Van Den Bossche Tim, Beslic Denis, van Puyenbroeck Sam, Suomi Tomi, Holstein Tanja, Martens Lennart, Elo Laura L, Muth Thilo
VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.
Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
Proteomics. 2025 Jan 31:e202400321. doi: 10.1002/pmic.202400321.
Metaproteomics enables the large-scale characterization of microbial community proteins, offering crucial insights into their taxonomic composition, functional activities, and interactions within their environments. By directly analyzing proteins, metaproteomics offers insights into community phenotypes and the roles individual members play in diverse ecosystems. Although database-dependent search engines are commonly used for peptide identification, they rely on pre-existing protein databases, which can be limiting for complex, poorly characterized microbiomes. De novo sequencing presents a promising alternative, which derives peptide sequences directly from mass spectra without requiring a database. Over time, this approach has evolved from manual annotation to advanced graph-based, tag-based, and deep learning-based methods, significantly improving the accuracy of peptide identification. This Viewpoint explores the evolution, advantages, limitations, and future opportunities of de novo sequencing in metaproteomics. We highlight recent technological advancements that have improved its potential for detecting unsequenced species and for providing deeper functional insights into microbial communities.
宏蛋白质组学能够对微生物群落蛋白质进行大规模表征，为深入了解其分类组成、功能活动以及在所处环境中的相互作用提供关键见解。通过直接分析蛋白质，宏蛋白质组学能够洞察群落表型以及各个成员在不同生态系统中所起的作用。尽管依赖数据库的搜索引擎通常用于肽段鉴定，但它们依赖预先存在的蛋白质数据库，这对于复杂且特征描述不足的微生物群落可能存在局限性。从头测序是一种很有前景的替代方法，它直接从质谱中推导肽段序列，无需数据库。随着时间的推移，这种方法已从手动注释发展到基于先进图形、标签和深度学习的方法，显著提高了肽段鉴定的准确性。本观点探讨了宏蛋白质组学中从头测序的演变、优势、局限性和未来机遇。我们重点介绍了最近的技术进步，这些进步提高了其检测未测序物种以及为微生物群落提供更深入功能见解的潜力。
