Prediction of Protein Ion-Ligand Binding Sites with ELECTRA.
ELECTRA
deep learning
ion-binding site prediction
natural language processing
sequence-based prediction
transformer
Journal
Molecules (Basel, Switzerland)
ISSN: 1420-3049
Titre abrégé: Molecules
Pays: Switzerland
ID NLM: 100964009
Informations de publication
Date de publication:
25 Sep 2023
25 Sep 2023
Historique:
received:
25
08
2023
revised:
15
09
2023
accepted:
19
09
2023
medline:
23
10
2023
pubmed:
14
10
2023
entrez:
14
10
2023
Statut:
epublish
Résumé
Interactions between proteins and ions are essential for various biological functions like structural stability, metabolism, and signal transport. Given that more than half of all proteins bind to ions, it is becoming crucial to identify ion-binding sites. The accurate identification of protein-ion binding sites helps us to understand proteins' biological functions and plays a significant role in drug discovery. While several computational approaches have been proposed, this remains a challenging problem due to the small size and high versatility of metals and acid radicals. In this study, we propose IonPred, a sequence-based approach that employs ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) to predict ion-binding sites using only raw protein sequences. We successfully fine-tuned our pretrained model to predict the binding sites for nine metal ions (Zn
Identifiants
pubmed: 37836636
pii: molecules28196793
doi: 10.3390/molecules28196793
pmc: PMC10574437
pii:
doi:
Substances chimiques
Ligands
0
Proteins
0
Metals
0
Ions
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NIGMS NIH HHS
ID : R35 GM126985
Pays : United States
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