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- Title
Protein family comparison using statistical models and predicted structural information.
- Authors
Chung, Richard; Yona, Golan
- Abstract
Background: This paper presents a simple method to increase the sensitivity of protein family comparisons by incorporating secondary structure (SS) information. We build upon the effective information theory approach towards profile-profile comparison described in [Yona & Levitt 2002]. Our method augments profile columns using PSIPRED secondary structure predictions and assesses statistical similarity using information theoretical principles. Results: Our tests show that this tool detects more similarities between protein families of distant homology than the previous primary sequence-based method. A very significant improvement in performance is observed when the real secondary structure is used. Conclusions: Integration of primary and secondary structure information can substantially improve detection of relationships between remotely related protein families.
- Subjects
PROTEIN analysis; INFORMATION theory in biology; HOMOLOGY (Biology); MATHEMATICAL models; BIOINFORMATICS
- Publication
BMC Bioinformatics, 2004, Vol 5, p183
- ISSN
1471-2105
- Publication type
Article
- DOI
10.1186/1471-2105-5-183