Beck David A C, Daggett Valerie
Department of Bioengineering, University of Washington, Seattle, Washington 98195-5061, USA.
Biophys J. 2007 Nov 15;93(10):3382-91. doi: 10.1529/biophysj.106.100149.
A properly identified transition state ensemble (TSE) in a molecular dynamics (MD) simulation can reveal a tremendous amount about how a protein folds and offer a point of comparison to experimentally derived Phi(F) values, which reflect the degree of structure in these transient states. In one such method of TSE identification, dubbed P(fold), MD simulations of individual protein structures taken from an unfolding trajectory are used to directly assess an input structure's probability of folding before unfolding, and P(fold) is, by definition, 0.5 for the TSE. Other, less computationally intensive methods, such as multidimensional scaling (MDS) of the pairwise root mean-squared deviation (RMSD) matrix of the conformations sampled in a thermal unfolding trajectory, have also been used to identify the TSE. Identification of the TSE is made from the original MD simulation without the need to run further simulations. Here we present a P(fold)-like study and describe methods for identification of the TSE through the derivation of a high fidelity, bounded, one-dimensional reaction coordinate for protein folding. These methods are applied to the engrailed homeodomain. The TSE identified by this approach is essentially identical to the TSE identified previously by MDS of the pairwise RMSD matrix. However, the cost of performing P(fold), or even our reduced P(fold)-like calculations, is at least 36,000 times greater than the MDS method.
在分子动力学(MD)模拟中,正确识别的过渡态系综(TSE)可以揭示关于蛋白质折叠方式的大量信息,并提供与实验得出的Phi(F)值的比较点,Phi(F)值反映了这些瞬态中的结构程度。在一种被称为P(fold)的TSE识别方法中,从展开轨迹中选取的单个蛋白质结构的MD模拟被用于直接评估输入结构在展开前折叠的概率,根据定义,TSE的P(fold)为0.5。其他计算强度较小的方法,如对热展开轨迹中采样的构象的成对均方根偏差(RMSD)矩阵进行多维缩放(MDS),也已被用于识别TSE。TSE的识别是基于原始的MD模拟,无需进一步运行模拟。在这里,我们展示了一项类似P(fold)的研究,并描述了通过推导用于蛋白质折叠的高保真、有界的一维反应坐标来识别TSE的方法。这些方法应用于engrailed同源结构域。通过这种方法识别的TSE与先前通过成对RMSD矩阵的MDS识别的TSE基本相同。然而,执行P(fold)甚至我们简化的类似P(fold)计算的成本至少比MDS方法高36000倍。