Improving the quality of protein structure models by selecting from alignment alternatives
<p>Abstract</p> <p>Background</p> <p>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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2006-07-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/7/364 |
| _version_ | 1811246329232359424 |
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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 | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>TM</it>-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<sup>-15</sup>). The average increase in <it>TM</it>score is 0.016, the maximum observed increase in <it>TM</it>-score is 0.29.</p> <p>Conclusion</p> <p>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.</p> |
| first_indexed | 2024-04-12T14:51:55Z |
| format | Article |
| id | doaj.art-a4b83b73bdfe41b8a31a9b550b939872 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T14:51:55Z |
| publishDate | 2006-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-a4b83b73bdfe41b8a31a9b550b9398722022-12-22T03:28:24ZengBMCBMC Bioinformatics1471-21052006-07-017136410.1186/1471-2105-7-364Improving the quality of protein structure models by selecting from alignment alternativesSommer IngolfToppo StefanoSander OliverLengauer ThomasTosatto Silvio CE<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>TM</it>-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<sup>-15</sup>). The average increase in <it>TM</it>score is 0.016, the maximum observed increase in <it>TM</it>-score is 0.29.</p> <p>Conclusion</p> <p>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.</p>http://www.biomedcentral.com/1471-2105/7/364 |
| spellingShingle | Sommer Ingolf Toppo Stefano Sander Oliver Lengauer Thomas Tosatto Silvio CE Improving the quality of protein structure models by selecting from alignment alternatives BMC Bioinformatics |
| 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 |
| url | http://www.biomedcentral.com/1471-2105/7/364 |
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