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

dc.citation.epageen_US
dc.citation.issue6en_US
dc.citation.volume7en_US
dc.citation.woscount3
dc.contributor.authorLu, Chih-Haoen_US
dc.contributor.authorLin, Yu-Fengen_US
dc.contributor.authorLin, Jau-Jien_US
dc.contributor.authorYu, Chin-Shengen_US
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.date.accessioned2014-12-08T15:23:34Z
dc.date.available2014-12-08T15:23:34Z
dc.date.issued2012-06-18en_US
dc.description.abstractThe 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 Ca2+, Cu2+, Fe3+, Mg2+, Mn2+, and Zn2+-were constructed using the residues within 3.5 angstrom 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.en_US
dc.identifier.doie39252en_US
dc.identifier.issn1932-6203en_US
dc.identifier.journalPLOS ONEen_US
dc.identifier.urihttp://dx.doi.org/e39252en_US
dc.identifier.urihttps://ir.lib.nycu.edu.tw/handle/11536/16476
dc.identifier.wosnumberWOS:000305583300122
dc.language.isoen_USen_US
dc.titlePrediction of Metal Ion-Binding Sites in Proteins Using the Fragment Transformation Methoden_US
dc.typeArticleen_US

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