Principles governing amino acid composition of integral membrane proteins

Application to topology prediction

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905 Citations (Scopus)

Abstract

A new method is suggested here for topology prediction of helical transmembrane proteins. The method is based on the hypothesis that the localizations of the transmembrane segments and the topology are determined by the difference in the amino acid distributions in various structural parts of these proteins rather than by specific amino acid compositions of these parts. A hidden Markov model with special architecture was developed to search transmembrane topology corresponding to the maximum likelihood among all the possible topologies of a given protein. The prediction accuracy was tested on 158 proteins and was found to be higher than that found using prediction methods already available. The method successfully predicted all the transmembrane segments in 143 proteins out of the 158, and for 135 of these proteins both the membrane spanning regions and the topologies were predicted correctly. The observed level of accuracy is a strong argument in favor of our hypothesis.

Original languageEnglish
Pages (from-to)489-506
Number of pages18
JournalJournal of Molecular Biology
Volume283
Issue number2
DOIs
Publication statusPublished - Oct 23 1998

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Membrane Proteins
Amino Acids
Proteins

Keywords

  • Distribution of amino acids
  • Hidden Markov model
  • Topology prediction for helical transmembrane proteins
  • Transmembrane α-helices

ASJC Scopus subject areas

  • Virology

Cite this

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title = "Principles governing amino acid composition of integral membrane proteins: Application to topology prediction",
abstract = "A new method is suggested here for topology prediction of helical transmembrane proteins. The method is based on the hypothesis that the localizations of the transmembrane segments and the topology are determined by the difference in the amino acid distributions in various structural parts of these proteins rather than by specific amino acid compositions of these parts. A hidden Markov model with special architecture was developed to search transmembrane topology corresponding to the maximum likelihood among all the possible topologies of a given protein. The prediction accuracy was tested on 158 proteins and was found to be higher than that found using prediction methods already available. The method successfully predicted all the transmembrane segments in 143 proteins out of the 158, and for 135 of these proteins both the membrane spanning regions and the topologies were predicted correctly. The observed level of accuracy is a strong argument in favor of our hypothesis.",
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T1 - Principles governing amino acid composition of integral membrane proteins

T2 - Application to topology prediction

AU - Tusnády, G.

AU - Simon, I.

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AB - A new method is suggested here for topology prediction of helical transmembrane proteins. The method is based on the hypothesis that the localizations of the transmembrane segments and the topology are determined by the difference in the amino acid distributions in various structural parts of these proteins rather than by specific amino acid compositions of these parts. A hidden Markov model with special architecture was developed to search transmembrane topology corresponding to the maximum likelihood among all the possible topologies of a given protein. The prediction accuracy was tested on 158 proteins and was found to be higher than that found using prediction methods already available. The method successfully predicted all the transmembrane segments in 143 proteins out of the 158, and for 135 of these proteins both the membrane spanning regions and the topologies were predicted correctly. The observed level of accuracy is a strong argument in favor of our hypothesis.

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