The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Prediction of Protein Ion–Ligand Binding Sites with ELECTRA
Open accessPublished version of a preprint
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 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 (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 the 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Flagged first, then by citations
Research works printed but not linked: 36
- Alberts, 2003, Molecular biology of the cell, Scand. J. Rheumatol., 32, 125
- Gao, 2012, The distribution of ligand-binding pockets around protein-protein interfaces suggests a general mechanism for pocket formation, Proc. Natl. Acad. Sci. USA, 109, 3784, 10.1073/pnas.1117768109 · DOI
- Gao, M., and Skolnick, J. (2013). A comprehensive survey of small-molecule binding pockets in proteins. PLoS Comput. Biol., 9. · DOI
- Tainer, 1991, Metal-binding sites in proteins, Curr. Opin. Biotechnol., 2, 582, 10.1016/0958-1669(91)90084-I · DOI
- Thomson, 1998, Bio-inorganic chemistry, Curr. Opin. Chem. Biol., 2, 155, 10.1016/S1367-5931(98)80056-2 · DOI
- Hsia, 1998, Respiratory function of hemoglobin, N. Engl. J. Med., 338, 239, 10.1056/NEJM199801223380407 · DOI
- Fracchia, 2013, Modulation of t cell metabolism and function through calcium signaling, Front. Immunol., 4, 324, 10.3389/fimmu.2013.00324 · DOI
- Baba, Y., and Kurosaki, T. (2015). B Cell Receptor Signaling, Springer. · DOI
- McCall, 2000, Function and mechanism of zinc metalloenzymes, J. Nutr., 130, 1437S, 10.1093/jn/130.5.1437S · DOI
- Levenson, 2012, Zinc in the central nervous system: From molecules to behavior, BioFactors, 38, 186, 10.1002/biof.1012 · DOI
- Wang, 2015, Phosphorylation is an on/off switch for 5-hydroxyconiferaldehyde o-methyl-transferase activity in poplar monolignol biosynthesis, Proc. Natl. Acad. Sci. USA, 112, 8481, 10.1073/pnas.1510473112 · DOI
- Zhang, B., and Chi, L. (2021). Chondroitin sulfate/dermatan sulfate-protein interactions and their biological functions in human diseases: Implications and analytical tools. Front. Cell Dev. Biol., 9. · DOI
- Sletten, E. (1997). Cytotoxic, Mutagenic and Carcinogenic Potential of Heavy Metals Related to Human Environment, Springer.
- Yonezawa, 2004, DNA display of biologically active proteins for in vitro protein selection, J. Biochem., 135, 285, 10.1093/jb/mvh034 · DOI
- Chen, P., Huang, J.Z., and Gao, X. (2014). Ligandrfs: Random forest ensemble to identify ligand-binding residues from sequence information alone. BMC Bioinform., 15. · DOI
- Chen, 2015, A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction, IEEE/ACM Trans. Comput. Biol. Bioinform., 13, 901, 10.1109/TCBB.2015.2505286 · DOI
- Roy, 2012, Cofactor: An accurate comparative algorithm for structure-based protein function annotation, Nucleic Acids Res., 40, W471, 10.1093/nar/gks372 · DOI
- Yang, 2013, Protein–ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment, Bioinformatics, 29, 2588, 10.1093/bioinformatics/btt447 · DOI
- Hu, 2016, Recognizing metal and acid radical ion-binding sites by integrating ab initio modeling with templatebased transferals, Bioinformatics, 32, 3260, 10.1093/bioinformatics/btw396 · DOI
- Sobolev, 2013, Web tools for predicting metal binding sites in proteins, Isr. J. Chem., 53, 166, 10.1002/ijch.201200084 · DOI
- Lu, C.H., Lin, Y.F., Lin, J.J., and Yu, C.S. (2012). Prediction of metal ion–binding sites in proteins using the fragment transformation method. PLoS ONE, 7. · DOI
- Hu, X., Wang, K., and Dong, Q. (2016). Protein ligand-specific binding residue predictions by an ensemble classifier. BMC Bioinform., 17. · DOI
- Yang, 2012, Biolip: A semi-manually curated database for biologically relevant ligand–protein interactions, Nucleic Acids Res., 41, D1096, 10.1093/nar/gks966 · DOI
- Cao, X., Hu, X., Zhang, X., Gao, S., Ding, C., Feng, Y., and Bao, W. (2017). Identification of metal ion binding sites based on amino acid sequences. PLoS ONE, 12. · DOI
- Greenside, P., Hillenmeyer, M., and Kundaje, A. (2018). Pacific Symposium on Biocomputing 2018: Proceedings of the Pacific Symposium, World Scientific.
- Clark, K., Luong, M.T., Le, Q.V., and Manning, C.D. (2020). ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. arXiv.
- Yu, 2013, Designing template-free predictor for targeting protein-ligand binding sites with classifier ensemble and spatial clustering, IEEE/ACM Trans. Comput. Biol. Bioinform., 10, 994, 10.1109/TCBB.2013.104 · DOI
- Essien, C., Wang, D., and Xu, D. (2019, January 18–21). Capsule network for predicting zinc binding sites in metalloproteins. Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA. · DOI
- Yuan, Q., Chen, S., and Wang, W. (2019, January 18–21). Prediction of ligand binding residues in protein sequences using machine learning. Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA.
- Lin, 2016, Mib: Metal ion-binding site prediction and docking server, J. Chem. Inf. Model., 56, 2287, 10.1021/acs.jcim.6b00407 · DOI
- Xia, 2020, Protein–ligand binding residue prediction enhancement through hybrid deep heterogeneous learning of sequence and structure data, Bioinformatics, 36, 3018, 10.1093/bioinformatics/btaa110 · DOI
- Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Costa, A.d.S., Fazel-Zarandi, M., and Sercu, T. (2022). Language models of protein sequences at the scale of evolution enable accurate structure prediction. bioRxiv.
- Berman, 2000, The protein data bank, Nucleic Acids Res., 28, 235, 10.1093/nar/28.1.235 · DOI · Paper page
- Cock, 2009, Biopython: Freely available python tools for computational molecular biology and bioinformatics, Bioinformatics, 25, 1422, 10.1093/bioinformatics/btp163 · DOI · Paper page
- Segura, 2020, Rcsb protein data bank 1d tools and services, Bioinformatics, 36, 5526, 10.1093/bioinformatics/btaa1012 · DOI
- Li, 2001, Clustering of highly homologous sequences to reduce the size of large protein databases, Bioinformatics, 17, 282, 10.1093/bioinformatics/17.3.282 · DOI
Metadata
- Authors
- Essien, C., Jiang, L., Wang, D., & Xu, D.
- Affiliations
- University of Missouri (US)
- Corresponding author
- Xu, Dong
- Venue
- Molecules
- Publisher
- MDPI AG
- Published
- 2023-09-25
- Volume
- 28
- Issue
- 19
- Pages
- 6793
- ISSN
- 1420-3049 (ISSN-L)
- PMC
- PMC10574437
- OpenAlex
- W4387056124
- Cited by
- 5
- References
- 1
- Citations by year
- 2026: 2 · 2025: 3
- FWCI
- 0.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecules (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- US National Institutes of Health (R35-GM126985)
- Research Computing Support Services at the University of Missouri (R35-GM126985)
- the Pacific Northwest National Laboratory (PNNL) (R35-GM126985)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- metal ion binding, Electra, sequence-based prediction, metal-protein interactions, NO2, drug discovery
- MeSH terms
- Binding Sites, Ions, Ligands, Metals, Protein Binding, Proteins
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
