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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.
