Article
Version 1
Preserved in Portico This version is not peer-reviewed
Prediction of Protein Ion-Ligand Binding Sites with ELECTRA
Version 1
: Received: 28 August 2023 / Approved: 28 August 2023 / Online: 29 August 2023 (02:50:38 CEST)
A peer-reviewed article of this Preprint also exists.
Essien, C.; Jiang, L.; Wang, D.; Xu, D. Prediction of Protein Ion–Ligand Binding Sites with ELECTRA. Molecules 2023, 28, 6793. Essien, C.; Jiang, L.; Wang, D.; Xu, D. Prediction of Protein Ion–Ligand Binding Sites with ELECTRA. Molecules 2023, 28, 6793.
Abstract
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 becomes crucial to identify ion-binding sites. 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 (Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, and K+) and four acid radical ion ligands (CO32−, SO42−, PO43−, NO2−). IonPred surpassed six current state-of-the-art tools by over 44.65% and 28.46% respectively in F1 score and MCC when compared on an independent test dataset. Our method is more computationally efficient than existing tools producing prediction results for a hundred sequences for a specific ion in under ten minutes.
Keywords
Deep Learning, ELECTRA; Ion binding site prediction; Transformer; Natural Language Processing; Sequence-based prediction
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
