Ming Dengming, Kong Yifei, Lambert Maxime A, Huang Zhong, Ma Jianpeng
Graduate Program of Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, One Baylor Plaza, BCM-125, Houston, TX 77030, USA.
Proc Natl Acad Sci U S A. 2002 Jun 25;99(13):8620-5. doi: 10.1073/pnas.082148899.
This paper reports a computational method, the quantized elastic deformational model, that can reliably describe the conformational flexibility of a protein in the absence of the amino acid sequence and atomic coordinates. The essence of this method lies in the fact that, in modeling the functionally important conformational changes such as domain movements, it is possible to abandon the traditional concepts of protein structure (bonds, angles, dihedrals, etc.) and treat the protein as an elastic object. The shape and mass distribution of the object are described by the electron density maps, at various resolutions, from methods such as x-ray diffraction or cryo-electron microscopy. The amplitudes and directionality of the elastic deformational modes of a protein, whose patterns match the biologically relevant conformational changes, can then be derived solely based on the electron density map. The method yields an accurate description of protein dynamics over a wide range of resolutions even as low as 15-20 A at which there is nearly no visually distinguishable internal structures. Therefore, this method dramatically enhances the capability of studying protein motions in structural biology. It is also expected to have ample applications in related fields such as bioinformatics, structural genomics, and proteomics, in which one's ability to extract functional information from the not-so-well-defined structural models is vitally important.
本文报道了一种计算方法——量化弹性变形模型,该模型能够在不依赖氨基酸序列和原子坐标的情况下,可靠地描述蛋白质的构象灵活性。这种方法的核心在于,在对诸如结构域运动等功能上重要的构象变化进行建模时,可以摒弃传统的蛋白质结构概念(键、角度、二面角等),并将蛋白质视为一个弹性物体。该物体的形状和质量分布由X射线衍射或冷冻电子显微镜等方法在不同分辨率下的电子密度图来描述。然后,仅基于电子密度图就可以推导出蛋白质弹性变形模式的幅度和方向性,其模式与生物学上相关的构象变化相匹配。即使在低至15 - 20埃的分辨率下,几乎没有视觉上可区分的内部结构,该方法也能在很宽的分辨率范围内准确描述蛋白质动力学。因此,这种方法极大地增强了结构生物学中研究蛋白质运动的能力。预计它在生物信息学、结构基因组学和蛋白质组学等相关领域也有广泛应用,在这些领域中,从定义不太明确的结构模型中提取功能信息的能力至关重要。