Improving consensus structure by eliminating averaging artifacts.
Improving consensus structure by eliminating averaging artifacts.
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DOI：
10.1186/1472-6807-9-12
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发表时间：
2009-03-06
影响因子：
--
通讯作者：
Dukka BK
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文献类型：
--
作者：
Dukka BK
常见的结构生物学方法（即核磁共振和分子动力学）通常会产生分子结构的集合。因此，对分子结构（蛋白质和 RNA）的 3D 坐标进行平均是获得代表整体的一致结构的常用方法。然而，当结构被平均时，伪影可能会导致不切实际的局部几何形状，包括非物理键长和角度。在这里，我们描述了一种在限制工件数量的同时导出代表性结构的方法。我们的方法基于蒙特卡罗模拟技术，该技术使用谐波伪能量函数将起始结构（扩展或“附近”结构）驱动到“平均结构”。为了评估算法的性能，我们将我们的方法应用于 TASSER 结构预测算法生成的 1364 种蛋白质的 Cα 模型。与来自原生结构的平均结构的平均 RMSD（精炼结构为 3.28 Å，平均结构为 3.36 A）相比，该集合的原生结构的精炼模型的平均 RMSD 仅差了 0.08 Å。然而，参与碰撞的原子百分比大大减少（从63%减少到1%）；事实上，大多数精制蛋白质的冲突为零。此外，与平均结构相比，少量（38）个精细结构导致天然蛋白质的 RMSD 较低。最后，与 PULCHRA 相比，我们的方法产生了具有相似 RMSD 质量的代表性结构，但冲突少得多。基准测试结果表明，我们消除平均伪影的方法对结构生物学界非常有益。此外，相同的方法可以应用于几乎任何执行 3D 坐标平均的问题。也就是说，结构平均也常用于 RNA 二次预测，这也可以从我们的方法中受益。
Common structural biology methods (i.e., NMR and molecular dynamics) often produce ensembles of molecular structures. Consequently, averaging of 3D coordinates of molecular structures (proteins and RNA) is a frequent approach to obtain a consensus structure that is representative of the ensemble. However, when the structures are averaged, artifacts can result in unrealistic local geometries, including unphysical bond lengths and angles. Herein, we describe a method to derive representative structures while limiting the number of artifacts. Our approach is based on a Monte Carlo simulation technique that drives a starting structure (an extended or a 'close-by' structure) towards the 'averaged structure' using a harmonic pseudo energy function. To assess the performance of the algorithm, we applied our approach to Cα models of 1364 proteins generated by the TASSER structure prediction algorithm. The average RMSD of the refined model from the native structure for the set becomes worse by a mere 0.08 Å compared to the average RMSD of the averaged structures from the native structure (3.28 Å for refined structures and 3.36 A for the averaged structures). However, the percentage of atoms involved in clashes is greatly reduced (from 63% to 1%); in fact, the majority of the refined proteins had zero clashes. Moreover, a small number (38) of refined structures resulted in lower RMSD to the native protein versus the averaged structure. Finally, compared to PULCHRA, our approach produces representative structure of similar RMSD quality, but with much fewer clashes. The benchmarking results demonstrate that our approach for removing averaging artifacts can be very beneficial for the structural biology community. Furthermore, the same approach can be applied to almost any problem where averaging of 3D coordinates is performed. Namely, structure averaging is also commonly performed in RNA secondary prediction, which could also benefit from our approach.
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