Prediction of Metal Ion–Binding Sites in Proteins Using the Fragment Transformation Method

Graduate Institute of Molecular Systems Biomedicine, China Medical University, Taichung, Taiwan.
PLoS ONE (Impact Factor: 3.23). 06/2012; 7(6):e39252. DOI: 10.1371/journal.pone.0039252
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The structure of a protein determines its function and its interactions with other factors. Regions of proteins that interact with ligands, substrates, and/or other proteins, tend to be conserved both in sequence and structure, and the residues involved are usually in close spatial proximity. More than 70,000 protein structures are currently found in the Protein Data Bank, and approximately one-third contain metal ions essential for function. Identifying and characterizing metal ion-binding sites experimentally is time-consuming and costly. Many computational methods have been developed to identify metal ion-binding sites, and most use only sequence information. For the work reported herein, we developed a method that uses sequence and structural information to predict the residues in metal ion-binding sites. Six types of metal ion-binding templates- those involving Ca(2+), Cu(2+), Fe(3+), Mg(2+), Mn(2+), and Zn(2+)-were constructed using the residues within 3.5 Å of the center of the metal ion. Using the fragment transformation method, we then compared known metal ion-binding sites with the templates to assess the accuracy of our method. Our method achieved an overall 94.6 % accuracy with a true positive rate of 60.5 % at a 5 % false positive rate and therefore constitutes a significant improvement in metal-binding site prediction.

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Available from: Jau-Ji Lin, Oct 12, 2015
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    • "The predicted 3D chemical ligands for Smp_076400 and Sjp_0058490 (Q86DW2) included three metal ions Ca2+, Mg2+, and Zn2+ (Figure 7). Metal ions are involved in many diverse biochemical reactions,64 including cellular cofactors for phosphorylation. The UspA protein of Escherichia coli undergoes phosphorylation in vitro with its phosphate donors ATP and/or GTP, in the absence of other proteins.65 "
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    Advances and Applications in Bioinformatics and Chemistry 05/2013; 6:15-27. DOI:10.2147/AABC.S37191
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    • "Structure-based predictions Sodhi et al. [18] MetSite 15313626 NN Schymkowitz et al. [19] 16006526 Fold-X Babor et al. [20] CHED 17657805 SVM Goyal and Mande [23] 17847089 Template-based Ebert and Altman [22] FEATURE 18042678 Bayesian Bordner [21] 18940825 Random forest Levy et al. [24] SeqCHED 19173310 SVM, ML Wang et al. [25] Rosetta 20054832 Rosetta Zhao et al. [26] TEMSP 21414989 Template-based Lu et al. [27] 22723976 FTM Zheng et al. [28] Zincidentifier 23166753 Random forest addition, the protein structures in this study were visualized in Pymol ( [34] "
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