Transmembrane Strand Predictors (Porin), class contains two different
predictors, one Neural Network (NN) based and the other built upon a Hidden
Markov Model (HMM). Both of them make use of "Sequence Profiles".
- B2TMR (NN and Profile)
- B2TMR-HMM (HMM and Profile)
- Target: Porins, maltoporins, outer membrane proteins, ligand-gated
protein channels and other transmembrane proteins with
a beta-barrel structure.
- Output: Output is presented graphically in two colors, according to the
legend at the bottom of the image, one for the predicted
transmembrane beta strands and the other for the outer-inner
membrane protein sequence parts (loops).
The output is represented by two predicted states for each
aminoacidic position, one representing the probability to be in
a transmembrane beta strands and the other to be into a loop.
The difference between these two predicted probability is
represented as a continuous line interpolating each discrete
In the table of the results each segments is separated from the
others and dedicated labels help to classify them according to
their predicted feature:
This protein membrane orientation derives directly from the
method only for the HMM based one. For the Neural Network based
predictor is applied a further computation based on rules
derived from the loop length.
- Internal - for inside membrane surface loops
- Transmembrane - for membrane spanning segments (beta-strands)
- External - for outside membrane surface loops
For more detailed information see:
Jacoboni I, Martelli PL, Fariselli P, De Pinto V, Casadio R
Protein sci 10: 779-787 (2001), Martelli PL, Fariselli P,
Krogh A, and Casadio R Bioinformatics Vol.18 no.90001 (2002).