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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...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2006
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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. |
format | Text |
id | pubmed-1579234 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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