PREDICTING PROTEIN MUTANT ENERGETICS BY SELF-CONSISTENT ENSEMBLE OPTIMIZATION
PREDICTING PROTEIN MUTANT ENERGETICS BY SELF-CONSISTENT ENSEMBLE OPTIMIZATION
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DOI：
10.1006/jmbi.1994.1198
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发表时间：
1994-02-25
影响因子：
5.6
通讯作者：
LEE, C
中科院分区：
文献类型：
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作者：
LEE, C
在本文中，我们提出了一个自洽系综优化（SCEO）理论，有效的构象搜索，我们已经应用到预测突变对蛋白质热稳定性的影响。这种方法利用了统计机械自洽条件的优势，在迭代的全局最小结构。我们采用了一个快速的潜在的平均力近似削减计算时间为几分钟的一个典型的蛋白质突变，只有线性时间依赖于预测问题的大小。这种新方法不是寻求一个单一的、静态的最小能量结构，而是优化了许多构象的集合，试图预测在所需温度下最有可能的天然状态的集合。测试这种方法与一个简单的物理模型完全集中在空间相互作用和侧链重排，我们得到了强大的收敛预测的核心侧链构象，和疏水核心突变对蛋白质稳定性的影响。自洽集成优化算法在收敛速度和全局最小值方面上级模拟退火算法，且对初始构象不敏感。在λ阻遏蛋白的计算中，对一个八残基熔融区的结构预测具有侧链r.m.s.野生型蛋白的误差为0·49 μ g。随着这些突变阻遏物的结构得到解决，对该方法的突变结构预测的评估应该成为可能。预测的一系列9个疏水核心突变体的能量与测得的展开自由能的系数为0·82。
In this paper we present a self-consistent ensemble optimization (SCEO) theory for efficient conformational search, which we have applied to predicting the effects of mutations on protein thermostability. This approach takes advantage of a statistical mechanical self-consistency condition to home in iteratively on the global minimum structure. We employ a fast potential of mean-force approximation to cut computation time to a few minutes for a typical protein mutation, with only linear time-dependence on the size of the prediction problem. Rather than seeking a single, static structure of minimum energy, the new method optimizes an ensemble of many conformations, seeking to predict the most likely ensemble for the native state at a desired temperature. Testing this approach with a simple physical model focusing entirely on steric interactions and side-chain rearrangement, we obtain robustly convergent prediction of core side-chain conformation, and of hydrophobic core mutations' effect on protein stability. Self-consistent ensemble optimization is superior to simulated annealing in its speed and convergence to the global minimum, and insensitive to starting conformation. In calculations of λ repressor protein, structural predictions for an eight-residue molten-zone had side-chain r.m.s. error of 0·49 Å for the wild-type protein. Evaluation of the method's mutant structure predictions should become possible, as structure of these mutant repressors are solved. Predicted energies for a series of nine hydrophobic core mutants correlated with measured free energies of unfolding with a coefficient of 0·82.