Ahmed Aqeel, Gohlke Holger
Department of Biology and Computer Science, J. W. Goethe-University, Frankfurt, Germany.
Proteins. 2006 Jun 1;63(4):1038-51. doi: 10.1002/prot.20907.
The development of a two-step approach for multiscale modeling of macromolecular conformational changes is based on recent developments in rigidity and elastic network theory. In the first step, static properties of the macromolecule are determined by decomposing the molecule into rigid clusters by using the graph-theoretical approach FIRST and an all-atom representation of the protein. In this way, rigid clusters are not limited to consist of residues adjacent in sequence or secondary structure elements as in previous studies. Furthermore, flexible links between rigid clusters are identified and can be modeled as such subsequently. In the second step, dynamical properties of the molecule are revealed by the rotations-translations of blocks approach (RTB) using an elastic network model representation of the coarse-grained protein. In this step, only rigid body motions are allowed for rigid clusters, whereas links between them are treated as fully flexible. The approach was tested on a data set of 10 proteins that showed conformational changes on ligand binding. For efficiency, coarse-graining the protein results in a remarkable reduction of memory requirements and computational times by factors of 9 and 27 on average and up to 25 and 125, respectively. For accuracy, directions and magnitudes of motions predicted by our approach agree well with experimentally determined ones, despite embracing in extreme cases >50% of the protein into one rigid cluster. In fact, the results of our method are in general comparable with when no or a uniform coarse-graining is applied; and the results are superior if the movement is dominated by loop or fragment motions. This finding indicates that explicitly distinguishing between flexible and rigid regions is advantageous when using a simplified protein representation in the second step. Finally, motions of atoms in rigid clusters are also well predicted by our approach, which points to the need to consider mobile protein regions in addition to flexible ones when modeling correlated motions.
用于大分子构象变化多尺度建模的两步法的发展基于刚性和弹性网络理论的最新进展。第一步,通过首先使用图论方法并结合蛋白质的全原子表示将分子分解为刚性簇,来确定大分子的静态性质。通过这种方式,刚性簇并不局限于由序列相邻的残基或二级结构元件组成,这与之前的研究不同。此外,识别出刚性簇之间的柔性连接,并随后可以将其建模。第二步,使用粗粒度蛋白质的弹性网络模型表示,通过块旋转-平移方法(RTB)揭示分子的动态性质。在这一步中,仅允许刚性簇进行刚体运动,而它们之间的连接被视为完全柔性的。该方法在一组10种蛋白质的数据集上进行了测试,这些蛋白质在配体结合时表现出构象变化。为了提高效率,对蛋白质进行粗粒度处理平均可使内存需求和计算时间分别显著减少9倍和27倍,最多可减少25倍和125倍。为了保证准确性,尽管在极端情况下我们的方法将超过50%的蛋白质纳入一个刚性簇,但预测的运动方向和幅度与实验确定的结果吻合良好。实际上,我们方法的结果总体上与不进行粗粒度处理或进行均匀粗粒度处理时相当;如果运动由环或片段运动主导,结果则更优。这一发现表明,在第二步使用简化的蛋白质表示时,明确区分柔性区域和刚性区域是有利的。最后,我们的方法也能很好地预测刚性簇中原子的运动,这表明在对相关运动进行建模时,除了柔性区域外还需要考虑蛋白质的可移动区域。