PRED-TMBB: a web server for predicting the topology of 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.
Keywords for this software
References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Tran, Van Du T.; Chassignet, Philippe; Steyaert, Jean-Marc: On permuted super-secondary structures of transmembrane $\beta$-barrel proteins (2014)
- Hu, Jing; Yan, Changhui: A method for discovering transmembrane beta-barrel proteins in Gram-negative bacterial proteomes (2008)