Georgiev Ivelin, Lilien Ryan H, Donald Bruce R
Department of Computer Science, Duke University, Durham, NC, USA.
J Comput Chem. 2008 Jul 30;29(10):1527-42. doi: 10.1002/jcc.20909.
One of the main challenges for protein redesign is the efficient evaluation of a combinatorial number of candidate structures. The modeling of protein flexibility, typically by using a rotamer library of commonly-observed low-energy side-chain conformations, further increases the complexity of the redesign problem. A dominant algorithm for protein redesign is dead-end elimination (DEE), which prunes the majority of candidate conformations by eliminating rigid rotamers that provably are not part of the global minimum energy conformation (GMEC). The identified GMEC consists of rigid rotamers (i.e., rotamers that have not been energy-minimized) and is thus referred to as the rigid-GMEC. As a postprocessing step, the conformations that survive DEE may be energy-minimized. When energy minimization is performed after pruning with DEE, the combined protein design process becomes heuristic, and is no longer provably accurate: a conformation that is pruned using rigid-rotamer energies may subsequently minimize to a lower energy than the rigid-GMEC. That is, the rigid-GMEC and the conformation with the lowest energy among all energy-minimized conformations (the minimized-GMEC) are likely to be different. While the traditional DEE algorithm succeeds in not pruning rotamers that are part of the rigid-GMEC, it makes no guarantees regarding the identification of the minimized-GMEC. In this paper we derive a novel, provable, and efficient DEE-like algorithm, called minimized-DEE (MinDEE), that guarantees that rotamers belonging to the minimized-GMEC will not be pruned, while still pruning a combinatorial number of conformations. We show that MinDEE is useful not only in identifying the minimized-GMEC, but also as a filter in an ensemble-based scoring and search algorithm for protein redesign that exploits energy-minimized conformations. We compare our results both to our previous computational predictions of protein designs and to biological activity assays of predicted protein mutants. Our provable and efficient minimized-DEE algorithm is applicable in protein redesign, protein-ligand binding prediction, and computer-aided drug design.
蛋白质重新设计的主要挑战之一是对组合数量的候选结构进行有效评估。蛋白质柔性建模通常通过使用常见的低能侧链构象的旋转异构体库来进行，这进一步增加了重新设计问题的复杂性。一种主要的蛋白质重新设计算法是死端消除（DEE），它通过消除可证明不属于全局最小能量构象（GMEC）的刚性旋转异构体来修剪大多数候选构象。所确定的GMEC由刚性旋转异构体（即未进行能量最小化的旋转异构体）组成，因此被称为刚性GMEC。作为后处理步骤，在DEE中幸存的构象可以进行能量最小化。当在使用DEE进行修剪后进行能量最小化时，组合的蛋白质设计过程就变得具有启发性，并且不再能保证其准确性：使用刚性旋转异构体能量被修剪的构象随后可能会最小化到比刚性GMEC更低的能量。也就是说，刚性GMEC与所有能量最小化构象中能量最低的构象（最小化GMEC）可能不同。虽然传统的DEE算法成功地没有修剪属于刚性GMEC的旋转异构体，但它对于识别最小化GMEC并没有保证。在本文中，我们推导了一种新颖、可证明且高效的类似DEE的算法，称为最小化DEE（MinDEE），它保证属于最小化GMEC的旋转异构体不会被修剪，同时仍然修剪组合数量的构象。我们表明，MinDEE不仅在识别最小化GMEC方面有用，而且还可作为基于集合的评分和搜索算法中的一个过滤器，用于利用能量最小化构象的蛋白质重新设计。我们将我们的结果与我们之前对蛋白质设计的计算预测以及对预测的蛋白质突变体的生物活性测定进行了比较。我们可证明且高效的最小化DEE算法适用于蛋白质重新设计、蛋白质 - 配体结合预测和计算机辅助药物设计。
