title: Assessment of optimized Markov models in protein fold classification.
abstract: Protein fold classification is a challenging task strongly associated with the determination of proteins' structure. In this work, we tested an optimization strategy on a Markov chain and a recently introduced Hidden Markov Model (HMM) with reduced state-space topology. The proteins with unknown structure were scored against both these models. Then the derived scores were optimized following a local optimization method. The Protein Data Bank (PDB) and the annotation of the Structural Classification of Proteins (SCOP) database were used for the evaluation of the proposed methodology. The results demonstrated that the fold classification accuracy of the optimized HMM was substantially higher compared to that of the Markov chain or the reduced state-space HMM approaches. The proposed methodology achieved an accuracy of 41.4% on fold classification, while Sequence Alignment and Modeling (SAM), which was used for comparison, reached an accuracy of 38%.
authors.affiliation: Department of Materials Science and Engineering, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece.
authors.forename: Christos
authors.initials: C
authors.lastname: Lampros
authors.forename: Thomas
authors.initials: T
authors.lastname: Simos
authors.forename: Themis P
authors.initials: TP
authors.lastname: Exarchos
authors.forename: Konstantinos P
authors.initials: KP
authors.lastname: Exarchos
authors.forename: Costas
authors.initials: C
authors.lastname: Papaloukas
authors.forename: Dimitrios I
authors.initials: DI
authors.lastname: Fotiadis
mesh_terms: D030562:Databases, Protein; D008390:Markov Chains; D008958:Models, Molecular; D017510:Protein Folding
keywords: Fold classification; Markov chain; hidden Markov model; optimization