Prediction of transmembrane alpha-helices in prokaryotic membrane proteins:
Application of the Dense Alignment Surface method

Miklos Cserzo#+, Erik Wallin*, Istvan Simon#, Gunnar von Heijne*, Arne Elofsson*

+University of Birmingham, School of Biochemistry
Ebgdaston, Birmingham B15 2TT, U.K.

#Institute of Enzymology, Biological Research Center
Hungarian Academy of Sciences
H-1518 Budapest P.O.Box 7, Hungary

*Department of Biochemistry, Stockholm University
S-106 91 Stockholm, Sweden

Transmembrane helices in integral membrane proteins are composed of stretches of 15-30 predominantly hydrophobic residues separated by polar connecting loops. A number of algorithms designed to identify putative transmembrane helices in the primary amino acid sequence have been developed, and current methods can identify around 90-95% of all true transmembrane segments with an over-prediction rate of only a few percent [1,2]. The best results have so far been obtained when multiply aligned sequences can b e analyzed; however, in many cases there are no homologs in the database and improvements in single-sequence prediction performance are thus important.

Recently, the so-called Dense Alignment Surface (DAS) method was introduced in an attempt to improve sequence alignments in the G-protein coupled receptor family of transmembrane proteins [3]. We have now generalized this method to predict transmembrane s egments in any integral membrane protein. DAS is based on low-stringency dot-plots of the query sequence against a collection of non-homologous membrane proteins using a previously derived, special scoring matrix.

We have compared the performance of four different transmembrane segment prediction methods: a sliding window averaging with trapezoid window, a method (TOPPRED) based on the "positive inside" rule, a neural network method (PHDhtm) including information f rom multiply aligned sequences, and the new DAS method. The predictive power of DAS and PHDhtm is essentially the same while the single-sequence based method performs slightly worse. Incorporating extra information related to the "positive inside" rule (T OPPRED) brings the predictive power to the level of the two other methods. This suggests that the DAS method, which uses only single sequence information, is as good as the PHDhtm method (which uses multiple sequence alignments) and TOPPRED (which uses extra information in the form of the distribution of positively charged residues) in predicting transmembrane segments in prokaryotic inner membrane proteins.

The curves obtained for the test set are available in gif format. You may wish to check them in order to have an idea about the efficiency of the prediction.

1; - von Heijne (1992), J. Mol.Biol., 225, 487-494
2; - Rost et al. (1996), Protein Sci., 5, 1704-1718
3; - Cserzo et al. (1994), J. Mol. Biol., 243, 388-396


This work was supported through the cooperation framework of the Royal Swedish Academy of Sciences and the Hungarian Academy of Sciences, and by grants from the Swedish Technical Sciences Research Council (TFR) and the Magnus Bergvall foundation to AE, and from the Swedish Natural Sciences Research Council to GvH.