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用于低分辨率大分子晶体学的可变形弹性网络细化

Deformable elastic network refinement for low-resolution macromolecular crystallography.

作者信息

Schröder Gunnar F, Levitt Michael, Brunger Axel T

机构信息

Institute of Complex Systems (ICS-6), Forschungszentrum Jülich, 52425 Jülich, Germany.

Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA.

出版信息

Acta Crystallogr D Biol Crystallogr. 2014 Sep;70(Pt 9):2241-55. doi: 10.1107/S1399004714016496. Epub 2014 Aug 29.

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.

摘要

由于膜蛋白和蛋白质复合物固有的分子柔性、异质性或微结构域的镶嵌分布,其晶体通常只能衍射到低分辨率。在低分辨率下,构建和优化原子模型是一项更具挑战性的任务。先前开发的可变形弹性网络(DEN)优化方法在确定多个低分辨率结构方面发挥了重要作用。本文对DEN优化方法进行了综述,提供了其最佳使用建议,并讨论了其局限性。还展示了DEN优化方法应用于低分辨率下具有挑战性的优化案例的代表性实例。这些案例包括在3至7埃的极限分辨率下测定的可溶性蛋白和膜蛋白。还讨论了DEN优化技术的潜在扩展以及低分辨率晶体结构解析的未来前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a09d/4157441/608a78c3abbe/d-70-02241-fig1.jpg

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