Simplified normal mode analysis of conformational transitions in DNA-dependent polymerases: the Elastic Network Model

Simplified normal mode analysis of conformational transitions in DNA-dependent polymerases: the Elastic Network Model
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DOI:
10.1016/s0022-2836(02)00562-4
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发表时间:
2002-07-26
影响因子:
5.6
通讯作者:
Sanejouand, YH
Sanejouand, YH
中科院分区:
生物学2区
文献类型:
--
作者:
Delarue, M;Sanejouand, YH

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弹性网络模型用于研究所有 DNA 依赖性聚合酶的开放/闭合转变,其结构以两种形式已知。对于每种结构,该模型都能很好地解释实验晶体学 B 因子。我们发现,在所有情况下,仅用少数正常模式就可以很好地描述过渡。通常,仅从开放形式推导的最低和/或次低频率简正模态产生计算出的位移矢量,其与两种形式之间观察到的差异矢量的相关系数大于0.50。对于可与实验结构数据进行直接比较的 DNA 依赖性聚合酶的每个结构类别都是如此。在 X 射线晶体学仅观察到一种形式的情况下,可以通过仔细检查最低频率简正模预测的矢量位移来预测溶液中可能存在的另一种形式。这种简单的模型具有计算成本低的优点,可用于设计针对聚合酶的新型药物,即防止细菌或病毒 DNA 依赖性聚合酶发生开放/闭合转变的药物。 (C) 2002 Elsevier Science Ltd. 保留所有权利。
The Elastic Network Model is used to investigate the open/closed transition in all DNA-dependent polymerases whose structure is known in both forms. For each structure the model accounts well for experimental crystallographic B-factors. It is found in all cases that the transition can be well described with just a handful of the normal modes. Usually, only the lowest and/or the second lowest frequency normal modes deduced from the open form give rise to calculated displacement vectors that have a correlation coefficient larger than 0.50 with the observed difference vectors between the two forms. This is true for every structural class of DNA-dependent polymerases where a direct comparison with experimental structural data is available. In cases where only one form has been observed by X-ray crystallography, it is possible to make predictions concerning the possible existence of another form in solution by carefully examining the vector displacements predicted for the lowest frequency normal modes. This simple model, which has the advantage to be computationally inexpensive, could be used to design novel kind of drugs directed against polymerases, namely drugs preventing the open/closed transition from occurring in bacterial or viral DNA-dependent polymerases. (C) 2002 Elsevier Science Ltd. All rights reserved.