ArticleBioinformatics (Oxford, England)2017
SANDPUMA: ensemble predictions of nonribosomal peptide chemistry reveal biosynthetic diversity across Actinobacteria.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed.
- Predicting Functional Diversity Within Enzyme Families: A Practical Perspective.Chembiochem : a European journal of chemical biology · 2026Review
- When context matters: community and environmental context to elicit natural products.Essays in biochemistry · 2026Review
- DeepAden: an explainable machine learning method for predicting the substrate specificity of nonribosomal peptide synthetases.Nucleic acids research · 2026Article
- PARAS: High-Accuracy Machine Learning of Substrate Specificities in Nonribosomal Peptide Synthetases.JACS Au · 2026Article
- An integrative chemical and genomic similarity approach linking fungal secondary metabolites and biosynthetic gene clusters.Organic & biomolecular chemistry · 2026Article
- Advances in natural product discovery: strategies, technologies, and insights.Natural products and bioprospecting · 2026Review
- Benchmarking methods for measuring biosynthetic gene cluster similarity and determination of gene cluster families.Bioinformatics (Oxford, England) · 2025Article
- Genome Mining Leads to the Discovery of Kasichelins A-D, Unusual β-Alanine- and β-Aminoisobutyric Acid-Containing Siderophores fromJournal of natural products · 2025Article
- Sequence modeling tools to decode the biosynthetic diversity of the human microbiome.mSystems · 2025Review
- Comparative Analysis of Signature Sequences from Adenylation Domains Situated within Bacterial-Origin Nonribosomal Peptide Synthetase Modules.Journal of microbiology and biotechnology · 2025Article
- Targeted genome mining with GATOR-GC maps the evolutionary landscape of biosynthetic diversity.Nucleic acids research · 2025Article
- Targeted genome mining with GATOR-GC maps the evolutionary landscape of biosynthetic diversity.bioRxiv : the preprint server for biology · 2025Article
- Synthetic-bioinformatic natural product-inspired peptides.Natural product reports · 2025Review
- Interpretable adenylation domain specificity prediction using protein language models.bioRxiv : the preprint server for biology · 2025Article
- RAIChU: automating the visualisation of natural product biosynthesis.Journal of cheminformatics · 2024Article
- Structural, biochemical and bioinformatic analyses of nonribosomal peptide synthetase adenylation domains.Natural product reports · 2024Review
- Discovery and Derivatization of Tridecaptin Antibiotics with Altered Host Specificity and Enhanced Bioactivity.ACS chemical biology · 2024Article
- A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.PLoS computational biology · 2024Article
- Analysis of Microbial Diversity and Community Structure of Rhizosphere Soil of ThreeMicroorganisms · 2024Article
- Chemical structure metagenomics of microbial natural products: surveying nonribosomal peptides and beyond.Beilstein journal of organic chemistry · 2024Article
4 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
summaryNonribosomally synthesized peptides (NRPs) are natural products with widespread applications in medicine and biotechnology. Many algorithms have been developed to predict the substrate specificities of nonribosomal peptide synthetase adenylation (A) domains from DNA sequences, which enables prioritization and dereplication, and integration with other data types in discovery efforts. However, insufficient training data and a lack of clarity regarding prediction quality have impeded optimal use. Here, we introduce prediCAT, a new phylogenetics-inspired algorithm, which quantitatively estimates the degree of predictability of each A-domain. We then systematically benchmarked all algorithms on a newly gathered, independent test set of 434 A-domain sequences, showing that active-site-motif-based algorithms outperform whole-domain-based methods. Subsequently, we developed SANDPUMA, a powerful ensemble algorithm, based on newly trained versions of all high-performing algorithms, which significantly outperforms individual methods. Finally, we deployed SANDPUMA in a systematic investigation of 7635 Actinobacteria genomes, suggesting that NRP chemical diversity is much higher than previously estimated. SANDPUMA has been integrated into the widely used antiSMASH biosynthetic gene cluster analysis pipeline and is also available as an open-source, standalone tool. AVAILABILITY AND IMPLEMENTATION: SANDPUMA is freely available at https://bitbucket.org/chevrm/sandpuma and as a docker image at https://hub.docker.com/r/chevrm/sandpuma/ under the GNU Public License 3 (GPL3). CONTACT: chevrette@wisc.edu or marnix.medema@wur.nl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.