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

Journal name:
Nature
Volume:
473,
Pages:
540–543
Date published:
DOI:
doi:10.1038/nature09964
Received
Accepted
Published online

Molecular replacement1, 2, 3, 4 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 methods5, 6, 7, 8, have allowed the rapid solution of large numbers of protein crystal structures. Despite extensive work9, 10, 11, 12, 13, 14, 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 3.2Å resolution, four or fewer copies in the asymmetric unit, and the availability of structures of homologous proteins with >20% sequence identity.

At a glance

Figures

  1. Examples of improvement in electron density and model quality.
    Figure 1: Examples of improvement in electron density and model quality.

    Each row corresponds to one of the entries in Table 1. First row: 6 (2.0Å resolution); second row: 7 (2.1Å resolution); third row: 12 (1.7Å 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 2mFoDFc density contoured at 1.5σ is shown in grey.

  2. Method comparison.
    Figure 2: Method comparison.

    a, Histogram of Rfree values after autobuilding for the eight difficult blind cases solved using the new approach (Table 1). For most existing approaches, none of the cases yielded Rfree values under 50%; DEN was able to reduce Rfree to 45–49% for three of the structures. For all eight cases, Rosetta energy and density guided structure optimization led to Rfree values under 40%. b, Dependence of success on sequence identity. The fraction of cases solved (Rfree 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 2mFoDFc 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 (Rfree<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.

  3. Comparison of the effectiveness of model diversification using Rosetta and simulated annealing.
    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 5,000K, simulated annealing with a start temperature of 50,000K, or Rosetta energy- and density-guided optimization. The correlation between 2mFoDFc 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.

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Author information

Affiliations

  1. University of Washington, Department of Biochemistry and HHMI, Seattle, Washington 98195, USA

    • Frank DiMaio &
    • David Baker
  2. Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

    • Thomas C. Terwilliger
  3. University of Cambridge, Department of Haematology, Cambridge Institute for Medical Research, Cambridge CB2 0XY, UK

    • Randy J. Read
  4. Macromolecular Crystallography Laboratory, National Cancer Institute at Frederick, Frederick, Maryland 21702, USA

    • Alexander Wlodawer
  5. Institute of Molecular Biosciences, University of Graz, Humboldtstrasse 50/3, 8010-Graz, Austria

    • Gustav Oberdorfer &
    • Ulrike Wagner
  6. University of Cambridge, Department of Biochemistry, Cambridge CB2 1GA, UK

    • Eugene Valkov
  7. Weizmann Institute of Science, Department of Structural Biology, Rehovot 76100, Israel

    • Assaf Alon &
    • Deborah Fass
  8. Joint Center for Structural Genomics and SSRL, SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA

    • Herbert L. Axelrod &
    • Debanu Das
  9. Northeast Structural Genomics Consortium, Columbia University, New York, New York 10027, USA

    • Sergey M. Vorobiev
  10. University of Helsinki, Institute of Biotechnology, FI-00014 Helsinki, Finland

    • Hideo Iwaï
  11. Argonne National Laboratory, Biosciences Division, Argonne, Illinois 60439, USA

    • P. Raj Pokkuluri

Contributions

F.D., T.C.T., R.J.R. and D.B. developed the methods described in the manuscript; F.D., T.C.T., R.J.R., A.W. and D.B wrote the paper. A.W., G.O., U.W., E.V., A.A., D.F., H.L.A., D.D., S.M.V., H.I. and P.R.P. provided the data and refined one or more structures to completion.

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The authors declare no competing financial interests.

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  1. Supplementary Information (1.6M)

    The file contains Supplementary Text, additional references, Supplementary Tables 1-5 and Supplementary Figures 1-3 with legends.

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