B1 Other refereed article (e.g., editorial, letter, comment) in a scientific journal
Metaproteomics Beyond Databases: Addressing the Challenges and Potentials of De Novo Sequencing
Authors: van den Bossche, Tim; Beslic, Denis; van Puyenbroeck, Sam; Suomi, Tomi; Holstein, Tanja; Martens, Lennart; Elo, Laura L.; Muth, Thilo
Publisher: WILEY
Publishing place: HOBOKEN
Publication year: 2025
Journal: Proteomics
Journal name in source: PROTEOMICS
Journal acronym: PROTEOMICS
Article number: e202400321
Number of pages: 11
ISSN: 1615-9853
eISSN: 1615-9861
DOI: https://doi.org/10.1002/pmic.202400321
Web address : https://doi.org/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.
Funding information in the publication:
T.V.D.B. acknowledges funding from the Research Foundation Flanders (FWO) [1286824N]. T.H. acknowledges funding from the Joachim-Herz Foundation. L.M. acknowledges funding by the Research Foundation Flanders (FWO) (G010023N, G028821N), a Ghent University Concerted Research Action (BOF21/GOA/033), and the European Union's Horizon Europe Programme (101080544, 101103253, 101195186, and 10119173). L.L.E. acknowledges funding from the Research Council of Finland [329278, 341342].
