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

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.

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

Positive and negative set standard scoring matrices.

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

5‐Fold cross‐validation results of different algorithms for Mg²⁺ and Fe²⁺.

FIGURE 5

Independent test results of different algorithms for Mg²⁺ and Fe²⁺.