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 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.


CONCLUSIONS
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.
Sommer
Ingolf
I
Department of Computational Biology and Applied Algorithmics, Max-Planck-lnstitute for Informatics, Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. sommer@mpi-sb.mpg.de
Toppo
Stefano
S
Sander
Oliver
O
Lengauer
Thomas
T
Tosatto
Silvio C E
SC
D003198:Computer Simulation; D030562:Databases, Protein; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein
D011506:Proteins