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. 2016 Mar;72(Pt 3):440-5.
doi: 10.1107/S2059798315022482. Epub 2016 Mar 1.

Initiating Heavy-Atom-Based Phasing by Multi-Dimensional Molecular Replacement

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Initiating Heavy-Atom-Based Phasing by Multi-Dimensional Molecular Replacement

Bjørn Panyella Pedersen et al. Acta Crystallogr D Struct Biol. .
Free PMC article

Abstract

To obtain an electron-density map from a macromolecular crystal the phase problem needs to be solved, which often involves the use of heavy-atom derivative crystals and concomitant heavy-atom substructure determination. This is typically performed by dual-space methods, direct methods or Patterson-based approaches, which however may fail when only poorly diffracting derivative crystals are available. This is often the case for, for example, membrane proteins. Here, an approach for heavy-atom site identification based on a molecular-replacement parameter matrix (MRPM) is presented. It involves an n-dimensional search to test a wide spectrum of molecular-replacement parameters, such as different data sets and search models with different conformations. Results are scored by the ability to identify heavy-atom positions from anomalous difference Fourier maps. The strategy was successfully applied in the determination of a membrane-protein structure, the copper-transporting P-type ATPase CopA, when other methods had failed to determine the heavy-atom substructure. MRPM is well suited to proteins undergoing large conformational changes where multiple search models should be considered, and it enables the identification of weak but correct molecular-replacement solutions with maximum contrast to prime experimental phasing efforts.

Keywords: experimental phasing; heavy-atom substructure; molecular replacement.

Figures

Figure 1
Figure 1
Overview of the MRPM search strategy. Prerun considerations (top green box) have to be made to identify parameters (dimensions) and sets of values to test for each parameter. The parameters and set size for each parameter shown here are specific for the CopA case. After each MR and FFT calculation, the result is plotted on a two-dimensional plot to identify clusters of MR solutions that both have a high Z-score and generate large difference peaks in the Pt-derivative data set.
Figure 2
Figure 2
Two-dimensional plot of the result of the MR parameter search. All solutions are plotted as a function of Z-score and corresponding highest difference peak in the Pt-derivative data set. The grey area highlights the MR solutions that turned out to be identical and correct. (a) High-resolution cutoff. (b) Data set used. (c) Scaffold used. The PDB code is noted. (d) Truncation and pruning used.

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