ArticleScientific reports2022
Deep embeddings to comprehend and visualize microbiome protein space.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- AI-empowered human microbiome research.Gut · 2026Review
- Generation of peptide detectability datasets from single DIA experiment for prediction model fine-tuning.Bioinformatics advances · 2026Article
- Review
- Zero-shot segmentation using embeddings from a protein language model identifies functional regions in the human proteome.PLoS computational biology · 2025Article
- Computational function prediction of bacteria and phage proteins.Microbiology and molecular biology reviews : MMBR · 2025Review
- Network-based representation learning reveals the impact of age and diet on the gut microbial and metabolomic environment of U.S. infants in a randomized controlled feeding trial.bioRxiv : the preprint server for biology · 2025Article
- RBI: a novel algorithm for regulatory-metabolic network model in designing the optimal mutant strain.PeerJ. Computer science · 2025Article
- SAP: Synteny-aware gene function prediction for bacteria using protein embeddings.bioRxiv : the preprint server for biology · 2023Article
- Article
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Authors and funding
6 authors.
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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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