title: An Integrated Framework for Functional Annotation of Protein Structural Domains.
abstract: 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 ~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.
authors.forename: Lei
authors.initials: L
authors.lastname: Deng
authors.forename: Zhigang
authors.initials: Z
authors.lastname: Chen
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D058977:Molecular Sequence Annotation / Q000379:methods*; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000145:classification*
chemical_list: D011506:Proteins