Version 1.0

Prediction of Transmembrane regions in proteins

Pasquier, C. and Hamodrakas, S.J.
Department of Cell Biology and Biophysics,
Faculty of Biology, University of Athens
Panepistimiopolis, Kouponia, Athens 15701, Greece

PRED-TMR is a method that predicts transmembrane domains in proteins using solely information contained in the sequence itself. The algorithm refines a standard hydrophobicity analysis with a detection of potential termini ("edges", starts and ends) of transmembrane regions. This allows both to discard highly hydrophobic regions not delimited by clear start and end configurations and to confirm putative transmembrane segments not distinguishable by their hydrophobic composition.

We have now extended this application with a pre-processing stage represented by an artificial neural network which is able to discriminate with a high accuracy transmembrane proteins from soluble or fibrous ones.

Applied on several test sets of transmembrane proteins, the system gives a perfect prediction rating of 100% by classifying all the sequences in the transmembrane class. Applied on 995 non-transmembrane protein extracted from the PDBSELECT database, the neural network predicts falsely 23 of them to be transmembrane (97.7% of correct assignment).

The neural network is described in :

Pasquier C, Hamodrakas SJ:
An hierarchical artificial neural network system for the classification of transmembrane proteins. Protein Eng 1999 Aug;12(8):631-4

Now, you can :

Run PRED-TMR2 on a sequence

Browse the results obtained with the algorithm

Go to the Biophysics Lab Homepage

Claude Pasquier