A Bayesian network model for protein fold and remote homologue recognition.
BACKGROUND
The Bayesian network approach is a framework which combines graphical representation and probability theory, which includes, as a special case, hidden Markov models. Hidden Markov models trained on amino acid sequence or secondary structure data alone have been shown to have potential for addressing the problem of protein fold and superfamily classification.


RESULTS
This paper describes a novel implementation of a Bayesian network which simultaneously learns amino acid sequence, secondary structure and residue accessibility for proteins of known three-dimensional structure. An awareness of the errors inherent in predicted secondary structure may be incorporated into the model by means of a confusion matrix. Training and validation data have been derived for a number of protein superfamilies from the Structural Classification of Proteins (SCOP) database. Cross validation results using posterior probability classification demonstrate that the Bayesian network performs better in classifying proteins of known structural superfamily than a hidden Markov model trained on amino acid sequences alone.
Raval
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Keck Graduate Institute of Applied Life Sciences, 535 Watson Drive, Claremont, CA 91711, USA.
Ghahramani
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Wild
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D000595:Amino Acid Sequence; D001499:Bayes Theorem; D019295:Computational Biology; D008390:Markov Chains; D008958:Models, Molecular; D011338:Probability Theory; D017510:Protein Folding; D017433:Protein Structure, Secondary