ArticleProceedings of the National Academy of Sciences of the United States of America2021
Decoding the link of microbiome niches with homologous sequences enables accurately targeted protein structure prediction.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.Nature biotechnology · 2026Article
- Improving AlphaFold2- and AlphaFold3-Based Protein Complex Structure Prediction With MULTICOM4 in CASP16.Proteins · 2026Article
- Boosting AlphaFold protein tertiary structure prediction through MSA engineering and extensive model sampling and ranking in CASP16.Communications biology · 2025Article
- Leveraging Sequence Purification for Accurate Prediction of Multiple Conformational States with AlphaFold2.Research square · 2025Article
- Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction.Biomolecules · 2024Review
- One step forward towards deep-learning protein complex structure prediction by precise multiple sequence alignment construction.Clinical and translational medicine · 2024Article
- Petabase-Scale Homology Search for Structure Prediction.Cold Spring Harbor perspectives in biology · 2024Review
- Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data.Nature methods · 2024Article
- Advanced computational tools, artificial intelligence and machine-learning approaches in gut microbiota and biomarker identification.Frontiers in medical technology · 2024Review
- Article
- Petascale Homology Search for Structure Prediction.bioRxiv : the preprint server for biology · 2023Article
- Microbiome-based enrichment pattern mining has enabled a deeper understanding of the biome-species-function relationship.Communications biology · 2023Article
- Designing of thiazolidinones against chicken pox, monkey pox, and hepatitis viruses: A computational approach.Computational biology and chemistry · 2023Article
- Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters.Frontiers in bioinformatics · 2023Review
- Characterization of Treponema denticola Major Surface Protein (Msp) by Deletion Analysis and Advanced Molecular Modeling.Journal of bacteriology · 2022Article
- Review
- Using metagenomic data to boost protein structure prediction and discovery.Computational and structural biotechnology journal · 2022Review
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4 authors.
Funding
Abstract
Information derived from metagenome sequences through deep-learning techniques has significantly improved the accuracy of template free protein structure modeling. However, most of the deep learning-based modeling studies are based on blind sequence database searches and suffer from low efficiency in computational resource utilization and model construction, especially when the sequence library becomes prohibitively large. We proposed a MetaSource model built on 4.25 billion microbiome sequences from four major biomes (Gut, Lake, Soil, and Fermentor) to decode the inherent linkage of microbial niches with protein homologous families. Large-scale protein family folding experiments on 8,700 unknown Pfam families showed that a microbiome targeted approach with multiple sequence alignment constructed from individual MetaSource biomes requires more than threefold less computer memory and CPU (central processing unit) time but generates contact-map and three-dimensional structure models with a significantly higher accuracy, compared with that using combined metagenome datasets. These results demonstrate an avenue to bridge the gap between the rapidly increasing metagenome databases and the limited computing resources for efficient genome-wide database mining, which provides a useful bluebook to guide future microbiome sequence database and modeling development for high-accuracy protein structure and function prediction.
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