Georgiev Ivelin, Keedy Daniel, Richardson Jane S, Richardson David C, Donald Bruce R
Department of Computer Science, Duke University, Durham, NC 27708, USA.
Bioinformatics. 2008 Jul 1;24(13):i196-204. doi: 10.1093/bioinformatics/btn169.
The Backrub is a small but kinematically efficient side-chain-coupled local backbone motion frequently observed in atomic-resolution crystal structures of proteins. A backrub shifts the C(alpha)-C(beta) orientation of a given side-chain by rigid-body dipeptide rotation plus smaller individual rotations of the two peptides, with virtually no change in the rest of the protein. Backrubs can therefore provide a biophysically realistic model of local backbone flexibility for structure-based protein design. Previously, however, backrub motions were applied via manual interactive model-building, so their incorporation into a protein design algorithm (a simultaneous search over mutation and backbone/side-chain conformation space) was infeasible.
We present a combinatorial search algorithm for protein design that incorporates an automated procedure for local backbone flexibility via backrub motions. We further derive a dead-end elimination (DEE)-based criterion for pruning candidate rotamers that, in contrast to previous DEE algorithms, is provably accurate with backrub motions. Our backrub-based algorithm successfully predicts alternate side-chain conformations from < or = 0.9 A resolution structures, confirming the suitability of the automated backrub procedure. Finally, the application of our algorithm to redesign two different proteins is shown to identify a large number of lower-energy conformations and mutation sequences that would have been ignored by a rigid-backbone model.
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“回擦”是一种在蛋白质的原子分辨率晶体结构中经常观察到的、规模较小但在运动学上高效的侧链耦合局部主链运动。一次“回擦”通过刚性二肽旋转以及两个肽段各自较小的旋转来改变给定侧链的Cα - Cβ取向，而蛋白质的其余部分几乎没有变化。因此，“回擦”可为基于结构的蛋白质设计提供一个生物物理上逼真的局部主链灵活性模型。然而，此前“回擦”运动是通过手动交互式模型构建来应用的，所以将其纳入蛋白质设计算法（对突变和主链/侧链构象空间进行同步搜索）是不可行的。
我们提出了一种用于蛋白质设计的组合搜索算法，该算法通过“回擦”运动纳入了一种用于局部主链灵活性的自动化程序。我们进一步推导了一种基于死端消除（DEE）的准则来修剪候选旋转异构体，与之前的DEE算法不同，该准则在“回擦”运动下被证明是准确的。我们基于“回擦”的算法成功地从分辨率小于或等于0.9 Å的结构中预测了替代侧链构象，证实了自动化“回擦”程序的适用性。最后，我们的算法应用于重新设计两种不同的蛋白质，结果表明它能识别出大量低能量构象和突变序列，而这些是刚性主链模型会忽略的。
可联系作者获取源代码。
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