ArticleBMC genomics2025
Predicting substrates for orphan solute carrier proteins using multi-omics datasets.
Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Unveiling Specificity, Redundancy, and Promiscuity of FiveInternational journal of molecular sciences · 2026Article
- The regulatory roles and therapeutic strategies of the solute carrier transporters in cancer metabolism.NPJ precision oncology · 2026Review
- Molecular Modeling and Gene Ontology Implicate SLC35F4 and SLC35F5 as Golgi-Associated Importers of Flavin-Adenine-Dinucleotide.International journal of molecular sciences · 2026Article
- Article
- Data- and knowledge-derived functional landscape of human solute carriers.Molecular systems biology · 2025Article
- Identification of SLC45A4 as a pain gene encoding a neuronal polyamine transporter.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Solute carriers (SLC) are integral membrane proteins responsible for transporting a wide variety of metabolites, signaling molecules and drugs across cellular membranes. Despite key roles in metabolism, signaling and pharmacology, around one third of SLC proteins are 'orphans' whose substrates are unknown. Experimental determination of SLC substrates is technically challenging, given the wide range of possible physiological candidates. Here, we develop a predictive algorithm to identify correlations between SLC expression levels and intracellular metabolite concentrations by leveraging existing cancer multi-omics datasets. Our predictions recovered known SLC-substrate pairs with high sensitivity and specificity compared to simulated random pairs. CRISPR-Cas9 dependency screen data and metabolic pathway adjacency data further improved the performance of our algorithm. In parallel, we combined drug sensitivity data with SLC expression profiles to predict new SLC-drug interactions. Together, we provide a novel bioinformatic pipeline to predict new substrate predictions for SLCs, offering new opportunities to de-orphanise SLCs with important implications for understanding their roles in health and disease.
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Registered trials
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 Socratic graph.