Database-independent de novo metaproteomics of complex microbial communities
peer-reviewed · Cell Systems · 2021
| Date | 2021-05-01 |
| Type | peer-reviewed |
| Venue | Cell Systems |
| Publisher | Elsevier BV |
| Contribution | downstream-application |
| DOI | 10.1016/j.cels.2021.04.003 |
| Citations (OpenAlex) | 67 |
| Venue 2-year citedness | 6.21 |
Preprint version: Quantitative profiling of microbial communities by de novo metaproteomics (2020-08-17, bioRxiv)
Abstract
Metaproteomics has emerged as one of the most promising approaches for determining the composition and metabolic functions of complete microbial communities. Conventional metaproteomics approaches rely on the construction of protein sequence databases and efficient peptide-spectrum-matching algorithms, an approach that is intrinsically biased towards the content of the constructed sequence database. Here, we introduce a highly efficient, database-independent de novo metaproteomics approach and systematically evaluate its quantitative performance using synthetic and natural microbial communities comprising dozens of taxonomic families. Our work demonstrates that the de novo sequencing approach can vastly expand many metaproteomics applications by enabling rapid quantitative profiling and by capturing unsequenced community members that otherwise remain inaccessible for further interpretation. Kleikamp et al., describe a novel de novo metaproteomics pipeline (NovoBridge) that enables rapid community profiling without the need for constructing protein sequence databases.
Methods and tools
- NovoBridge: A database-independent metaproteomics pipeline that maps de novo peptide sequences to taxonomy and function, giving quantitative community profiles without a sample-specific database.
Methods it uses
- PEAKS: Commercial DP-based de novo
Cites (7)
- De Novo Peptide Sequencing Reveals Many Cyclopeptides in the Human Gut and Other Environments (2020) crossref
- Assessing Protein Sequence Database Suitability Using De Novo Sequencing (2020) crossref
- Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry (2018) crossref
- De novo peptide sequencing by deep learning (2017) crossref
- Lessons in de novo peptide sequencing by tandem mass spectrometry (2015) crossref
- De Novo Sequencing and Homology Searching (2012) crossref
- PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry (2003) crossref
Cited by (14)
- Zero-shot de novo peptide sequencing with open posttranslational modification discovery (2026) both
- MegaPX: fast and space-efficient peptide assignment method using IBF-based multi-indexing (2026) both
- Wastewater metaproteomics: tracking microbial and human protein biomarkers (2025) both
- A procedure for controlling the false discovery rate of de novo peptide sequencing (2025) both
- De novo peptide databases enable protein-based stable isotope probing of microbial communities with up to species-level resolution (2025) both
- MARLOWE: Taxonomic Characterization of Unknown Samples for Forensics Using De Novo Peptide Identification (2025) crossref
- Metaproteomics Beyond Databases: Addressing the Challenges and Potentials of De Novo Sequencing (2025) both
- Deep Learning Methods for De Novo Peptide Sequencing (2024) crossref
- De novo peptide databases enable protein-based stable isotope probing of microbial communities with up to species-level resolution (2024) crossref
- Transforming de novo peptide sequencing by explainable AI (2024) crossref
- NovoLign: metaproteomics by sequence alignment (2024) crossref
- NovoLign: metaproteomics by sequence alignment (2024) both
- Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing (2024) both
- Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing (2023) crossref