Deep embeddings to comprehend and visualize microbiome protein space
Open accessPublished version of a preprint
Abstract
Understanding the function of microbial proteins is essential to reveal the clinical potential of the microbiome. The application of high-throughput sequencing technologies allows for fast and increasingly cheaper acquisition of data from microbial communities. However, many of the inferred protein sequences are novel and not catalogued, hence the possibility of predicting their function through conventional homology-based approaches is limited, which indicates the need for further research on alignment-free methods. Here, we leverage a deep-learning-based representation of proteins to assess its utility in alignment-free analysis of microbial proteins. We trained a language model on the Unified Human Gastrointestinal Protein catalogue and validated the resulting protein representation on the bacterial part of the SwissProt database. Finally, we present a use case on proteins involved in SCFA metabolism. Results indicate that the deep learning model manages to accurately represent features related to protein structure and function, allowing for alignment-free protein analyses. Technologies that contextualize metagenomic data are a promising direction to deeply understand the microbiome.
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Cites
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“On the other hand, the most recent findings show the superiority of Transformer-based architectures 23,40,70 in protein informatics.”
BackgroundProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
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“Moreover, we have chosen the LSTM architecture as it gave the best results in Remote Homology detection in the TAPE benchmark 39 and achieved superior performance over Transformer-based architecture in the ProtTrans benchmark 44 .”
MethodResultUnified rational protein engineering with sequence-based deep representation learning
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- Authors
- Odrzywolek, K., Karwowska, Z., Majta, J., Byrski, A., Milanowska-Zabel, K., & Kosciolek, T.
- Affiliations
- Jagiellonian University (PL)
- AGH University of Krakow (PL)
- Venue
- Scientific Reports
- Published
- 2022-06-20
- Volume
- 12
- Issue
- 1
- Pages
- 10332-10332
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC9209496
- OpenAlex
- W4283171569
- Cited by
- 22
- References
- 10
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 3 · 2023: 2 · 2022: 1
- FWCI
- 1.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Homo Politicus (Academy of Humanities and Economics in Lodz) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Regional Development Fund (POIR.01.01.01-00-0347/17)
- Narodowe Centrum Nauki (2019/35/D/NZ2/04353)
- Ministerstwo Edukacji i Nauki
- Narodowa Agencja Wymiany Akademickiej (PPN/PPO/2018/1/00014)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Keywords
- deep embedding, protein representation, microbial protein, microbiome, language models, protein function prediction, short-chain fatty acid metabolism, metagenomics, protein structure, Swiss-Prot
- MeSH terms
- Bacteria, Humans, Proteins, Metagenome, Metagenomics, High-Throughput Nucleotide Sequencing, Microbiota
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