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

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.

Figure 1

Overview of model quality improvement with respect to the model difficulty. Left for PVS, right for PVH analogously. Each dot corresponds to a model where the x-coordinate is the TM-score of the corresponding default Arby model and the y-coordinate is the TM-score improvement with respect to this default model. Smoothed quantile lines are shown for the 10% (lower dashed), 50% (middle), 90% (upper dashed) quantiles of the models within a sliding window of size 0.15. Black lines represent all models, red lines represent the models selected using FRST, green lines represent the models selected using the SVM approach. For the smoothing evaluations are made at 1000 equidistant points and the resulting quantiles are smoothed with a lowess function (local linear scatter plot smoother). Interpretation: The TM-score of the Arby default gives an indication of how difficult it is to find the right template for a target. For the selection methods random, FRST, and SVM, this plot shows the potential improvement with respect to difficulty of the target. For PVH, more models are generated below default. For both PVS and PVH, the SVM selection performs better than FRST selection, and FRST performs better than random.

Figure 2

(Left) Average increase in TM-score, for ranges of difficulty. Targets are binned according to the TM-score of the default Arby model. Within each bin the average increase in quality qimis plotted. Bins are enumerated horizontally, the two outer bins were concatenated with their neighbors as each contained less than 100 target samples. Models are selected from PVS using the SVM. For comparison the average increase in quality obtained on this benchmark set by performing loop modeling is 0.003. (Right) Maximum increase in TM-score, for the same ranges of difficulty. The maximum increase in quality max qim within each bin is visualized as a line above the box representing the average increase (which is the same as on the left side, just the scale is different).