TPRpred: A tool for prediction of TPR-, PPR- and SELI-like repeats from protein 
sequences.

Karpenahalli, M. R.; Lupas, A. N.; Söding, J.

doi:10.1186/1471-2105-8-2

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

TPRpred: A tool for prediction of TPR-, PPR- and SELI-like repeats from protein sequences.

MPS-Authors

/persons/resource/persons128572

Söding, J.
Research Group of Computational Biology, MPI for Biophysical Chemistry, Max Planck Society;

External Resource

http://www.biomedcentral.com/content/pdf/1471-2105-8-2.pdf
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

1944241.pdf
(Publisher version), 837KB

Supplementary Material (public)

There is no public supplementary material available

Citation

Karpenahalli, M. R., Lupas, A. N., & Söding, J. (2007). TPRpred: A tool for prediction of TPR-, PPR- and SELI-like repeats from protein sequences. BMC Bioinformatics, 8: 2. doi:10.1186/1471-2105-8-2.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0017-E73E-C

Abstract

Background Solenoid repeat proteins of the Tetratrico Peptide Repeat (TPR) family are involved as scaffolds in a broad range of protein-protein interactions. Several resources are available for the prediction of TPRs, however, they often fail to detect divergent repeat units. Results We have developed TPRpred, a profile-based method which uses a P-value-dependent score offset to include divergent repeat units and which exploits the tendency of repeats to occur in tandem. TPRpred detects not only TPR-like repeats, but also the related Pentatrico Peptide Repeats (PPRs) and SEL1-like repeats. The corresponding profiles were generated through iterative searches, by varying the threshold parameters for inclusion of repeat units into the profiles, and the best profiles were selected based on their performance on proteins of known structure. We benchmarked the performance of TPRpred in detecting TPR-containing proteins and in delineating the individual repeats therein, against currently available resources. Conclusion TPRpred performs significantly better in detecting divergent repeats in TPR-containing proteins, and finds more individual repeats than the existing methods. The web server is available at http://tprpred.tuebingen.mpg.de webcite, and the C++ and Perl sources of TPRpred along with the profiles can be downloaded from ftp://ftp.tuebingen.mpg.de/ebio/protevo/TPRpred/ webcite.