Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks

Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks

Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction. In this paper we expand upon this work by making use of the more powerful class of dynamic Bayesian networks (DBNs). Our model, Philius, is inspired by a previously published HMM, Phobius, and combines a signal peptide submodel with a transmembrane ...

A combined transmembrane topology and signal peptide prediction method.

An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes....

The presence of signal peptide significantly affects transmembrane topology prediction

The presence of signal peptide in the query sequence complicates the transmembrane (TM) topology prediction because the hydrophobic core of signal peptide is easily predicted as the putative first TM segment (Lao and Shimizu, 2001). In genome wide analyses, the likely signal peptide region is treated in several ways. It was either masked out from topological calculations (Jones, 1998) or omitte...

Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server

When using conventional transmembrane topology and signal peptide predictors, such as TMHMM and SignalP, there is a substantial overlap between these two types of predictions. Applying these methods to five complete proteomes, we found that 30-65% of all predicted signal peptides and 25-35% of all predicted transmembrane topologies overlap. This impairs predictions of 5-10% of the proteome, hen...

Bridge Condition Modelling and Prediction Using Dynamic Bayesian Belief Networks