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Modeling Protein Conformational Changes by Iterative Fitting of Distance Constraints Using Reoriented Normal Modes

机译:使用重定向的法向模式通过距离约束的迭代拟合对蛋白质构象变化进行建模

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摘要

Recently we have developed a normal-modes-based algorithm that predicts the direction of protein conformational changes given the initial state crystal structure together with a small number of pairwise distance constraints for the end state. Here we significantly extend this method to accurately model both the direction and amplitude of protein conformational changes. The new protocol implements a multisteps search in the conformational space that is driven by iteratively minimizing the error of fitting the given distance constraints and simultaneously enforcing the restraint of low elastic energy. At each step, an incremental structural displacement is computed as a linear combination of the lowest 10 normal modes derived from an elastic network model, whose eigenvectors are reorientated to correct for the distortions caused by the structural displacements in the previous steps. We test this method on a list of 16 pairs of protein structures for which relatively large conformational changes are observed (root mean square deviation >3 Å), using up to 10 pairwise distance constraints selected by a fluctuation analysis of the initial state structures. This method has achieved a near-optimal performance in almost all cases, and in many cases the final structural models lie within root mean square deviation of 1 ∼ 2 Å from the native end state structures.
机译:最近,我们已经开发了一种基于正常模式的算法,该算法在给定初始状态晶体结构以及少数成对距离约束条件下,预测蛋白质构象变化的方向。在这里,我们大大扩展了此方法,以准确地建模蛋白质构象变化的方向和幅度。新协议在构象空间中实现了多步搜索,该搜索是通过迭代地最小化拟合给定距离约束的错误并同时加强对低弹性能量的约束来驱动的。在每个步骤中,将增量结构位移计算为从弹性网络模型导出的最低10个法线模式的线性组合,其特征向量经过重新定向以校正在先前步骤中由结构位移引起的变形。我们使用多达10个通过初始状态结构的波动分析选择的成对距离约束条件,在观察到相对较大构象变化(均方根偏差> 3Å)的16对蛋白质结构列表上测试了该方法。这种方法几乎在所有情况下都达到了近乎最佳的性能,并且在许多情况下,最终的结构模型都位于与原始末端状态结构的均方根偏差1到2Å之内。

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