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 Sn 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.
Highlights
The realization of protein functions requires interaction with ligands; in particular, metalloproteins formed by the combination of proteins and metal ion ligands play a vital role in biological functions (Barondeau and Getzoff, 2004)
Since the surrounding residues have an influence on the binding of metal ion ligands, we considered the binding residues and surrounding residues in the datasets
The Sn and Matthew’s correlation coefficient (MCC) values of alkali–metal ion ligands were higher than 7.28% and 0.253, respectively
Summary
The realization of protein functions requires interaction with ligands; in particular, metalloproteins formed by the combination of proteins and metal ion ligands play a vital role in biological functions (Barondeau and Getzoff, 2004). The mechanism of protein–metal ion ligand binding is that some special protein functions need the precise binding of proteins and ligand-binding residues, while the abnormal binding would lead to many related diseases. Abnormal binding residues of Cu2+ ligand can lead to the diseases of Wilson and Menkes (Yuan et al, 1995; Petris et al, 1996). The study of protein–metal ion ligand-binding residues is helpful to understand the mechanism of protein functions, the treatment of diseases, and the design of molecular drugs
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