Open AccessThe prediction of membrane protein structure and genome structural annotation
Author(s) -
Martelli Pier Luigi,
Fariselli Piero,
Tasco Gianluca,
Casadio Rita
Publication year - 2003
Publication title -
comparative and functional genomics
Language(s) - English
Resource type - Journals
eISSN - 1532-6268
pISSN - 1531-6912
DOI - 10.1002/cfg.308
Subject(s) - false positive paradox , hidden markov model , computer science , proteome , false positives and false negatives , suite , genome , signature (topology) , artificial intelligence , filter (signal processing) , membrane protein , computational biology , true positive rate , false positive rate , data mining , pattern recognition (psychology) , machine learning , bioinformatics , membrane , biology , gene , genetics , computer vision , mathematics , history , geometry , archaeology
New methods, essentially based on hidden Markov models (HMM) and neural networks (NN), can predict the topography of both β‐barrel and all‐α membrane proteins with high accuracy and a low rate of false positives and false negatives. These methods have been integrated in a suite of programs to filter proteomes of Gram‐negative bacteria, searching for new membrane proteins. Copyright © 2003 John Wiley & Sons, Ltd.