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Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold
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Abstract
The open-source de novo protein-level assembler, Plass ( https://plass.mmseqs.com ), assembles six-frame-translated sequencing reads into protein sequences. It recovers 2–10 times more protein sequences from complex metagenomes and can assemble huge datasets. We assembled two redundancy-filtered reference protein catalogs, 2 billion sequences from 640 soil samples (soil reference protein catalog) and 292 million sequences from 775 marine eukaryotic metatranscriptomes (marine eukaryotic reference catalog), the largest free collections of protein sequences. The protein-level assembler can assemble protein catalogs from raw metagenomic sequencing data, enabling large-scale metagenomics studies.
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“To address this issue, we leveraged the widely used pre-trained model ProtT5-XL-U50 (ProtT5) [38], which was pre-trained and fine-tuned on BFD (Big Fantastic Database) [39] and UniRef50 [40], covering target sequences of multiple biological species.”
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“For the query sequence, MSA is generated by iteratively searching against UniRef30 [40] and BFD [41] sequence databases using HHblits [38].”
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Self-citationAdditional file 1 of Protein embedding based alignment
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“Additionally, direct protein assembly from metagenomic reads was performed using PLASS [23].”
Intelligent Methods in Behavioral Studies on Animal Models
Babenko, Y., & Romanov, V. (2024) · International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI) · doi:10.55056/ceur-ws.org/vol-3777/paper17.pdf
“…filtered reference protein catalogs were assembled, 2 billion sequences from 640 soil samples (soil reference protein catalog) and 292 million sequences from 775 marine eukaryotic metatranscriptomes (marine eukaryotic reference catalog), the largest free collection of protein sequences [20].”
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“The MSA is generated by searching protein databases such as Uniref [39], MGnify [40], BFD [41], and PDB [42] using JackHMMER [43] and HHsearch [44].”
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“MSAs were generated for each protein using the default data pipeline implemented in AlphaFold3 [15], which utilizes JackHMMER [32] and HHBlits [33] searches on UniRef90 [34], UniProt [31], Uniclust30 [35], BFD [36] and MGnify [37].”
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FAIRsharing record for: AlphaFold Protein Structure Database
FAIRsharing Team, & Liang, Y. (2025) · FAIRsharing.org · doi:10.25504/fairsharing.246086
Metadata
- Authors
- Steinegger, M., Mirdita, M., & Söding, J.
- Affiliations
- Seoul National University (KR)
- Max Planck Institute for Biophysical Chemistry (DE)
- Corresponding author
- Steinegger, Martin
- Söding, Johannes
- Venue
- Nature Methods
- Published
- 2019-06-24
- Volume
- 16
- Issue
- 7
- Pages
- 603-606
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2949342052
- Cited by
- 445
- References
- 31
- Citations by year
- 2026: 38 · 2025: 78 · 2024: 101 · 2023: 78 · 2022: 71 · 2021: 50 · 2020: 23 · 2019: 6
- FWCI
- 12.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Methods (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Funders
- Materials and Energy Research Center
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- metagenomics, metatranscriptomics, soil metagenome
- MeSH terms
- Amino Acid Sequence, Codon, Proteins, Open Reading Frames, Metagenomics
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