Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100442
Title: Improving taxonomy-based protein fold recognition by using global and local features
Authors: Yang, Jian-Yi
Chen, Xin
Keywords: Mathematical Sciences
Issue Date: 2011
Source: Yang, J. Y., & Chen, X. (2011). Improving taxonomy-based protein fold recognition by using global and local features. Proteins: Structure, Function, and Bioinformatics, 79(7), 2053-2064.
Series/Report no.: Proteins: structure, function, and bioinformatics
Abstract: Fold recognition from amino acid sequences plays an important role in identifying protein structures and functions. The taxonomy-based method, which classifies a query protein into one of the known folds, has been shown very promising for protein fold recognition. However, extracting a set of highly discriminative features from amino acid sequences remains a challenging problem. To address this problem, we developed a new taxonomy-based protein fold recognition method called TAXFOLD. It extensively exploits the sequence evolution information from PSI-BLAST profiles and the secondary structure information from PSIPRED profiles. A comprehensive set of 137 features is constructed, which allows for the depiction of both global and local characteristics of PSI-BLAST and PSIPRED profiles. We tested TAXFOLD on four datasets and compared it with several major existing taxonomic methods for fold recognition. Its recognition accuracies range from 79.6 to 90% for 27, 95, and 194 folds, achieving an average 6.9% improvement over the best available taxonomic method. Further test on the Lindahl benchmark dataset shows that TAXFOLD is comparable with the best conventional template-based threading method at the SCOP fold level. These experimental results demonstrate that the proposed set of features is highly beneficial to protein fold recognition.
URI: https://hdl.handle.net/10356/100442
http://hdl.handle.net/10220/17877
ISSN: 0887-3585
DOI: http://dx.doi.org/10.1002/prot.23025
Rights: © 2011 Wiley-Liss, Inc.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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