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

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...

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Bibliographic Details
Published in:Journal of cellular biochemistry 2023-11, Vol.124 (11), p.1803-1824
Main Authors: Anteghini, Marco, Santos, Vitor Ap Martins Dos, Saccenti, Edoardo
Format: Article
Language:English
Subjects:
Amino Acid Sequence
Amino acids
Computational Biology - methods
Deep Learning
Machine Learning
Macromolecules
Membrane Proteins - metabolism
Membrane Transport Proteins - metabolism
Membranes
Protein transport
Proteins
Substrates
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Summary: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.
ISSN:0730-2312
1097-4644
DOI:10.1002/jcb.30490