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 . 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.
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