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


CONCLUSIONS
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.
Fariselli
Piero
P
Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy. piero.fariselli@unibo.it
Martelli
Pier Luigi
PL
Casadio
Rita
R
D000465:Algorithms; D001499:Bayes Theorem; D002462:Cell Membrane / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D030562:Databases, Protein; D020407:Internet; D008390:Markov Chains; D008565:Membrane Proteins; D008954:Models, Biological; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D011336:Probability; D017433:Protein Structure, Secondary; D017422:Sequence Analysis, DNA; D020539:Sequence Analysis, Protein; D012984:Software
D008565:Membrane Proteins