The Fold-X force field algorithm was used by Joost et al. One type of method directly predicts the metal ion binding sites using 3D structural information, and high accuracy can be achieved. The methods of identifying metal ion-binding residues are generally divided into two types. In addition, the role of metal ions in dSPNs (disease-related single nucleotide polymorphisms) is directly related to human disease, and the identification of metal ion-binding residues is of great significance for the development of molecular drugs to treat human diseases.ĭuring the last few years, many approaches have been developed to predict the binding sites of protein-metal ions. The molecular mechanism involves the metal ions binding with specific residues within proteins. The realization of biological function depends on the interaction between the ligand-binding residues and metal ions. The metal ions play a crucial role in protein structure and function, for example the transportation of iron ions in hemoglobin, the stabilization of zinc ions in the zinc finger domain, and the regulation of calcium ions in calmodulin. Īpproximately one-third of all known proteins bind with metal ions. An online server was constructed based on the framework of the proposed method and is freely available at. In addition, we found that Ca 2+ was insensitive to hydrophobicity and hydrophilicity information and Mn 2+ was insensitive to polarization charge information. The binding sites of other metals can also be accurately identified using the Support Vector Machine algorithm with multifeature parameters as input. The analysis showed that Zn 2+, Cu 2+, Fe 2+, Fe 3+, and Co 2+ were sensitive to the conservation of amino acids at binding sites, and promising results can be achieved using the Position Weight Scoring Matrix algorithm, with an accuracy of over 79.9% and a Matthews correlation coefficient of over 0.6. Ten metal ions were extracted from the BioLip database: Zn 2+, Cu 2+, Fe 2+, Fe 3+, Ca 2+, Mg 2+, Mn 2+, Na +, K + and Co 2+. This study presents an effective method of analyzing and identifying the binding residues of metal ions based solely on sequence information. The identification of metal ion binding sites is important for protein function annotation and the design of new drug molecules.