An HMM model for coiled-coil domains and a comparison with PSSM-based predictions.

Details

Serval ID
serval:BIB_9CA288184742
Type
Article: article from journal or magazin.
Collection
Publications
Title
An HMM model for coiled-coil domains and a comparison with PSSM-based predictions.
Journal
Bioinformatics
Author(s)
Delorenzi M., Speed T.
ISSN
1367-4803 (Print)
ISSN-L
1367-4803
Publication state
Published
Issued date
2002
Volume
18
Number
4
Pages
617-625
Language
english
Abstract
MOTIVATION: Large-scale sequence data require methods for the automated annotation of protein domains. Many of the predictive methods are based either on a Position Specific Scoring Matrix (PSSM) of fixed length or on a window-less Hidden Markov Model (HMM). The performance of the two approaches is tested for Coiled-Coil Domains (CCDs). The prediction of CCDs is used frequently, and its optimization seems worthwhile.
RESULTS: We have conceived MARCOIL, an HMM for the recognition of proteins with a CCD on a genomic scale. A cross-validated study suggests that MARCOIL improves predictions compared to the traditional PSSM algorithm, especially for some protein families and for short CCDs. The study was designed to reveal differences inherent in the two methods. Potential confounding factors such as differences in the dimension of parameter space and in the parameter values were avoided by using the same amino acid propensities and by keeping the transition probabilities of the HMM constant during cross-validation.
AVAILABILTY: The prediction program and the databases are available at http://www.wehi.edu.au/bioweb/Mauro/Marcoil
Keywords
Algorithms, Amino Acid Sequence, Databases, Protein, Genome, Information Storage and Retrieval/methods, Markov Chains, Models, Genetic, Models, Statistical, Molecular Sequence Data, Protein Conformation, Proteins/chemistry, Proteins/classification, Reproducibility of Results, Sensitivity and Specificity, Sequence Analysis, Protein/methods, Software
Pubmed
Web of science
Open Access
Yes
Create date
22/04/2013 8:26
Last modification date
20/08/2019 15:03
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