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. 2025 Jan-Dec;19(1):e70001.
doi: 10.1049/syb2.70001.

The optimised model of predicting protein-metal ion ligand binding residues

Caiyun Yang  1 , Xiuzhen Hu  1 , Zhenxing Feng  1 , Sixi Hao  1 , Gaimei Zhang  2 , Shaohua Chen  1 , Guodong Guo  3
Affiliations

The optimised model of predicting protein-metal ion ligand binding residues

Caiyun Yang et al. IET Syst Biol. 2025 Jan-Dec.

Abstract

Metal ions are significant ligands that bind to proteins and play crucial roles in cell metabolism, material transport, and signal transduction. Predicting the protein-metal ion ligand binding residues (PMILBRs) accurately is a challenging task in theoretical calculations. In this study, the authors employed fused amino acids and their derived information as feature parameters to predict PMILBRs using three classical machine learning algorithms, yielding favourable prediction results. Subsequently, deep learning algorithm was incorporated in the prediction, resulting in improved results for the sets of Ca2+ and Mg2+ compared to previous studies. The validation matrix provided the optimal prediction model for each ionic ligand binding residue, exhibiting the capability of effectively predicting the binding sites of metal ion ligands for real protein chains.

Keywords: biocomputers; bioinformatics.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Energy classification of the K+ ligand. The abscissa is 20 amino acids, and the ordinate is the difference of energy probability.
FIGURE 2
FIGURE 2
Positive and negative set standard scoring matrices.
FIGURE 3
FIGURE 3
Flow chart for the prediction of PMILBRs. PS, 2L, H, F, V represent component information, site conservation information, information entropy, propensity factors and factor values. aa, ss, sa, φ, ψ, wx, le, dh, qs, 10 factors, respectively, represent amino acids, secondary structure, relative solvent accessibility, dihedral angle, disorder, energy, charge, hydrophilicity and 10 orthogonal factors; RF, SVM, KNN and DNN represent RF algorithm, SVM algorithm, KNN algorithm and DNN. DNN, deep neural network; KNN, k‐nearest neighbour; PMILBRs, protein‐metal ion ligand binding residues; RF, random forest; SVM, support vector machine.
FIGURE 4
FIGURE 4
5‐Fold cross‐validation results of different algorithms for Mg2+ and Fe2+.
FIGURE 5
FIGURE 5
Independent test results of different algorithms for Mg2+ and Fe2+.
See this image and copyright information in PMC

References

    1. Akam, E.A. , et al.: Disulfide‐masked iron prochelators: effects on cell death, proliferation, and hemoglobin production. J. Inorg. Biochem. 180, 186–193 (2018). 10.1016/j.jinorgbio.2017.12.016 - DOI - PMC - PubMed
    1. Brailoiu, E. , et al.: Mechanisms of modulation of brain microvascular endothelial cells function by thrombin. Brain Res. 1657, 167–175 (2016). 10.1016/j.brainres.2016.12.011 - DOI - PMC - PubMed
    1. Reif, D.W. : Ferritin as a source of iron for oxidative damage. Free Radical Biol. Med. 12(5), 417–427 (1992). 10.1016/0891-5849(92)90091-t - DOI - PubMed
    1. Reed, G.H. , Poyner, R.R. : Mn2+ as a probe of divalent metal ion binding and function in enzymes and other proteins. Met. Ions Biol. Syst. 37(12), 183–207 (2000) - PubMed
    1. Jiang, Z. , et al.: Identification of Ca(2+)‐binding residues of a protein from its primary sequence. Genet. Mol. Res. 15(2) (2016). 10.4238/gmr.15027618 - DOI - PubMed

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