Methodology article

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

Ingolf Sommer¹ , Stefano Toppo² , Oliver Sander¹ , Thomas Lengauer¹ and Silvio CE Tosatto³

¹  Department of Computational Biology and Applied Algorithmics, Max-Planck-lnstitute for Informatics, Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany

²  Department of Biological Chemistry, University of Padova, via U. Bassi 58/b, 1-35121 Padova, Italy

³  Department of Biology and CRIBI Biotechnology Centre University of Padova, V.le G. Colombo 3, I-35131 Padova, Italy

author email corresponding author email

BMC Bioinformatics 2006, 7:364doi:10.1186/1471-2105-7-364

Published:	27 July 2006

Abstract

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.