Taylor Richard D, Jewsbury Philip J, Essex Jonathan W
Department of Chemistry, University of Southampton, Highfield, Southampton, SO17 1BJ, UK.
J Comput Chem. 2003 Oct;24(13):1637-56. doi: 10.1002/jcc.10295.
The docking of flexible small molecule ligands to large flexible protein targets is addressed in this article using a two-stage simulation-based method. The methodology presented is a hybrid approach where the first component is a dock of the ligand to the protein binding site, based on deriving sets of simultaneously satisfied intermolecular hydrogen bonds using graph theory and a recursive distance geometry algorithm. The output structures are reduced in number by cluster analysis based on distance similarities. These structures are submitted to a modified Monte Carlo algorithm using the AMBER-AA molecular mechanics force field with the Generalized Born/Surface Area (GB/SA) continuum model. This solvent model is not only less expensive than an explicit representation, but also yields increased sampling. Sampling is also increased using a rotamer library to direct some of the protein side-chain movements along with large dihedral moves. Finally, a softening function for the nonbonded force field terms is used, enabling the potential energy function to be slowly turned on throughout the course of the simulation. The docking procedure is optimized, and the results are presented for a single complex of the arabinose binding protein. It was found that for a rigid receptor model, the X-ray binding geometry was reproduced and uniquely identified based on the associated potential energy. However, when side-chain flexibility was included, although the X-ray structure was identified, it was one of three possible binding geometries that were energetically indistinguishable. These results suggest that on relaxing the constraint on receptor flexibility, the docking energy hypersurface changes from being funnel-like to rugged. A further 14 complexes were then examined using the optimized protocol. For each complex the docking methodology was tested for a fully flexible ligand, both with and without protein side-chain flexibility. For the rigid protein docking, 13 out of the 15 test cases were able to find the experimental binding mode; this number was reduced to 11 for the flexible protein docking. However, of these 11, in the majority of cases the experimental binding mode was not uniquely identified, but was present in a cluster of low energy structures that were energetically indistinguishable. These results not only support the presence of a rugged docking energy hypersurface, but also suggest that it may be necessary to consider the possibility of more than one binding conformation during ligand optimization.
本文采用一种基于两阶段模拟的方法来解决柔性小分子配体与大型柔性蛋白质靶点的对接问题。所提出的方法是一种混合方法，其中第一个组成部分是基于使用图论和递归距离几何算法推导同时满足的分子间氢键集合，将配体对接至蛋白质结合位点。基于距离相似性的聚类分析减少了输出结构的数量。这些结构使用具有广义玻恩/表面积（GB/SA）连续介质模型的AMBER-AA分子力学力场提交给改进的蒙特卡罗算法。这种溶剂模型不仅比显式表示成本更低，而且能增加采样。使用旋转异构体库来指导一些蛋白质侧链运动以及大的二面角移动也增加了采样。最后，对非键合力场项使用软化函数，使势能函数在整个模拟过程中能缓慢开启。对接程序得到了优化，并给出了阿拉伯糖结合蛋白单一复合物的结果。结果发现，对于刚性受体模型，基于相关势能再现并唯一确定了X射线结合几何结构。然而，当考虑侧链柔性时，虽然确定了X射线结构，但它是在能量上无法区分的三种可能结合几何结构之一。这些结果表明，在放宽对受体柔性的约束时，对接能量超曲面从漏斗状变为崎岖状。然后使用优化后的方案检查了另外14个复合物。对于每个复合物，对接方法针对完全柔性配体进行了测试，包括蛋白质侧链有无柔性的情况。对于刚性蛋白质对接，15个测试案例中有13个能够找到实验结合模式；对于柔性蛋白质对接，这个数字降至11个。然而，在这11个案例中，在大多数情况下，实验结合模式并非唯一确定，而是存在于能量上无法区分的低能结构簇中。这些结果不仅支持存在崎岖的对接能量超曲面，还表明在配体优化过程中可能有必要考虑不止一种结合构象的可能性。
