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Improving the quality of protein structure models by selecting from alignment alternatives

BACKGROUND: In the area of protein structure prediction, recently a lot of effort has gone into the development of Model Quality Assessment Programs (MQAPs). MQAPs distinguish high quality protein structure models from inferior models. Here, we propose a new method to use an MQAP to improve the qual...

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Autores principales: Sommer, Ingolf, Toppo, Stefano, Sander, Oliver, Lengauer, Thomas, Tosatto, Silvio CE
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1579234/
https://www.ncbi.nlm.nih.gov/pubmed/16872519
http://dx.doi.org/10.1186/1471-2105-7-364
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author Sommer, Ingolf
Toppo, Stefano
Sander, Oliver
Lengauer, Thomas
Tosatto, Silvio CE
author_facet Sommer, Ingolf
Toppo, Stefano
Sander, Oliver
Lengauer, Thomas
Tosatto, Silvio CE
author_sort Sommer, Ingolf
collection PubMed
description BACKGROUND: In the area of protein structure prediction, recently a lot of effort has gone into the development of Model Quality Assessment Programs (MQAPs). MQAPs distinguish high quality protein structure models from inferior models. Here, we propose a new method to use an MQAP to improve the quality of models. With a given target sequence and template structure, we construct a number of different alignments and corresponding models for the sequence. The quality of these models is scored with an MQAP and used to choose the most promising model. An SVM-based selection scheme is suggested for combining MQAP partial potentials, in order to optimize for improved model selection. RESULTS: The approach has been tested on a representative set of proteins. The ability of the method to improve models was validated by comparing the MQAP-selected structures to the native structures with the model quality evaluation program TM-score. Using the SVM-based model selection, a significant increase in model quality is obtained (as shown with a Wilcoxon signed rank test yielding p-values below 10(-15)). The average increase in TMscore is 0.016, the maximum observed increase in TM-score is 0.29. CONCLUSION: In template-based protein structure prediction alignment is known to be a bottleneck limiting the overall model quality. Here we show that a combination of systematic alignment variation and modern model scoring functions can significantly improve the quality of alignment-based models.
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spelling pubmed-15792342006-10-02 Improving the quality of protein structure models by selecting from alignment alternatives Sommer, Ingolf Toppo, Stefano Sander, Oliver Lengauer, Thomas Tosatto, Silvio CE BMC Bioinformatics Methodology Article BACKGROUND: In the area of protein structure prediction, recently a lot of effort has gone into the development of Model Quality Assessment Programs (MQAPs). MQAPs distinguish high quality protein structure models from inferior models. Here, we propose a new method to use an MQAP to improve the quality of models. With a given target sequence and template structure, we construct a number of different alignments and corresponding models for the sequence. The quality of these models is scored with an MQAP and used to choose the most promising model. An SVM-based selection scheme is suggested for combining MQAP partial potentials, in order to optimize for improved model selection. RESULTS: The approach has been tested on a representative set of proteins. The ability of the method to improve models was validated by comparing the MQAP-selected structures to the native structures with the model quality evaluation program TM-score. Using the SVM-based model selection, a significant increase in model quality is obtained (as shown with a Wilcoxon signed rank test yielding p-values below 10(-15)). The average increase in TMscore is 0.016, the maximum observed increase in TM-score is 0.29. CONCLUSION: In template-based protein structure prediction alignment is known to be a bottleneck limiting the overall model quality. Here we show that a combination of systematic alignment variation and modern model scoring functions can significantly improve the quality of alignment-based models. BioMed Central 2006-07-27 /pmc/articles/PMC1579234/ /pubmed/16872519 http://dx.doi.org/10.1186/1471-2105-7-364 Text en Copyright © 2006 Sommer et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Sommer, Ingolf
Toppo, Stefano
Sander, Oliver
Lengauer, Thomas
Tosatto, Silvio CE
Improving the quality of protein structure models by selecting from alignment alternatives
title Improving the quality of protein structure models by selecting from alignment alternatives
title_full Improving the quality of protein structure models by selecting from alignment alternatives
title_fullStr Improving the quality of protein structure models by selecting from alignment alternatives
title_full_unstemmed Improving the quality of protein structure models by selecting from alignment alternatives
title_short Improving the quality of protein structure models by selecting from alignment alternatives
title_sort improving the quality of protein structure models by selecting from alignment alternatives
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1579234/
https://www.ncbi.nlm.nih.gov/pubmed/16872519
http://dx.doi.org/10.1186/1471-2105-7-364
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