ReviewComputational and structural biotechnology journal2022
Using metagenomic data to boost protein structure prediction and discovery.
Review in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- metaLoc: protein localisation prediction workflow.Bioinformatics advances · 2026Article
- CASP16 Protein Monomer Structure Prediction Assessment.Proteins · 2026Article
- ProteoSeeker: A Feature-Rich Metagenomic Analysis Tool for Accessible and Comprehensive Metagenomic Exploration.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- PyPropel: a Python-based tool for efficiently processing and characterising protein data.BMC bioinformatics · 2025Article
- Exploring Evolution to Uncover Insights Into Protein Mutational Stability.Molecular biology and evolution · 2025Article
- Analysis of metagenomic data.Nature reviews. Methods primers · 2025Article
- Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Precision enzyme discovery through targeted mining of metagenomic data.Natural products and bioprospecting · 2024Review
- Article
- Mammals' sperm microbiome: current knowledge, challenges, and perspectives on metagenomics of seminal samples.Frontiers in microbiology · 2023Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the past decade, metagenomic sequencing approaches have been providing an ever-increasing amount of protein sequence data at an astonishing rate. These constitute an invaluable source of information which has been exploited in various research fields such as the study of the role of the gut microbiota in human diseases and aging. However, only a small fraction of all metagenomic sequences collected have been functionally or structurally characterized, leaving much of them completely unexplored. Here, we review how this information has been used in protein structure prediction and protein discovery. We begin by presenting some widely used metagenomic databases and analyze in detail how metagenomic data has contributed to the impressive improvement in the accuracy of structure prediction methods in recent years. We then examine how metagenomic information can be exploited to annotate protein sequences. More specifically, we focus on the role of metagenomes in the discovery of enzymes and new CRISPR-Cas systems, and in the identification of antibiotic resistance genes. With this review, we provide an overview of how metagenomic data is currently revolutionizing our understanding of protein science.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.