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Author, Editor(s)

Author(s):

Bock, Christoph
Hesser, Jürgen

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dblp

Not MPG Author(s):

Hesser, Jürgen

BibTeX cite key*:

Bock2006a

Title

Title*:

Analysis and Prediction of Helix Shift Errors in Homology Modeling

Journal

Journal Title*:

In Silico Biology

Journal's URL:


Download URL
for the article:

http://www.bioinfo.de/isb/2006/06/0013/

Language:

English

Publisher

Publisher's
Name:

IOS

Publisher's URL:


Publisher's
Address:


ISSN:


Vol, No, pp, Date

Volume*:

6

Number:

1-2

Publishing Date:

2006

Pages*:

131-145

Number of
VG Pages:


Page Start:


Page End:


Sequence Number:


DOI:


Note, Abstract, ©

Note:


(LaTeX) Abstract:

High sequence identity between two proteins (e.g. > 60%) is a strong evidence for high structural similarity. However, internal shifts in one of the two proteins can sometimes give rise to unexpectedly high structural differences. This, in turn, causes unreliable structure predictions when two such proteins are used in homology modeling. Here, we perform a computational analysis of helix shifts and we show that their occurrence can be predicted with statistical learning methods.
Our results indicate that helix shifts increase the RMS error by factor 2.6 compared to those protein pairs without a helix shift. Although helix shifts are rare (1.6% of helices and a commensurately higher number of proteins are affected), they therefore pose a significant problem for reliable structure prediction systems. In this paper, we prototype a new approach for model quality assessment and demonstrate that it can successfully warn against helix shifts. A support vector machine trained on a wide range of sequence and structure properties predicts the occurrence of helix shifts with a sensitivity of 74.2% and a specificity of 83.6%. On an equalized test dataset, this corresponds to an accuracy of 78.9%. Projected to the full dataset, it translates to an accuracy of 83.4%.
Our analysis shows that helix shift detection is a valuable building block for highly reliable structure prediction systems. Furthermore, the statistical learning based approach to helix shift detection that we employ here is orthogonal to well-established model quality assessment methods (which use geometric constraint checking or mean force potentials). Therefore, a further increase of prediction accuracy is expected from the combination of these methods.

URL for the Abstract:


Categories,
Keywords:

comparative modeling, homology modeling, error sources, MQAP, helix movement, secondary structure

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Access Level:

Internal

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Computational Biology and Applied Algorithmics

Audience:

Expert

Appearance:

MPG publications list, VG Wort


BibTeX Entry:

@ARTICLE{Bock2006a,
AUTHOR = {Bock, Christoph and Hesser, J{\"u}rgen},
TITLE = {Analysis and Prediction of Helix Shift Errors in Homology Modeling},
JOURNAL = {In Silico Biology},
PUBLISHER = {IOS},
YEAR = {2006},
NUMBER = {1-2},
VOLUME = {6},
PAGES = {131--145},
}


Entry last modified by Uwe Brahm, 07/18/2007
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Editor(s)
Christine Kiesel
Created
09/19/2006 01:41:07 PM
Revisions
6.
5.
4.
3.
2.
Editor(s)
Uwe Brahm
Uwe Brahm
Christine Kiesel
Christoph Bock
Christoph Bock
Edit Dates
2007-07-18 17:58:36
2007-07-02 12:03:48
20.02.2007 15:37:10
08/11/2006 10:18:09
07/11/2006 19:09:30