Quantitative profiling of microbial communities by de novo metaproteomics

preprint · bioRxiv · 2020

preprint · bioRxiv · 2020. Hugo B.C. Kleikamp et al. Metaproteomics has emerged as one of the most promising approaches for determining the composition and…

Date	2020-08-17
Type	preprint
Venue	bioRxiv
Publisher	openRxiv
Contribution	downstream-application
DOI	10.1101/2020.08.16.252924
Citations (OpenAlex)	67

Peer-reviewed version: Database-independent de novo metaproteomics of complex microbial communities (2021-05-01, Cell Systems)

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 however, rely on the construction of protein sequence databases and efficient peptide-spectrum matching algorithms. Thereby, very large sequence databases impact on computational efforts and sensitivity. More recently, advanced de novo sequencing strategies–which annotate peptide sequences without the requirement for a database–have become (again) increasingly proposed for proteomics applications. Such approaches would vastly expand many metaproteomics applications by enabling rapid community profiling and by capturing unsequenced community members, which otherwise remain inaccessible for further interpretation. Nevertheless, because of the lack of efficient pipelines and validation procedures, those strategies have only rarely been employed for community proteomics. Here we report on a newly established de novo metaproteomics pipeline which was evaluated for its quantitative performance using synthetic and natural communities. Additionally, we introduce a novel validation strategy and investigate the actual content of community members within community proteomics data.

Authors

1. Hugo B.C. Kleikamp · Delft University of Technology, University of Antwerp
2. Mario Pronk · Delft University of Technology
3. Claudia G. Tugui · Delft University of Technology
4. Leonor Guedes da Silva · Delft University of Technology
5. Ben Abbas · Delft University of Technology
6. Yue Mei Lin · Delft University of Technology
7. Mark C. M. van Loosdrecht · Delft University of Technology
8. Martin Pabst · Delft University of Technology

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

Data used

• Quantification of mock microbial communities with metagenomes, 16S rRNA gene amplicons and metaproteomics (as deposited) · PXD006118

Cites (7)

• De Novo Peptide Sequencing Reveals Many Cyclopeptides in the Human Gut and Other Environments (2020) both
• Assessing Protein Sequence Database Suitability Using De Novo Sequencing (2020) both
• MetaNovo: a probabilistic approach to peptide discovery in complex metaproteomic datasets (2019) both
• Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry (2018) both
• Lessons in de novo peptide sequencing by tandem mass spectrometry (2015) both
• De Novo Sequencing and Homology Searching (2012) both
• PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry (2003) crossref

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