Sequence-based protein structure prediction using a reduced state-space hidden Markov model

Authors:
Christos Lampros; Costas Papaloukas;Themis P. Exarchos; Yorgos Goletsis;Dimitrios I. Fotiadis
Affiliations:
Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece and Department of Medical Physics, Medical School, ...;Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece and Department of Biological Applications and Tech ...;Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece and Department of Medical Physics, Medical School, ...;Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece and Department of Economics, University of Ioannin ...;Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece and Biomedical Research Institute-FORTH, GR 45110 ...
Venue:
Computers in Biology and Medicine
Year:
2007

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Abstract

This work describes the use of a hidden Markov model (HMM), with a reduced number of states, which simultaneously learns amino acid sequence and secondary structure for proteins of known three-dimensional structure and it is used for two tasks: protein class prediction and fold recognition. The Protein Data Bank and the annotation of the SCOP database are used for training and evaluation of the proposed HMM for a number of protein classes and folds. Results demonstrate that the reduced state-space HMM performs equivalently, or even better in some cases, on classifying proteins than a HMM trained with the amino acid sequence. The major advantage of the proposed approach is that a small number of states is employed and the training algorithm is of low complexity and thus relatively fast.