Docking of Macrocycles: Comparing Rigid and Flexible Docking in Glide

In
recent years, there has been an increased interest in using
macrocyclic compounds for drug discovery and development. For docking
of these commonly large and flexible compounds to be addressed, a
screening and a validation set were assembled from the PDB consisting
of 16 and 31 macrocycle-containing protein complexes, respectively.
The macrocycles were docked in Glide by rigid docking of pregenerated
conformational ensembles produced by the macrocycle conformational
sampling method (MCS) in Schrödinger Release 2015-3 or by direct
Glide flexible docking after performing ring-templating. The two protocols
were compared to rigid docking of pregenerated conformational ensembles
produced by an exhaustive Monte Carlo multiple minimum (MCMM) conformational
search and a shorter MCMM conformational search (MCMM-short). The
docking accuracy was evaluated and expressed as the RMSD between the
heavy atoms of the ligand as found in the X-ray structure after refinement
and the poses obtained by the docking protocols. The median RMSD values
for top-scored poses of the screening set were 0.83, 0.80, 0.88, and
0.58 Å for MCMM, MCMM-short, MCS, and Glide flexible docking,
respectively. There was no statistically significant difference in
the performance between rigid docking of pregenerated conformations
produced by the MCS and direct docking using Glide flexible docking.
However, the flexible docking protocol was 2-times faster in docking
the screening set compared to that of the MCS protocol. In a final
study, the new Prime-MCS method was evaluated in Schrödinger
Release 2016-3. This method is faster compared that of to MCS; however,
the conformations generated were found to be suboptimal for rigid
docking. Therefore, on the basis of timing, accuracy, and ease of
set up, standard Glide flexible docking with prior ring-templating
is recommended over current gold standard protocols using rigid docking
of pregenerated conformational ensembles.

近年来，大环化合物（macrocyclic compounds）在药物研发领域的研究热度持续攀升。为解决此类通常体积庞大且构象灵活的化合物的分子对接（docking）难题，研究人员从蛋白质数据库（PDB, Protein Data Bank）中构建了筛选集与验证集，二者分别包含16个和31个含大环化合物的蛋白质复合物。本次研究采用两种方案开展大环化合物的Glide分子对接：一是通过Schrödinger Release 2015-3中的大环构象采样方法（MCS）生成预构象集合后，进行刚性对接；二是先实施环模板化，再直接开展Glide柔性对接。同时将上述两种方案，与基于穷举蒙特卡洛多极小值（MCMM）构象搜索得到的预构象集合的刚性对接，以及简化版MCMM构象搜索（MCMM-short）得到的预构象集合的刚性对接进行性能对比。对接精度以配体重原子的均方根偏差（RMSD, Root Mean Square Deviation）为评价指标，即经结构精修后的X射线晶体结构中配体的重原子坐标，与各对接方案得到的对接构象之间的RMSD值。针对筛选集得分最高的对接构象，四种方案的中位RMSD值分别为：MCMM为0.83 Å、MCMM-short为0.80 Å、MCS为0.88 Å、Glide柔性对接为0.58 Å。分析结果显示，采用MCS生成预构象集合的刚性对接，与直接使用Glide柔性对接的性能无统计学显著性差异；但后者对接筛选集的速度是MCS方案的2倍。在后续的验证研究中，研究人员在Schrödinger Release 2016-3中对新型Prime-MCS方法进行了评估。该方法的对接速度优于MCS方案，但生成的构象用于刚性对接时效果欠佳。因此，综合考量耗时、精度与操作便捷性，相较于当前采用预生成构象集合刚性对接的金标准方案，推荐使用预先进行环模板化的标准Glide柔性对接方案。