期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 12, 期 4, 页码 902-913出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2015.2389213
关键词
Scop domain function; structure alignment; PSSM; Bayesian network
类别
资金
- National Natural Science Foundation of China [61309010, 61379057]
- Specialized Research Fund for the Doctoral Program of Higher Education of China [20130162120073]
- Central South University
Structural domains are evolutionary and functional units of proteins and play a critical role in comparative and functional genomics. Computational assignment of domain function with high reliability is essential for understanding whole-protein functions. However, functional annotations are conventionally assigned onto full-length proteins rather than associating specific functions to the individual structural domains. In this article, we present Structural Domain Annotation (SDA), a novel computational approach to predict functions for SCOP structural domains. The SDA method integrates heterogeneous information sources, including structure alignment based protein-SCOP mapping features, InterPro2GO mapping information, PSSM Profiles, and sequence neighborhood features, with a Bayesian network. By large-scale annotating Gene Ontology terms to SCOP domains with SDA, we obtained a database of SCOP domain to Gene Ontology mappings, which contains similar to 162,000 out of the approximately 166,900 domains in SCOPe 2.03 (>97 percent) and their predicted Gene Ontology functions. We have benchmarked SDA using a single-domain protein dataset and an independent dataset from different species. Comparative studies show that SDA significantly outperforms the existing function prediction methods for structural domains in terms of coverage and maximum F-measure.
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