Protein function annotation from sequence: prediction of residues interacting with RNA
Author(s) -
Ruth V. Spriggs,
Yoichi Murakami,
H. Nakamura,
Susan R. Jones
Publication year - 2009
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/btp257
Subject(s) - annotation , proteome , support vector machine , function (biology) , computer science , computational biology , matthews correlation coefficient , protein function , sequence (biology) , rna , protein function prediction , machine learning , artificial intelligence , data mining , bioinformatics , biology , genetics , gene
All eukaryotic proteomes are characterized by a significant percentage of proteins of unknown function. Comp-utational function prediction methods are therefore essential as initial steps in the function annotation process. This article describes an annotation method (PiRaNhA) for the prediction of RNA-binding residues (RBRs) from protein sequence information. A series of sequence properties (position specific scoring matrices, interface propensities, predicted accessibility and hydrophobicity) are used to train a support vector machine. This method is then evaluated for its potential to be applied to RNA-binding function prediction at the level of the complete protein.
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