Literature DB >> 16524835

Towards genome-scale structure prediction for transmembrane proteins.

Naama Hurwitz1, Marialuisa Pellegrini-Calace, David T Jones.   

Abstract

In this paper we briefly review some of the recent progress made by ourselves and others in developing methods for predicting the structures of transmembrane proteins from amino acid sequence. Transmembrane proteins are an important class of proteins involved in many diverse biological functions, many of which have great impact in terms of disease mechanism and drug discovery. Despite their biological importance, it has proven very difficult to solve the structures of these proteins by experimental techniques, and so there is a great deal of pressure to develop effective methods for predicting their structure. The methods we discuss range from methods for transmembrane topology prediction to new methods for low resolution folding simulations in a knowledge-based force field. This potential is designed to reproduce the properties of the lipid bilayer. Our eventual aim is to apply these methods in tandem so that useful three-dimensional models can be built for a large fraction of the transmembrane protein domains in whole proteomes.

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Year:  2006        PMID: 16524835      PMCID: PMC1609336          DOI: 10.1098/rstb.2005.1804

Source DB:  PubMed          Journal:  Philos Trans R Soc Lond B Biol Sci        ISSN: 0962-8436            Impact factor:   6.237


  44 in total

1.  kPROT: a knowledge-based scale for the propensity of residue orientation in transmembrane segments. Application to membrane protein structure prediction.

Authors:  Y Pilpel; N Ben-Tal; D Lancet
Journal:  J Mol Biol       Date:  1999-12-10       Impact factor: 5.469

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Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

Review 3.  Membrane protein folding and stability: physical principles.

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Journal:  Annu Rev Biophys Biomol Struct       Date:  1999

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Authors:  T J Stevens; I T Arkin
Journal:  Proteins       Date:  1999-07-01

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Authors:  D C Rees; D Eisenberg
Journal:  Proteins       Date:  2000-02-01

6.  Principles governing amino acid composition of integral membrane proteins: application to topology prediction.

Authors:  G E Tusnády; I Simon
Journal:  J Mol Biol       Date:  1998-10-23       Impact factor: 5.469

7.  A hidden Markov model for predicting transmembrane helices in protein sequences.

Authors:  E L Sonnhammer; G von Heijne; A Krogh
Journal:  Proc Int Conf Intell Syst Mol Biol       Date:  1998

Review 8.  Do transmembrane protein superfolds exist?

Authors:  D T Jones
Journal:  FEBS Lett       Date:  1998-02-27       Impact factor: 4.124

9.  Genome-wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms.

Authors:  E Wallin; G von Heijne
Journal:  Protein Sci       Date:  1998-04       Impact factor: 6.725

10.  InterPro, progress and status in 2005.

Authors:  Nicola J Mulder; Rolf Apweiler; Teresa K Attwood; Amos Bairoch; Alex Bateman; David Binns; Paul Bradley; Peer Bork; Phillip Bucher; Lorenzo Cerutti; Richard Copley; Emmanuel Courcelle; Ujjwal Das; Richard Durbin; Wolfgang Fleischmann; Julian Gough; Daniel Haft; Nicola Harte; Nicolas Hulo; Daniel Kahn; Alexander Kanapin; Maria Krestyaninova; David Lonsdale; Rodrigo Lopez; Ivica Letunic; Martin Madera; John Maslen; Jennifer McDowall; Alex Mitchell; Anastasia N Nikolskaya; Sandra Orchard; Marco Pagni; Chris P Ponting; Emmanuel Quevillon; Jeremy Selengut; Christian J A Sigrist; Ville Silventoinen; David J Studholme; Robert Vaughan; Cathy H Wu
Journal:  Nucleic Acids Res       Date:  2005-01-01       Impact factor: 16.971

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  9 in total

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2.  A generalized born implicit-membrane representation compared to experimental insertion free energies.

Authors:  Martin B Ulmschneider; Jakob P Ulmschneider; Mark S P Sansom; Alfredo Di Nola
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3.  Introduction. Bioinformatics: from molecules to systems.

Authors:  David T Jones; Michael J E Sternberg; Janet M Thornton
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2006-03-29       Impact factor: 6.237

4.  Transmembrane START domain proteins: in silico identification, characterization and expression analysis under stress conditions in chickpea (Cicer arietinum L.).

Authors:  Viswanathan Satheesh; Parameswaran Chidambaranathan; Prasanth Tejkumar Jagannadham; Vajinder Kumar; Pradeep K Jain; Viswanathan Chinnusamy; Shripad R Bhat; R Srinivasan
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5.  Knowledge-based potential for positioning membrane-associated structures and assessing residue-specific energetic contributions.

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Journal:  Structure       Date:  2012-05-09       Impact factor: 5.006

6.  CREST--a large and diverse superfamily of putative transmembrane hydrolases.

Authors:  Jimin Pei; Douglas P Millay; Eric N Olson; Nick V Grishin
Journal:  Biol Direct       Date:  2011-07-06       Impact factor: 4.540

7.  Assessing the ability of sequence-based methods to provide functional insight within membrane integral proteins: a case study analyzing the neurotransmitter/Na+ symporter family.

Authors:  Dennis R Livesay; Patrick D Kidd; Sepehr Eskandari; Usman Roshan
Journal:  BMC Bioinformatics       Date:  2007-10-17       Impact factor: 3.169

8.  Transmembrane helix prediction using amino acid property features and latent semantic analysis.

Authors:  Madhavi Ganapathiraju; N Balakrishnan; Raj Reddy; Judith Klein-Seetharaman
Journal:  BMC Bioinformatics       Date:  2008       Impact factor: 3.169

9.  Membrane protein orientation and refinement using a knowledge-based statistical potential.

Authors:  Timothy Nugent; David T Jones
Journal:  BMC Bioinformatics       Date:  2013-09-18       Impact factor: 3.169

  9 in total

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