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

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45521

Sommer,  Ingolf
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45343

Sander,  Oliver
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44907

Lengauer,  Thomas
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45631

Tosatto,  Silvio
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Zitation

Sommer, I., Toppo, S., Sander, O., Lengauer, T., & Tosatto, S. (2006). Improving the quality of protein structure models by selecting from alignment alternatives. BMC Bioinformatics, 7, 1-11.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-2330-6
Zusammenfassung
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.