TY  - JOUR
A1  - Anteghini, Marco
A1  - Martins dos Santos, Vitor AP
A1  - Saccenti, Edoardo
T1  - PortPred: Exploiting deep learning embeddings of amino acid sequences for the identification of transporter proteins and their substrates
T2  - Journal of Cellular Biochemistry
N2  - 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.
Y1  - 2023
UR  - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/9251
VL  - 124
IS  - 11
SP  - 1665
EP  - 1885
ER  -