ArticleBioinformatics (Oxford, England)2024
Predicting the subcellular location of prokaryotic proteins with DeepLocPro.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed.
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- A multi-label cascade flexible neural forest model for predicting the subcellular location of multi-site bacterial proteins.Scientific reports · 2026Article
- Screening of Marine Bacteria for Lipase Activity and Application as Whole-Cell Biocatalysts.Microorganisms · 2026Article
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- PLM-effector: unleashing the potential of protein language models for bacterial secreted protein prediction.Briefings in bioinformatics · 2026Article
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- Insights into the proteome of next-generation probiotic Faecalibacterium duncaniae A2-165 through label-free proteomics approach.BMC microbiology · 2026Article
- Biosynthesis and Export of Membrane-Enveloped Selenium Nanoparticles byEnvironmental science & technology · 2026Article
- Bacterial extracellular vesicles exhibit distinct functional potential across biogeographic provinces of the South Pacific Ocean.The ISME journal · 2026Article
- Fructan utilization by members of marine Gammaproteobacteria involves SusC/D-like proteins.The ISME journal · 2026Article
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Authors and funding
4 authors.
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Abstract
motivationProtein subcellular location prediction is a widely explored task in bioinformatics because of its importance in proteomics research. We propose DeepLocPro, an extension to the popular method DeepLoc, tailored specifically to archaeal and bacterial organisms.
resultsDeepLocPro is a multiclass subcellular location prediction tool for prokaryotic proteins, trained on experimentally verified data curated from UniProt and PSORTdb. DeepLocPro compares favorably to the PSORTb 3.0 ensemble method, surpassing its performance across multiple metrics in our benchmark experiment. AVAILABILITY AND IMPLEMENTATION: The DeepLocPro prediction tool is available online at https://ku.biolib.com/deeplocpro and https://services.healthtech.dtu.dk/services/DeepLocPro-1.0/.
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