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. 2013 Nov;69(Pt 11):2202-8.
doi: 10.1107/S0907444913023305. Epub 2013 Oct 12.

Advances in Rosetta structure prediction for difficult molecular-replacement problems

Frank DiMaio  1
Affiliations

Advances in Rosetta structure prediction for difficult molecular-replacement problems

Frank DiMaio. Acta Crystallogr D Biol Crystallogr. 2013 Nov.

Abstract

Recent work has shown the effectiveness of structure-prediction methods in solving difficult molecular-replacement problems. The Rosetta protein structure modeling suite can aid in the solution of difficult molecular-replacement problems using templates from 15 to 25% sequence identity; Rosetta refinement guided by noisy density has consistently led to solved structures where other methods fail. In this paper, an overview of the use of Rosetta for these difficult molecular-replacement problems is provided and new modeling developments that further improve model quality are described. Several variations to the method are introduced that significantly reduce the time needed to generate a model and the sampling required to improve the starting template. The improvements are benchmarked on a set of nine difficult cases and it is shown that this improved method obtains consistently better models in less running time. Finally, strategies for best using Rosetta to solve difficult molecular-replacement problems are presented and future directions for the role of structure-prediction methods in crystallography are discussed.

Keywords: model building; molecular replacement; structure prediction.

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Figures

Figure 1
Figure 1
(a) An overview of the approach used by MR-Rosetta to refine models against noisy density data resulting from difficult molecular-replacement problems. In addition to identifying the correct solution from among a list of candidates, MR-Rosetta is often able to improve the model enough so that automatic chain tracing can solve (or very nearly solve) the structure. (b) How Rosetta combines sequence information to guide backbone sampling with energetics and experimental data during refinement.
Figure 2
Figure 2
(a) An overview of model rebuilding in our previous approach. (b) In our new approach, model rebuilding is interspersed with minimization moves, which allow deviations from the template to accommodate the new fragment. (c) A brief example of how our improved model building may handle small sequence misalignments. The aligned template (top; cyan) places insertions within a β-strand pairing (native in black). Our previous approach (middle; yellow) breaks the strand pairing. In our new protocol (bottom; magenta), by refining the template backbone during rebuilding the strand pairing is kept intact.
Figure 3
Figure 3
A comparison of the previous and new model-building approaches in MR-Rosetta. Plots show the density correlation between models and the 2mF o − DF c density from the final refined structure, where the models are either the template, the MR-Rosetta model using the previous model-building approach or the MR-Rosetta model using the new model-building approach. The left plot compares the average model quality, while the right plot shows the selected model quality. Both plots show correlations after one round of model building without reciprocal-space refinement. While most cases show similar performance, there are three cases in which off-template movement allows more accurate model rebuilding.
Figure 4
Figure 4
An example illustrating the improvements allowed by the new model-building approach. The template model, indicated in black, has a loop whose conformation is changed in the final model. The previous model-building approach (in red) was unable to move this loop. Our new approach (in green) correctly rebuilds this region, giving better agreement with the final structure (PDB entry 2y92, shown in blue; Valkov et al., 2011 ▶).
Figure 5
Figure 5
A plot illustrating the role of sampling in improving input models. The correlation of the selected model is plotted as a function of the number of models generated. This plot shows that with the algorithmic improvements 5–10 models are sufficient to see most of the improvement of a large-scale run.
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