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

Marco Anteghini*, Vitor Martins dos Santos, Edoardo Saccenti*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1803-1824
JournalJournal of Cellular Biochemistry
Volume124
Issue number11
DOIs
Publication statusPublished - Nov 2023

Keywords

  • membrane proteins
  • pre-trained embeddings
  • protein sequence embeddings
  • substrates prediction
  • transporter proteins

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