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Fishing new proteins in the twilight zone of genomes: the test case of outer membrane proteins in Escherichia coli K12, Escherichia coli O157:H7, and other Gram-negative bacteria
Authors:Casadio Rita  Fariselli Piero  Finocchiaro Giacomo  Martelli Pier Luigi
Institution:Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, 40126 Bologna, Italy. casadio@alma.unibo.it
Abstract:We address the problem of clustering the whole protein content of genomes into three different categories-globular, all-alpha, and all-beta membrane proteins-with the aim of fishing new membrane proteins in the pool of nonannotated proteins (twilight zone). The focus is then mainly on outer membrane proteins. This is performed by using an integrated suite of programs (Hunter) specifically developed for predicting the occurrence of signal peptides in proteins of Gram-negative bacteria and the topography of all-alpha and all-beta membrane proteins. Hunter is tested on the well and partially annotated proteins (2160 and 760, respectively) of Escherichia coli K 12 scoring as high as 95.6% in the correct assignment of each chain to the category. Of the remaining 1253 nonannotated sequences, 1099 are predicted globular, 136 are all-alpha, and 18 are all-beta membrane proteins. In Escherichia coli 0157:H7 we filtered 1901 nonannotated proteins. Our analysis classifies 1564 globular chains, 327 inner membrane proteins, and 10 outer membrane proteins. With Hunter, new membrane proteins are added to the list of putative membrane proteins of Gram-negative bacteria. The content of outer membrane proteins per genome (nine are analyzed) ranges from 1.5% to 2.4%, and it is one order of magnitude lower than that of inner membrane proteins. The finding is particularly relevant when it is considered that this is the first large-scale analysis based on validated tools that can predict the content of outer membrane proteins in a genome and can allow cross-comparison of the same protein type between different species.
Keywords:All‐β membrane proteins  all‐α membrane proteins  structural genomics  neural networks  hidden Markov models  topography prediction of membrane proteins
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