An Integrative Computational Framework Based on a Two-Step Random Forest Algorithm Improves Prediction of Zinc-Binding Sites in Proteins

Zinc-binding proteins are the most abundant metalloproteins in the Protein Data Bank where the zinc ions usually have catalytic, regulatory or structural roles critical for the function of the protein. Accurate prediction of zinc-binding sites is not only useful for the inference of protein function...

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Bibliographic Details
Published in:	PloS one Vol. 7; no. 11; p. e49716
Main Authors:	Zheng, Cheng, Wang, Mingjun, Takemoto, Kazuhiro, Akutsu, Tatsuya, Zhang, Ziding, Song, Jiangning
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 14.11.2012 Public Library of Science (PLoS)
Subjects:	Algorithms Amino acids Amino Acids - chemistry Amino Acids - metabolism Apoproteins - chemistry Apoproteins - metabolism Artificial intelligence Banks (Finance) Binding proteins Binding Sites Bioinformatics Biology Biotechnology Catalysis Computational Biology - methods Computer applications Enzymes Forests Graph theory Identification methods Information retrieval International conferences Internet Laboratories Mathematics Metalloproteins Metalloproteins - chemistry Metalloproteins - metabolism Methods Models, Biological Neural networks Protein Binding Proteins Recall Reproducibility of Results Residues ROC Curve Solvents Zinc Zinc - chemistry Zinc - metabolism
ISSN:	1932-6203, 1932-6203
Online Access:	Get full text
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