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PortPred: Exploiting deep learning embeddings of amino acid sequences for the identification of transporter proteins and their substrates

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  • The physiology of every living cell is regulated at some level by transporter proteins which constitute a relevant portion of membrane-bound proteins and are involved in the movement of ions, small and macromolecules across bio-membranes. The importance of transporter proteins is unquestionable. The prediction and study of previously unknown transporters can lead to the discovery of new biological pathways, drugs and treatments. Here we present PortPred, a tool to accurately identify transporter proteins and their substrate starting from the protein amino acid sequence. PortPred successfully combines pre-trained deep learning-based protein embeddings and machine learning classification approaches and outperforms other state-of-the-art methods. In addition, we present a comparison of the most promising protein sequence embeddings (Unirep, SeqVec, ProteinBERT, ESM-1b) and their performances for this specific task.

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Metadaten
Verfasserangaben:Marco AnteghiniORCiD, Vitor AP Martins dos Santos, Edoardo Saccenti
Dokumentart:Artikel
Titel des übergeordneten Werkes (Englisch):Journal of Cellular Biochemistry
Band:124
Ausgabe / Heft:11
Erste Seite:1665
Letzte Seite:1885
Jahr der Erstveröffentlichung:2023
DOI:https://doi.org/10.1002/jcb.30490
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