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Open Access Research article

MPRAP: An accessibility predictor for a-helical transmem-brane proteins that performs well inside and outside the membrane

Kristoffer Illergård, Simone Callegari and Arne Elofsson*

Author Affiliations

Center for Biomembrane Research, Stockholm Bioinformatics Center, Dept. of Biochemistry and biophysics, Stockholm University, SE-106 91 Stockholm, Sweden

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BMC Bioinformatics 2010, 11:333  doi:10.1186/1471-2105-11-333

Published: 18 June 2010

Abstract

Background

In water-soluble proteins it is energetically favorable to bury hydrophobic residues and to expose polar and charged residues. In contrast to water soluble proteins, transmembrane proteins face three distinct environments; a hydrophobic lipid environment inside the membrane, a hydrophilic water environment outside the membrane and an interface region rich in phospholipid head-groups. Therefore, it is energetically favorable for transmembrane proteins to expose different types of residues in the different regions.

Results

Investigations of a set of structurally determined transmembrane proteins showed that the composition of solvent exposed residues differs significantly inside and outside the membrane. In contrast, residues buried within the interior of a protein show a much smaller difference. However, in all regions exposed residues are less conserved than buried residues. Further, we found that current state-of-the-art predictors for surface area are optimized for one of the regions and perform badly in the other regions. To circumvent this limitation we developed a new predictor, MPRAP, that performs well in all regions. In addition, MPRAP performs better on complete membrane proteins than a combination of specialized predictors and acceptably on water-soluble proteins. A web-server of MPRAP is available at http://mprap.cbr.su.se/ webcite

Conclusion

By including complete a-helical transmembrane proteins in the training MPRAP is able to predict surface accessibility accurately both inside and outside the membrane. This predictor can aid in the prediction of 3D-structure, and in the identification of erroneous protein structures.