The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
DeepES: Deep learning-based enzyme screening to identify orphan enzyme genes
Open accessPreprint — published as 10.1093/bioinformatics/btaf053
Abstract
Motivation: Progress in sequencing technology has led to determination of large numbers of protein sequences, and large enzyme databases are now available. Although many computational tools for enzyme annotation were developed, sequence information is unavailable for many enzymes, known as orphan enzymes. These orphan enzymes hinder sequence similarity-based functional annotation, leading gaps in understanding the association between sequences and enzymatic reactions. Results: Therefore, we developed DeepES, a deep learning-based tool for enzyme screening to identify orphan enzyme genes, focusing on biosynthetic gene clusters and reaction class. DeepES uses protein sequences as inputs and evaluates whether the input genes contain biosynthetic gene clusters of interest by integrating the outputs of the binary classifier for each reaction class. The validation results suggested that DeepES can capture functional similarity between protein sequences, and it can be implemented to explore orphan enzyme genes. By applying DeepES to 4744 metagenome-assembled genomes, we identified candidate genes for 236 orphan enzymes, including those involved in short-chain fatty acid production as a characteristic pathway in human gut bacteria. Availability and implementation: DeepES is available at https://github.com/yamada-lab/DeepES . Model weights and the candidate genes are available at Zenodo ( https://doi.org/10.5281/zenodo.11123900 ).
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Flagged first, then by citations
Research works printed but not linked: 30
- 10.1006/jmbi.1990.9999 · DOI · Paper page
- Buton N , Coste F , Le Cunff Y . Predicting enzymatic function of protein sequences with attention. Bioinformatics 2023;39:btad620. · DOI
- 10.1093/bioinformatics/btab198 · DOI
- 10.1093/NAR/GKAA1025 · DOI · Paper page
- 2018, Absorption and metabolism of yerba mate phenolic compounds in humans, Food Chem, 240, 1028, 10.1016/j.foodchem.2017.08.003 · DOI
- 10.1093/nar/gkaa970 · DOI
- 2021, BiG-FAM: the biosynthetic gene cluster families database, Nucleic Acids Res, 49, D490, 10.1093/nar/gkaa812 · DOI
- 2023, Functional annotation of enzyme-encoding genes using deep learning with transformer layers, Nat Commun, 14, 7370, 10.1038/s41467-023-43216-z · DOI
- 10.1021/ja0466457 · DOI
- 2005, Orphan Enzymes?, Science, 307, 42, 10.1126/science.307.5706.42a · DOI
- 10.1126/science.ade2574 · DOI · Paper page
- 2021, Embeddings from deep learning transfer GO annotations beyond homology, Sci Rep, 11, 1160, 10.1038/s41598-020-80786-0 · DOI
- 2023, Gut Microbiome in Colorectal Cancer: Clinical Diagnosis and Treatment, Genomics Proteomics Bioinformatics, 21, 84, 10.1016/j.gpb.2022.07.002 · DOI
- 10.1038/nchembio.1890 · DOI
- 2016, Identification of Enzyme Genes Using Chemical Structure Alignments of Substrate–Product Pairs, J Chem Inf Model, 56, 510, 10.1021/acs.jcim.5b00216 · DOI
- 10.1021/ci3005379 · DOI
- Parada Venegas D , De la Fuente MK , Landskron G et al. Short Chain Fatty Acids (SCFAs)-Mediated Gut Epithelial and Immune Regulation and Its Relevance for Inflammatory Bowel Diseases. Front Immunol 2019;10:277. · DOI
- Pouliot Y , Karp PD . A survey of orphan enzyme activities. BMC Bioinformatics 2007;8:244. · DOI
- 10.1186/s12885-021-09054-2 · DOI
- 10.1093/nar/gkac1080 · DOI
- 10.1073/pnas.1821905116 · DOI
- 10.7554/eLife.80942 · DOI
- Shiroma H , Darzi Y , Terajima E et al. Enteropathway: the metabolic pathway database for the human gut microbiota. bioRxiv, 2023.06.28.546710, 2023, preprint: not peer reviewed. · DOI
- Sorokina M , Stam M , Médigue C et al. Profiling the orphan enzymes. Biol Direct 2014;9:10. · DOI
- 10.1038/nbt.3988 · DOI · Paper page
- 2019, DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks, Sci Rep, 9, 7344, 10.1038/s41598-019-43708-3 · DOI
- 10.1128/JB.187.21.7292-7308.2005 · DOI
- Yamada T , Waller AS , Raes J et al. Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours. Mol Syst Biol 2012;8:581. · DOI
- 10.1126/sci-ence.adf2465 · DOI
- 2023, Enzyme annotation for orphan reactions and its applications in biomanufacturing, Green Chem Eng, 4, 137, 10.1016/j.gce.2022.10.003 · DOI
Metadata
- Authors
- Hirota, K., Salim, F., & Yamada, T.
- Affiliations
- Institute of Science Tokyo (JP)
- Corresponding author
- Yamada, Takuji
- Venue
- Bioinformatics
- Publisher
- openRxiv
- Published
- 2024-05-10
- Volume
- 41
- Issue
- 3
- ISSN
- 1367-4803 (ISSN-L)
- PMC
- PMC11881691
- OpenAlex
- W4407236378
- Cited by
- 6
- References
- 1
- Citations by year
- 2026: 4 · 2025: 2
- FWCI
- 1.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (16H06279, 22H04925, KAKENHI JP16H06279)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Immunology and Microbiology › Applied Microbiology and Biotechnology › Microbial Natural Products and Biosynthesis
- Keywords
- orphan enzymes, enzyme screening, biosynthetic gene clusters, metagenome-assembled genomes, functional annotation, gut microbiota
- MeSH terms
- Deep Learning, Enzymes, Multigene Family, Humans, Software, Computational Biology, Metagenome, Molecular Sequence Annotation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
