Skip Navigation


Bioinformatics Advance Access originally published online on March 3, 2005
Bioinformatics 2005 21(10):2264-2270; doi:10.1093/bioinformatics/bti363
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
21/10/2264    most recent
bti363v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (5)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Bae, K.
Right arrow Articles by Elsik, C. G.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Bae, K.
Right arrow Articles by Elsik, C. G.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Prediction of protein interdomain linker regions by a hidden Markov model

Kyounghwa Bae 1, Bani K. Mallick 1 and Christine G. Elsik 2,*

1Department of Statistics, Texas A&M University College Station, TX 77843-3143, USA
2Department of Animal Science and Intercollegiate Faculty of Genetics, Texas A&M University College Station, TX 77843-2471, USA

*To whom correspondence should be addressed.

Motivation: Our aim was to predict protein interdomain linker regions using sequence alone, without requiring known homology. Identifying linker regions will delineate domain boundaries, and can be used to computationally dissect proteins into domains prior to clustering them into families. We developed a hidden Markov model of linker/non-linker sequence regions using a linker index derived from amino acid propensity. We employed an efficient Bayesian estimation of the model using Markov Chain Monte Carlo, Gibbs sampling in particular, to simulate parameters from the posteriors. Our model recognizes sequence data to be continuous rather than categorical, and generates a probabilistic output.

Results: We applied our method to a dataset of protein sequences in which domains and interdomain linkers had been delineated using the Pfam-A database. The prediction results are superior to a simpler method that also uses linker index.

Contact: c-elsik{at}tamu.edu

Supplementary information: http://racerx00.tamu.edu/kbae


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Nucleic Acids ResHome page
C. N.I. Pang, K. Lin, M. A. Wouters, J. Heringa, and R. A. George
Identifying foldable regions in protein sequence from the hydrophobic signal
Nucleic Acids Res., February 2, 2008; 36(2): 578 - 588.
[Abstract] [Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.