Predicting the subcellular location of prokaryotic proteins with DeepLocPro

Jaime Moreno, Henrik Nielsen, Ole Winther, Felix Teufel*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

2 Citations (Scopus)
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Abstract

Motivation: Protein 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. Results: DeepLocPro 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/.

Original languageEnglish
Article numberbtae677
JournalBioinformatics
Volume40
Issue number12
Number of pages5
ISSN1367-4803
DOIs
Publication statusPublished - 2024

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© The Author(s) 2024.

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