Knowledge-based real-space explorations for low-resolution structure determination
Knowledge-based real-space explorations for low-resolution structure determination
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DOI：
10.1016/j.str.2006.06.014
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发表时间：
2006-08-01
期刊：
影响因子：
5.7
通讯作者：
Blundell, Tom L.
中科院分区：
文献类型：
--
作者：
Furnham, Nicholas;Dore, Andrew S.;Blundell, Tom L.
在X射线晶体学的低分辨率数据的准确和有效的解释变得越来越重要的结构倡议转向大型多蛋白质复合物。由于低分辨率电子密度图的信息含量低和解释模糊，仍然存在重大挑战。在这里，我们描述了一个半自动化的程序，采用了限制为基础的构象搜索算法，RAPPER，以产生一个起始模型的连接酶相互作用因子1在复杂的DNA连接酶IV的片段在低分辨率的结构测定。结合使用实验数据和先验知识！蛋白质结构的知识使我们不仅能够产生一个全原子模型，而且还能重新确认推断的序列登记处。这种方法提供了一种手段，从实验数据中快速提取有用的信息，否则将被丢弃，并考虑到在解释的不确定性-低分辨率数据的压倒一切的问题。
The accurate and effective interpretation of low-resolution data in X-ray crystallography is becoming increasingly important as structural initiatives turn toward large multiprotein complexes. Substantial challenges remain due to the poor information content and ambiguity in the interpretation of electron density maps at low resolution. Here, we describe a semiautomated procedure that employs a restraint-based conformational search algorithm, RAPPER, to produce a starting model for the structure determination of ligase interacting factor 1 in complex with a fragment of DNA ligase IV at low resolution. The combined use of experimental data and a prior! knowledge of protein structure enabled us not only to generate an all-atom model but also to reaffirm the inferred sequence registry. This approach provides a means to extract quickly from experimental data useful information that would otherwise be discarded and to take into account the uncertainty in the interpretation-an overriding issue for low-resolution data.