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Bioinformatics Advance Access originally published online on March 3, 2005
Bioinformatics 2005 21(10):2264-2270; doi:10.1093/bioinformatics/bti363
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© 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


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