Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors

Shuang Xu; Xiuzhen Hu; Zhenxing Feng; Jing Pang; Kai Sun; Xiaoxiao You; Ziyang Wang

doi:10.3389/fgene.2021.793800

Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors

Front Genet. 2022 Jan 4:12:793800. doi: 10.3389/fgene.2021.793800. eCollection 2021.

Authors

Shuang Xu^{1

2}, Xiuzhen Hu^{1

2}, Zhenxing Feng^{1

2}, Jing Pang^{1

2}, Kai Sun^{1

2}, Xiaoxiao You^{1

2}, Ziyang Wang^{1

2}

Affiliations

¹ College of Sciences, Inner Mongolia University of Technology, Hohhot, China.
² Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China.

Abstract

The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function. Currently, it is a challenging work to identify the metal ion ligand-binding residues accurately by computational approaches. In this study, we proposed an improved method to predict the binding residues of 10 metal ion ligands (Zn²⁺, Cu²⁺, Fe²⁺, Fe³⁺, Co²⁺, Mn²⁺, Ca²⁺, Mg²⁺, Na⁺, and K⁺). Based on the basic feature parameters of amino acids, and physicochemical and predicted structural information, we added another two features of amino acid correlation information and binding residue propensity factors. With the optimized parameters, we used the GBM algorithm to predict metal ion ligand-binding residues. In the obtained results, the Sn and MCC values were over 10.17% and 0.297, respectively. Besides, the S_n and MCC values of transition metals were higher than 34.46% and 0.564, respectively. In order to test the validity of our model, another method (Random Forest) was also used in comparison. The better results of this work indicated that the proposed method would be a valuable tool to predict metal ion ligand-binding residues.

Keywords: GBM algorithm; binding residues; correlation features; metal ion ligand; propensity factors.