Research article

Analysis of an optimal hidden Markov model for secondary structure prediction

Juliette Martin^1,2 , Jean-François Gibrat² and François Rodolphe²

¹  INSERM U726, Equipe de Bioinformatique Génomique et Moléculaire Université Denis Diderot Paris 7, 2 place jussieu, 75251 Paris Cedex 05, France

²  INRA, Unité Mathématiques Informatique et Génome, Domaine de Vilvert, 78352 Jouy en Josas Cedex, France

author email corresponding author email

BMC Structural Biology 2006, 6:25doi:10.1186/1472-6807-6-25

Published:	13 December 2006

Abstract

Background

Secondary structure prediction is a useful first step toward 3D structure prediction. A number of successful secondary structure prediction methods use neural networks, but unfortunately, neural networks are not intuitively interpretable. On the contrary, hidden Markov models are graphical interpretable models. Moreover, they have been successfully used in many bioinformatic applications. Because they offer a strong statistical background and allow model interpretation, we propose a method based on hidden Markov models.

Results

Our HMM is designed without prior knowledge. It is chosen within a collection of models of increasing size, using statistical and accuracy criteria. The resulting model has 36 hidden states: 15 that model α-helices, 12 that model coil and 9 that model β-strands. Connections between hidden states and state emission probabilities reflect the organization of protein structures into secondary structure segments. We start by analyzing the model features and see how it offers a new vision of local structures. We then use it for secondary structure prediction. Our model appears to be very efficient on single sequences, with a Q3 score of 68.8%, more than one point above PSIPRED prediction on single sequences. A straightforward extension of the method allows the use of multiple sequence alignments, rising the Q3 score to 75.5%.

Conclusion

The hidden Markov model presented here achieves valuable prediction results using only a limited number of parameters. It provides an interpretable framework for protein secondary structure architecture. Furthermore, it can be used as a tool for generating protein sequences with a given secondary structure content.