ArticleBioinformatics (Oxford, England)2021
PolarProtPred: predicting apical and basolateral localization of transmembrane proteins using putative short linear motifs and deep learning.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- SLiMs prediction method based on enhanced attention mechanism and feature fusion.Bioinformatics advances · 2026Article
- Optimized Biotinylated Peptide Detection Method for Characterizing the Cell Surface Proteome.Methods in molecular biology (Clifton, N.J.) · 2025Article
- An analytical study on the identification of N-linked glycosylation sites using machine learning model.PeerJ. Computer science · 2022Article
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Authors and funding
3 authors.
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Abstract
motivationCell polarity refers to the asymmetric organization of cellular components in various cells. Epithelial cells are the best-known examples of polarized cells, featuring apical and basolateral membrane domains. Mounting evidence suggests that short linear motifs play a major role in protein trafficking to these domains, although the exact rules governing them are still elusive.
resultsIn this study we prepared neural networks that capture recurrent patterns to classify transmembrane proteins localizing into apical and basolateral membranes. Asymmetric expression of drug transporters results in vectorial drug transport, governing the pharmacokinetics of numerous substances, yet the data on how proteins are sorted in epithelial cells is very scattered. The provided method may offer help to experimentalists to identify or better characterize molecular networks regulating the distribution of transporters or surface receptors (including viral entry receptors like that of COVID-19). AVAILABILITY AND IMPLEMENTATION: The prediction server PolarProtPred is available at http://polarprotpred.ttk.hu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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