Dataset Information

Deformable elastic network refinement for low-resolution macromolecular crystallography.

ABSTRACT: Crystals of membrane proteins and protein complexes often diffract to low resolution owing to their intrinsic molecular flexibility, heterogeneity or the mosaic spread of micro-domains. At low resolution, the building and refinement of atomic models is a more challenging task. The deformable elastic network (DEN) refinement method developed previously has been instrumental in the determinion of several structures at low resolution. Here, DEN refinement is reviewed, recommendations for its optimal usage are provided and its limitations are discussed. Representative examples of the application of DEN refinement to challenging cases of refinement at low resolution are presented. These cases include soluble as well as membrane proteins determined at limiting resolutions ranging from 3 to 7?Å. Potential extensions of the DEN refinement technique and future perspectives for the interpretation of low-resolution crystal structures are also discussed.

SUBMITTER: Schroder GF

PROVIDER: S-EPMC4157441 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Deformable elastic network refinement for low-resolution macromolecular crystallography.

Schröder Gunnar F GF Levitt Michael M Brunger Axel T AT

Acta crystallographica. Section D, Biological crystallography 20140829 Pt 9

Crystals of membrane proteins and protein complexes often diffract to low resolution owing to their intrinsic molecular flexibility, heterogeneity or the mosaic spread of micro-domains. At low resolution, the building and refinement of atomic models is a more challenging task. The deformable elastic network (DEN) refinement method developed previously has been instrumental in the determinion of several structures at low resolution. Here, DEN refinement is reviewed, recommendations for its optima ...[more]

PMID: 25195739

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Project description:X-ray diffraction plays a pivotal role in the understanding of biological systems by revealing atomic structures of proteins, nucleic acids and their complexes, with much recent interest in very large assemblies like the ribosome. As crystals of such large assemblies often diffract weakly (resolution worse than 4 A), we need methods that work at such low resolution. In macromolecular assemblies, some of the components may be known at high resolution, whereas others are unknown: current refinement methods fail as they require a high-resolution starting structure for the entire complex. Determining the structure of such complexes, which are often of key biological importance, should be possible in principle as the number of independent diffraction intensities at a resolution better than 5 A generally exceeds the number of degrees of freedom. Here we introduce a method that adds specific information from known homologous structures but allows global and local deformations of these homology models. Our approach uses the observation that local protein structure tends to be conserved as sequence and function evolve. Cross-validation with R(free) (the free R-factor) determines the optimum deformation and influence of the homology model. For test cases at 3.5-5 A resolution with known structures at high resolution, our method gives significant improvements over conventional refinement in the model as monitored by coordinate accuracy, the definition of secondary structure and the quality of electron density maps. For re-refinements of a representative set of 19 low-resolution crystal structures from the Protein Data Bank, we find similar improvements. Thus, a structure derived from low-resolution diffraction data can have quality similar to a high-resolution structure. Our method is applicable to the study of weakly diffracting crystals using X-ray micro-diffraction as well as data from new X-ray light sources. Use of homology information is not restricted to X-ray crystallography and cryo-electron microscopy: as optical imaging advances to subnanometre resolution, it can use similar tools.

Dataset Information

Deformable elastic network refinement for low-resolution macromolecular crystallography.

Publications

Deformable elastic network refinement for low-resolution macromolecular crystallography.

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