Title
A fast SCOP fold classification system using content-based E-Predict algorithm.
Abstract
Domain experts manually construct the Structural Classification of Protein (SCOP) database to categorize and compare protein structures. Even though using the SCOP database is believed to be more reliable than classification results from other methods, it is labor intensive. To mimic human classification processes, we develop an automatic SCOP fold classification system to assign possible known SCOP folds and recognize novel folds for newly-discovered proteins.With a sufficient amount of ground truth data, our system is able to assign the known folds for newly-discovered proteins in the latest SCOP v1.69 release with 92.17% accuracy. Our system also recognizes the novel folds with 89.27% accuracy using 10 fold cross validation. The average response time for proteins with 500 and 1409 amino acids to complete the classification process is 4.1 and 17.4 seconds, respectively. By comparison with several structural alignment algorithms, our approach outperforms previous methods on both the classification accuracy and efficiency.In this paper, we build an advanced, non-parametric classifier to accelerate the manual classification processes of SCOP. With satisfactory ground truth data from the SCOP database, our approach identifies relevant domain knowledge and yields reasonably accurate classifications. Our system is publicly accessible at http://ProteinDBS.rnet.missouri.edu/E-Predict.php.
Year
DOI
Venue
2006
10.1186/1471-2105-7-362
BMC Bioinformatics
Keywords
Field
DocType
amino acid,protein structure,protein conformation,proteins,structure alignment,classification system,microarrays,ground truth,cross validation,protein folding,artificial intelligence,sequence alignment,algorithms,bioinformatics,domain knowledge,structural classification of proteins
Human taxonomy,Computer science,Threading (protein sequence),Structural classification,Artificial intelligence,Bioinformatics,Structural Classification of Proteins database,Machine learning,Protein structure
Journal
Volume
Issue
ISSN
7
1
1471-2105
Citations 
PageRank 
References 
26
0.52
15
Authors
3
Name
Order
Citations
PageRank
Pin-Hao Chi1543.31
Chi-Ren Shyu265667.58
Dong Xu3714.63