| Download | - View final version: A hybrid machine learning framework for functional annotation of mitochondrial glutathione transport and metabolism proteins in cancers (PDF, 2.0 MiB)
- View supplementary information: A hybrid machine learning framework for functional annotation of mitochondrial glutathione transport and metabolism proteins in cancers (ZIP, 28.0 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1186/s12859-025-06051-1 |
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| Author | Search for: Kennedy, Luke; Search for: Sandhu, Jagdeep K.1ORCID identifier: https://orcid.org/0000-0002-0065-1335; Search for: Harper, Mary-Ellen; Search for: Cuperlovic-Culf, Miroslava2ORCID identifier: https://orcid.org/0000-0002-9483-8159 |
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| Affiliation | - National Research Council Canada. Human Health Therapeutics
- National Research Council Canada. Digital Technologies
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| Funder | Search for: National Research Council Canada; Search for: Canadian Institutes of Health Research; Search for: Natural Sciences and Engineering Research Council of Canada |
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| Format | Text, Article |
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| Subject | mitochondria; glutathione; machine learning; cancer; protein function annotation; transmembrane transport; SLC25; multi-omics; knowledge-based; gene ontology |
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| Abstract | Background: Alterations of metabolism, including changes in mitochondrial metabolism as well as glutathione (GSH) metabolism are a well appreciated hallmark of many cancers. Mitochondrial GSH (mGSH) transport is a poorly characterized aspect of GSH metabolism, which we investigate in the context of cancer. Existing functional annotation approaches from machine (ML) or deep learning (DL) models based only on protein sequences, were unable to annotate functions in biological contexts. Results: We develop a flexible ML framework for functional annotation from diverse feature data. This hybrid ML framework leverages cancer cell line multi-omics data and other biological knowledge data as features, to uncover potential genes involved in mGSH metabolism and membrane transport in cancers. This framework achieves strong performance across functional annotation tasks and several cell line and primary tumor cancer samples. For our application, classification models predict the known mGSH transporter SLC25A39 but not SLC25A40 as being highly probably related to mGSH metabolism in cancers. SLC25A10, SLC25A50, and orphan SLC25A24, SLC25A43 are predicted to be associated with mGSH metabolism in multiple biological contexts and structural analysis of these proteins reveal similarities in potential substrate binding regions to the binding residues of SLC25A39. Conclusion: These findings have implications for a better understanding of cancer cell metabolism and novel therapeutic targets with respect to GSH metabolism through potential novel functional annotations of genes. The hybrid ML framework proposed here can be applied to other biological function classifications or multi-omics datasets to generate hypotheses in various biological contexts. Code and a tutorial for generating models and predictions in this framework are available at: https://github.com/lkenn012/mGSH_cancerClassifiers. |
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| Publication date | 2025-02-11 |
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| Publisher | Springer Nature |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 112b2789-023e-4745-bd40-ba02b2240ab1 |
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| Record created | 2025-03-12 |
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| Record modified | 2025-11-03 |
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