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Towards region-specific propagation of protein functions
Author(s) -
Da Chen Emily Koo,
Richard Bonneau
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty834
Subject(s) - uniprot , protein function prediction , computer science , source code , function (biology) , categorization , protein domain , protein function , domain (mathematical analysis) , computational biology , annotation , data mining , artificial intelligence , machine learning , pattern recognition (psychology) , bioinformatics , biology , mathematics , genetics , mathematical analysis , gene , operating system
Due to the nature of experimental annotation, most protein function prediction methods operate at the protein-level, where functions are assigned to full-length proteins based on overall similarities. However, most proteins function by interacting with other proteins or molecules, and many functional associations should be limited to specific regions rather than the entire protein length. Most domain-centric function prediction methods depend on accurate domain family assignments to infer relationships between domains and functions, with regions that are unassigned to a known domain-family left out of functional evaluation. Given the abundance of residue-level annotations currently available, we present a function prediction methodology that automatically infers function labels of specific protein regions using protein-level annotations and multiple types of region-specific features.

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