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Open Access Open Badges Research article

An evolutionary method for learning HMM structure: prediction of protein secondary structure

Kyoung-Jae Won123*, Thomas Hamelryck1, Adam Prügel-Bennett2 and Anders Krogh1

Author Affiliations

1 Bioinformatics Centre, Department of Molecular Biology, University of Copenhagen, Ole Maaloes Vej 5, DK-2200 Copenhagen, Denmark

2 School of Electronics and Computer Science, University of Southampton, SO17 1BJ, UK

3 Department of Chemistry & Biochemistry, UCSD, 9500 Gilman Drive, Mail Code 0359, La Jolla, CA, 92093-0359, USA

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BMC Bioinformatics 2007, 8:357  doi:10.1186/1471-2105-8-357

Published: 21 September 2007



The prediction of the secondary structure of proteins is one of the most studied problems in bioinformatics. Despite their success in many problems of biological sequence analysis, Hidden Markov Models (HMMs) have not been used much for this problem, as the complexity of the task makes manual design of HMMs difficult. Therefore, we have developed a method for evolving the structure of HMMs automatically, using Genetic Algorithms (GAs).


In the GA procedure, populations of HMMs are assembled from biologically meaningful building blocks. Mutation and crossover operators were designed to explore the space of such Block-HMMs. After each step of the GA, the standard HMM estimation algorithm (the Baum-Welch algorithm) was used to update model parameters. The final HMM captures several features of protein sequence and structure, with its own HMM grammar. In contrast to neural network based predictors, the evolved HMM also calculates the probabilities associated with the predictions. We carefully examined the performance of the HMM based predictor, both under the multiple- and single-sequence condition.


We have shown that the proposed evolutionary method can automatically design the topology of HMMs. The method reads the grammar of protein sequences and converts it into the grammar of an HMM. It improved previously suggested evolutionary methods and increased the prediction quality. Especially, it shows good performance under the single-sequence condition and provides probabilistic information on the prediction result. The protein secondary structure predictor using HMMs (P.S.HMM) is on-line available It runs under the single-sequence condition.