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用于从大分子晶体学中的多个晶体中最优选择数据集的聚类程序。

Clustering procedures for the optimal selection of data sets from multiple crystals in macromolecular crystallography.

作者信息

Foadi James, Aller Pierre, Alguel Yilmaz, Cameron Alex, Axford Danny, Owen Robin L, Armour Wes, Waterman David G, Iwata So, Evans Gwyndaf

机构信息

Membrane Protein Laboratory, Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0DE, England.

出版信息

Acta Crystallogr D Biol Crystallogr. 2013 Aug;69(Pt 8):1617-32. doi: 10.1107/S0907444913012274. Epub 2013 Jul 20.

Abstract

The availability of intense microbeam macromolecular crystallography beamlines at third-generation synchrotron sources has enabled data collection and structure solution from microcrystals of <10 µm in size. The increased likelihood of severe radiation damage where microcrystals or particularly sensitive crystals are used forces crystallographers to acquire large numbers of data sets from many crystals of the same protein structure. The associated analysis and merging of multi-crystal data is currently a manual and time-consuming step. Here, a computer program, BLEND, that has been written to assist with and automate many of the steps in this process is described. It is demonstrated how BLEND has successfully been used in the solution of a novel membrane protein.

摘要

第三代同步辐射源上高强度微束大分子晶体学光束线的出现,使得能够从尺寸小于10 µm的微晶中收集数据并解析结构。在使用微晶或特别敏感的晶体时,严重辐射损伤的可能性增加,这迫使晶体学家从许多具有相同蛋白质结构的晶体中获取大量数据集。目前,多晶体数据的相关分析和合并是一个手动且耗时的步骤。本文描述了一个计算机程序BLEND,它被编写用于协助并自动化此过程中的许多步骤。展示了BLEND如何成功应用于一种新型膜蛋白的结构解析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9eb/3727331/44bc47aced82/d-69-01617-fig1.jpg

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