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SDADB: a functional annotation database of protein structural domains
Author(s) -
Cheng Zeng,
Weihua Zhan,
Lei Deng
Publication year - 2018
Publication title -
database
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.406
H-Index - 62
ISSN - 1758-0463
DOI - 10.1093/database/bay064
Subject(s) - computer science , annotation , domain (mathematical analysis) , probabilistic logic , protein domain , information retrieval , tree (set theory) , ontology , visualization , data mining , artificial intelligence , biology , gene , mathematics , mathematical analysis , philosophy , biochemistry , epistemology
Annotating functional terms with individual domains is essential for understanding the functions of full-length proteins. We describe SDADB, a functional annotation database for structural domains. SDADB provides associations between gene ontology (GO) terms and SCOP domains calculated with an integrated framework. GO annotations are assigned probabilities of being correct, which are estimated with a Bayesian network by taking advantage of structural neighborhood mappings, SCOP-InterPro domain mapping information, position-specific scoring matrices (PSSMs) and sequence homolog features, with the most substantial contribution coming from high-coverage structure-based domain-protein mappings. The domain-protein mappings are computed using large-scale structure alignment. SDADB contains ontological terms with probabilistic scores for more than 214 000 distinct SCOP domains. It also provides additional features include 3D structure alignment visualization, GO hierarchical tree view, search, browse and download options.Database URL: http://sda.denglab.org.

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