个领域不可或缺的工具。相应数据集分析中的计算挑战与纯培养蛋白质组学的计算挑战不同，例如，由于样本的复杂性更高，参考数据库更大，需要特定的计算管道。相应的数据分析通常包含许多必须紧密同步的手动步骤。借助 MetaProteomeAnalyzer 和 Prophane，我们建立了两个专为宏蛋白质组学开发和优化的开源软件解决方案。除其他功能外，肽谱匹配通过结合不同的搜索引擎得到改进，并且与类似工具相比，元蛋白质组注释受益于最全面的可用数据库集（例如 NCBI、UniProt、EggNOG、PFAM 和 CAZy）。该协议中描述的工作流程结合了这两种工具，并引导用户完成整个数据分析过程，包括蛋白质数据库创建、数据库搜索、蛋白质分组和注释以及结果可视化。据我们所知，该协议提供了迄今为止最全面、详细和灵活的宏蛋白质组数据分析指南。虽然初学者可以在合理的时间内（几个小时，取决于蛋白质数据库的大小以及已识别的肽和推断的肽的数量）获得强大的、易于使用的、最先进的数据分析蛋白质），高级用户受益于工作流程的灵活性和适应性。