Literature DB >> 19089980

A unified hydrophobicity scale for multispan membrane proteins.

Julia Koehler1, Nils Woetzel, René Staritzbichler, Charles R Sanders, Jens Meiler.   

Abstract

The concept of hydrophobicity is critical to our understanding of the principles of membrane protein (MP) folding, structure, and function. In the last decades, several groups have derived hydrophobicity scales using both experimental and statistical methods that are optimized to mimic certain natural phenomena as closely as possible. The present work adds to this toolset the first knowledge-based scale that unifies the characteristics of both alpha-helical and beta-barrel multispan MPs. This unified hydrophobicity scale (UHS) distinguishes between amino acid preference for solution, transition, and trans-membrane states. The scale represents average hydrophobicity values of amino acids in folded proteins, irrespective of their secondary structure type. We furthermore present the first knowledge-based hydrophobicity scale for mammalian alpha-helical MPs (mammalian hydrophobicity scale--MHS). Both scales are particularly useful for computational protein structure elucidation, for example as input for machine learning techniques, such as secondary structure or trans-membrane span prediction, or as reference energies for protein structure prediction or protein design. The knowledge-based UHS shows a striking similarity to a recent experimental hydrophobicity scale introduced by Hessa and coworkers (Hessa T et al., Nature 2007;450:U1026-U1032). Convergence of two very different approaches onto similar hydrophobicity values consolidates the major differences between experimental and knowledge-based scales observed in earlier studies. Moreover, the UHS scale represents an accurate absolute free energy measure for folded, multispan MPs--a feature that is absent from many existing scales. The utility of the UHS was demonstrated by analyzing a series of diverse MPs. It is further shown that the UHS outperforms nine established hydrophobicity scales in predicting trans-membrane spans along the protein sequence. The accuracy of the present hydrophobicity scale profits from the doubling of the number of integral MPs in the PDB over the past four years. The UHS paves the way for an increased accuracy in the prediction of trans-membrane spans.

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Year:  2009        PMID: 19089980      PMCID: PMC2761718          DOI: 10.1002/prot.22315

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  42 in total

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Authors:  J Janin
Journal:  Nature       Date:  1979-02-08       Impact factor: 49.962

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Journal:  Macromolecules       Date:  1978 Jan-Feb       Impact factor: 5.985

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Journal:  Nature       Date:  1978-04-13       Impact factor: 49.962

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Journal:  Proc Natl Acad Sci U S A       Date:  1980-08       Impact factor: 11.205

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Journal:  Biochemistry       Date:  1981-02-17       Impact factor: 3.162

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Journal:  Science       Date:  1979-11-02       Impact factor: 47.728

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  23 in total

1.  Tyrosine-lipid peroxide adducts from radical termination: para coupling and intramolecular Diels-Alder cyclization.

Authors:  Roman Shchepin; Matias N Möller; Hye-young H Kim; Duane M Hatch; Silvina Bartesaghi; Balaraman Kalyanaraman; Rafael Radi; Ned A Porter
Journal:  J Am Chem Soc       Date:  2010-11-19       Impact factor: 15.419

Review 2.  Marginally hydrophobic transmembrane α-helices shaping membrane protein folding.

Authors:  Minttu T De Marothy; Arne Elofsson
Journal:  Protein Sci       Date:  2015-05-30       Impact factor: 6.725

3.  Improved prediction of trans-membrane spans in proteins using an Artificial Neural Network.

Authors:  Julia Koehler; Ralf Mueller; Jens Meiler
Journal:  IEEE Symp Comput Intell Bioinforma Comput Biol Proc       Date:  2009-05-15

4.  Integrated Structural Biology for α-Helical Membrane Protein Structure Determination.

Authors:  Yan Xia; Axel W Fischer; Pedro Teixeira; Brian Weiner; Jens Meiler
Journal:  Structure       Date:  2018-03-08       Impact factor: 5.006

5.  Outer Membrane Protein Folding and Topology from a Computational Transfer Free Energy Scale.

Authors:  Meishan Lin; Dennis Gessmann; Hammad Naveed; Jie Liang
Journal:  J Am Chem Soc       Date:  2016-02-19       Impact factor: 15.419

Review 6.  Computational modeling of membrane proteins.

Authors:  Julia Koehler Leman; Martin B Ulmschneider; Jeffrey J Gray
Journal:  Proteins       Date:  2014-11-19

7.  Simultaneous prediction of protein secondary structure and transmembrane spans.

Authors:  Julia Koehler Leman; Ralf Mueller; Mert Karakas; Nils Woetzel; Jens Meiler
Journal:  Proteins       Date:  2013-04-10

8.  Molecular mechanisms of Slo2 K+ channel closure.

Authors:  M Hunter Giese; Alison Gardner; Angela Hansen; Michael C Sanguinetti
Journal:  J Physiol       Date:  2016-12-02       Impact factor: 5.182

9.  Determination of Hydrophobic Lengths of Membrane Proteins with the HDGB Implicit Membrane Model.

Authors:  Bercem Dutagaci; Michael Feig
Journal:  J Chem Inf Model       Date:  2017-12-01       Impact factor: 4.956

Review 10.  Life at the border: adaptation of proteins to anisotropic membrane environment.

Authors:  Irina D Pogozheva; Henry I Mosberg; Andrei L Lomize
Journal:  Protein Sci       Date:  2014-07-02       Impact factor: 6.725

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