A Hidden Markov Model method, capable of predicting and discriminating beta-barrel outer membrane proteins.
In this work we developed a method based on a Hidden Markov Model,
capable of predicting the transmembrane beta-strands of the gram-negative
bacteria outer membrane proteins, and of discriminating such proteins
from water-soluble ones when screening large datasets.
The model is trained in a discriminative manner,
aiming at maximizing the probability of the correct
prediction rather than the likelihood of the sequences.
The training is performed on a non-redundant database
consisting of 16 outer membrane proteins (OMP's) with their structures known at atomic resolution.
We show that we can achieve predictions at least as good comparing with other existing methods, using as input only the amino-acid sequence, without the need of evolutionary information included in multiple alignments. The method is also powerful when used for discrimination purposes, as it can discriminate with a high accuracy the outer membrane proteins from water soluble in large datasets, making it a quite reliable solution for screening entire genomes.
This web-server can help you run a discriminating process on any amino-acid sequence and thereafter localize the transmembrane strands and find the topology of the loops.
University of Athens
Faculty of Biology
Dept. of Cell Biology and Biophysics