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Improving the accuracy of transmembrane protein topology prediction using evolutionary information
Author(s) -
David T. Jones
Publication year - 2007
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/btl677
Subject(s) - transmembrane protein , computer science , source code , benchmark (surveying) , topology (electrical circuits) , transmembrane domain , membrane protein , membrane topology , set (abstract data type) , web server , data mining , code (set theory) , computational biology , algorithm , pattern recognition (psychology) , artificial intelligence , biological system , biology , mathematics , biochemistry , membrane , receptor , the internet , geodesy , combinatorics , programming language , geography , operating system , world wide web
Many important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion are mediated by membrane proteins. Unfortunately, as these proteins are not water soluble, it is extremely hard to experimentally determine their structure. Therefore, improved methods for predicting the structure of these proteins are vital in biological research. In order to improve transmembrane topology prediction, we evaluate the combined use of both integrated signal peptide prediction and evolutionary information in a single algorithm.

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