Improved molecular replacement by density- and energy-guided protein structure optimization

Abstract

Molecular replacement procedures, which search for placements of a starting model within the crystallographic unit cell that best account for the measured diffraction amplitudes, followed by automatic chain tracing methods, have allowed the rapid solution of large numbers of protein crystal structures. Despite extensive work, molecular replacement or the subsequent rebuilding usually fail with more divergent starting models based on remote homologues with less than 30% sequence identity. Here we show that this limitation can be substantially reduced by combining algorithms for protein structure modelling with those developed for crystallographic structure determination. An approach integrating Rosetta structure modelling with Autobuild chain tracing yielded high-resolution structures for 8 of 13 X-ray diffraction data sets that could not be solved in the laboratories of expert crystallographers and that remained unsolved after application of an extensive array of alternative approaches. We estimate that the new method should allow rapid structure determination without experimental phase information for over half the cases where current methods fail, given diffraction data sets of better than 3.2 Å resolution, four or fewer copies in the asymmetric unit, and the availability of structures of homologous proteins with >20% sequence identity.

Figure 1

Examples of improvement in electron density and model quality. First row: Table 1 #6 (2.0 Å resolution); second row: Table 1 #7 (2.1 Å resolution); third row: Table 1 #12 (1.7 Å resolution). Left column: correct initial molecular replacement solution (not necessarily identifiable at this stage) using starting model and corresponding density. Middle column: energy optimized model and corresponding density. Right column: model and density following automatic building using the energy optimized model as the source of phase information. The final deposited structure is shown in yellow in each panel; the initial model, energy optimized model, and model after chain rebuilding are in red, green, and blue respectively. The sigma-A weighted 2mFo-DFc density contoured at 1.5σ is shown in gray.

Figure 2

Method comparison. (A) Histogram of R_free values after autobuilding for the seven difficult blind cases solved using the new approach (Table 1, set B). For most existing approaches, none of the cases yielded R_free values under 50%; DEN was able to reduce R_free to 45–49% for three of the structures. For all seven cases, Rosetta energy and density guided structure optimization led to R_free values under 40%. (B) Dependence of success on sequence identity. The fraction of cases solved (R_free after Autobuilding < 40%) is shown as a function of template sequence identity over the 18 blind cases and 59 benchmark cases. The new method is a clear improvement below 28% sequence identity. (C) Dependence of structure determination success on initial map quality. Sigma-A weighted 2mFo-DFc density maps (contoured at 1.5σ) computed from benchmark set templates with divergence from the native structure increasing from left to right are shown in grey; the solved crystal structure is shown in yellow. The correlation with the native density is shown above each panel. The solid green bar indicates structures the new approach was able to solve (R_free<0.4); the red bar those that torsion-space refinement or DEN refinement is able to solve, and the purple bar those that can be solved directly using the template.

Figure 3

Comparison of the effectiveness of model diversification using Rosetta and simulated annealing. For blind case #6, 100 models were generated using either simulated annealing with a start temperature of 5000 K, simulated annealing with a start temperature of 50000 K, or Rosetta energy-and density-guided optimization. The correlation between 2mFo-DFc density maps computed from each structure and the final refined density was then computed; the starting model has a correlation of 0.29 and the distributions of the refined models are shown in the figure. Rosetta models have correlations better than the initial model much more often than simulated annealing.