Sequence-based protein structure prediction using a reduced state-space hidden Markov model.
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.
Lampros
Christos
C
Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece.
Costas Papaloukas
Exarchos
Themis P
TP
Yorgos Goletsis
Fotiadis
Dimitrios I
DI
D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D016013:Likelihood Functions; D008390:Markov Chains; D008956:Models, Chemical; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D012372:ROC Curve; D017386:Sequence Homology, Amino Acid
D011506:Proteins