Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: Italian Society of Bioinformatics (BITS): Annual Meeting 2005

Open Access Research article

A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins

Piero Fariselli*, Pier Luigi Martelli and Rita Casadio

Author Affiliations

Department of Biology, University of Bologna via Irnerio 42, 40126 Bologna, Italy

For all author emails, please log on.

BMC Bioinformatics 2005, 6(Suppl 4):S12  doi:10.1186/1471-2105-6-S4-S12

Published: 1 December 2005

Abstract

Background

Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the labels, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the HMM grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi.

Results

In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm.

Conclusion

We show that PV decoding performs better than other algorithms when tested on the problem of the prediction of the topology of beta-barrel membrane proteins.