Identification of Intrinsically Unstructured Proteins using hierarchical classifier

Authors:
Jack Y. Yang;Mary Qu Yang
Affiliations:
Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Harvard University, Boston, Massachusetts 02114, USA.;National Human Genome Research Institute, National Institutes of Health, US Department of Health and Human Services Bethesda, MD 20852, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2008

Citing 4
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IUP: Intrinsically Unstructured Protein predictor - A software tool for analyzing polypeptide sequences

BIBE '06 Proceedings of the Sixth IEEE Symposium on BionInformatics and BioEngineering
Identification of Intrinsically Unstructured Proteins using hierarchical classifier

International Journal of Data Mining and Bioinformatics

Identification of Intrinsically Unstructured Proteins using hierarchical classifier

International Journal of Data Mining and Bioinformatics

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Abstract

It is suggested that protein functions only when folded into a particular 3-D structure. Recently, many protein regions and some entire proteins have been identified with no definite tertiary structure, but presenting instead as dynamic, disorder ensembles under different physiochemical circumstances. These proteins and regions are known as Intrinsically Unstructured regions and Proteins (IUP). We constructed a Recursive Maximum Contrast Tree (RMCT) based classifier to identify IUP. The classifier has been benchmarked against industrial standard PONDR VLXT on out-of-sample data by external evaluators. The IUP predictor is a viable alternative software tool for identifying intrinsic unstructured regions and proteins.