Lu Chih-Hao, Huang Shao-Wei, Lai Yan-Long, Lin Chih-Peng, Shih Chien-Hua, Huang Cuen-Chao, Hsu Wei-Lun, Hwang Jenn-Kang
Institute of Bioinformatics, National Chiao Tung University, HsinChu 30050, Taiwan, Republic of China.
Proteins. 2008 Aug;72(2):625-34. doi: 10.1002/prot.21954.
Recently, we have developed a method (Shih et al., Proteins: Structure, Function, and Bioinformatics 2007;68: 34-38) to compute correlation of fluctuations of proteins. This method, referred to as the protein fixed-point (PFP) model, is based on the positional vectors of atoms issuing from the fixed point, which is the point of the least fluctuations in proteins. One corollary from this model is that atoms lying on the same shell centered at the fixed point will have the same thermal fluctuations. In practice, this model provides a convenient way to compute the average dynamical properties of proteins directly from the geometrical shapes of proteins without the need of any mechanical models, and hence no trajectory integration or sophisticated matrix operations are needed. As a result, it is more efficient than molecular dynamics simulation or normal mode analysis. Though in the previous study the PFP model has been successfully applied to a number of proteins of various folds, it is not clear to what extent this model will be applied. In this article, we have carried out the comprehensive analysis of the PFP model for a dataset comprising 972 high-resolution X-ray structures with pairwise sequence identity <or=25%. We found that in most cases the PFP model works well. However, in case of proteins comprising multiple domains, each domain should be treated separately as an independent dynamical module with its own fixed point; and in case of the protein complex comprising a number of subunits, if functioning as a biological unit, the whole complex should be considered as one single dynamical module with one fixed point. Under such considerations, the resultant correlation coefficient between the computed and the X-ray structural B-factors for the data set is 0.59 and 75% (727/972) of proteins with a correlation coefficient >or=0.5. Our result shows that the fixed-point model is indeed quite general and will be a useful tool for high throughput analysis of dynamical properties of proteins.
最近,我们开发了一种方法(施等人,《蛋白质:结构、功能与生物信息学》2007年;68:34 - 38)来计算蛋白质波动的相关性。这种方法被称为蛋白质定点(PFP)模型,它基于从定点发出的原子位置向量,该定点是蛋白质中波动最小的点。这个模型的一个推论是,位于以定点为中心的同一壳层上的原子将具有相同的热波动。在实际应用中,该模型提供了一种直接从蛋白质的几何形状计算蛋白质平均动力学性质的便捷方法,无需任何力学模型,因此无需轨迹积分或复杂的矩阵运算。结果,它比分子动力学模拟或正常模式分析更高效。尽管在先前的研究中,PFP模型已成功应用于多种折叠类型的许多蛋白质,但该模型的适用范围尚不清楚。在本文中,我们对一个包含972个高分辨率X射线结构且两两序列同一性≤25%的数据集进行了PFP模型的综合分析。我们发现,在大多数情况下,PFP模型效果良好。然而,对于包含多个结构域的蛋白质,每个结构域应作为一个具有自身定点的独立动力学模块分别处理;对于包含多个亚基的蛋白质复合物,如果其作为一个生物单元发挥作用,则整个复合物应被视为一个具有一个定点的单一动力学模块。基于这些考虑,该数据集计算得到的与X射线结构B因子之间的相关系数为0.59,75%(727/972)的蛋白质相关系数≥0.5。我们的结果表明,定点模型确实具有相当的通用性,将成为蛋白质动力学性质高通量分析的有用工具。