title: HMMs in Protein Fold Classification.
abstract: The limitation of most HMMs is their inherent high dimensionality. Therefore we developed several variations of low complexity models that can be applied even to protein families with a few members. In this chapter we present these variations. All of them include the use of a hidden Markov model (HMM), with a small number of states (called reduced state-space HMM), which is trained with both amino acid sequence and secondary structure of proteins whose 3D structure is known and it is used for protein fold classification. We used data from Protein Data Bank and annotation from SCOP database for training and evaluation of the proposed HMM variations for a number of protein folds that belong to major structural classes. Results indicate that the variations have similar performance, or even better in some cases, on classifying proteins than SAM, which is a widely used HMM-based method for protein classification. The major advantage of the proposed variations is that we employed a small number of states and the algorithms used for training and scoring are of low complexity and thus relatively fast. The main variations examined include a version of the reduced state-space HMM with seven states (7-HMM), a version of the reduced state-space HMM with three states (3-HMM) and an optimized version of the reduced state-space HMM with three states, where an optimization process is applied to its scores (optimized 3-HMM).
authors.affiliation: Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, University Campus of Ioannina, GR45110, Ioannina, Greece.
authors.forename: Christos
authors.initials: C
authors.lastname: Lampros
authors.affiliation: Department of Biological Applications and Technology, University of Ioannina, Ioannina, Greece.
authors.forename: Costas
authors.initials: C
authors.lastname: Papaloukas
authors.affiliation: Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, University Campus of Ioannina, GR45110, Ioannina, Greece.
authors.forename: Themis
authors.initials: T
authors.lastname: Exarchos
authors.affiliation: Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, University Campus of Ioannina, GR45110, Ioannina, Greece. Fotiadis@cc.uoi.gr.
authors.forename: Dimitrios I
authors.initials: DI
authors.lastname: Fotiadis
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D008390:Markov Chains; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000145:classification*
chemical_list: D011506:Proteins
keywords: Fold classification; Hidden Markov model; Optimization