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A structure-based protocol for learning the family-specific mechanisms of membrane-binding domains
Author(s) -
Morten Källberg,
Nitin Bhardwaj,
Robert Langlois,
Hui Lü
Publication year - 2012
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/bts409
Subject(s) - classifier (uml) , computational biology , artificial intelligence , computer science , sequence (biology) , function (biology) , machine learning , c2 domain , similarity (geometry) , bioinformatics , biology , membrane , biochemistry , genetics , image (mathematics)
Peripheral membrane-targeting domain (MTD) families, such as C1-, C2- and PH domains, play a key role in signal transduction and membrane trafficking by dynamically translocating their parent proteins to specific plasma membranes when changes in lipid composition occur. It is, however, difficult to determine the subset of domains within families displaying this property, as sequence motifs signifying the membrane binding properties are not well defined. For this reason, procedures based on sequence similarity alone are often insufficient in computational identification of MTDs within families (yielding less than 65% accuracy even with a sequence identity of 70%).

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