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Author, Editor

Author(s):

Sander, Oliver
Sommer, Ingolf
Lengauer, Thomas

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BibTeX cite key*:

Sander2006

Title

Title*:

Local protein structure prediction using discriminative models

Journal

Journal Title*:

BMC Bioinformatics

Journal's URL:

http://www.biomedcentral.com/bmcbioinformatics

Download URL
for the article:

http://www.biomedcentral.com/content/pdf/1471-2105-7-27.pdf

Language:

English

Publisher

Publisher's
Name:

BioMed Central

Publisher's URL:


Publisher's
Address:

London, UK

ISSN:

14712105

Vol, No, Year, pp.

Volume:

7

Number:

14

Month:

January

Year*:

2006

Pages:

1-13

Number of VG Pages:


Sequence Number:


DOI:


Abstract, Links, (C)

Note:


(LaTeX) Abstract:

Background

In recent years protein structure prediction methods using local structure information have shown promising improvements. The quality of new fold predictions has risen significantly and in fold recognition incorporation of local structure predictions led to improvements in the accuracy of results.

We developed a local structure prediction method to be integrated into either fold recognition or new fold prediction methods. For each local sequence window of a protein sequence the method predicts probability estimates for the sequence to attain particular local structures from a set of predefined local structure candidates.

The first step is to define a set of local structure representatives based on clustering recurrent local structures. In the second step a discriminative model is trained to predict the local structure representative given local sequence information.

Results

The step of clustering local structures yields an average RMSD quantization error of 1.19 Å for 27 structural representatives (for a fragment length of 7 residues). In the prediction step the area under the ROC curve for detection of the 27 classes ranges from 0.68 to 0.88.

Conclusion

The described method yields probability estimates for local protein structure candidates, giving signals for all kinds of local structure. These local structure predictions can be incorporated either into fold recognition algorithms to improve alignment quality and the overall prediction accuracy or into new fold prediction methods.


URL for the Abstract:

http://www.biomedcentral.com/1471-2105/7/14/abstract

Categories / Keywords:


HyperLinks / References / URLs:


Copyright Message:


Personal Comments:


Download
Access Level:

Intranet

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Computational Biology and Applied Algorithmics

Appearance:

MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@MISC{Sander2006,
AUTHOR = {Sander, Oliver and Sommer, Ingolf and Lengauer, Thomas},
TITLE = {Local protein structure prediction using discriminative models},
JOURNAL = {BMC Bioinformatics},
PUBLISHER = {BioMed Central},
YEAR = {2006},
NUMBER = {14},
VOLUME = {7},
PAGES = {1--13},
ADDRESS = {London, UK},
MONTH = {January},
ISBN = {14712105},
}


Entry last modified by Christine Kiesel, 03/30/2007
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Editor(s)
Hongbo Zhu
Created
11/06/2006 03:26:38 PM
Revisions
5.
4.
3.
2.
1.
Editor(s)
Christine Kiesel
Christine Kiesel
Christine Kiesel
Hongbo Zhu
Hongbo Zhu
Edit Dates
30.03.2007 09:12:51
22.02.2007 14:28:46
22.02.2007 13:52:42
11/29/2006 01:28:55 PM
11/08/2006 10:40:07 AM