Improving Accuracy, Diversity, and Speed with Prime Macrocycle Conformational Sampling

A novel method for
exploring macrocycle conformational space, Prime
macrocycle conformational sampling (Prime-MCS), is introduced and
evaluated in the context of other available algorithms (Molecular
Dynamics, LowModeMD in MOE, and MacroModel Baseline Search). The algorithms
were benchmarked on a data set of 208 macrocycles which was curated
for diversity from the Cambridge Structural Database, the Protein
Data Bank, and the Biologically Interesting Molecule Reference Dictionary.
The algorithms were evaluated in terms of accuracy (ability to reproduce
the crystal structure), diversity (coverage of conformational space),
and computational speed. Prime-MCS most reliably reproduced crystallographic
structures for RMSD thresholds >1.0 Å, most often produced
the
most diverse conformational ensemble, and was most often the fastest
algorithm. Detailed analysis and examination of both typical and outlier
cases were performed to reveal characteristics, shortcomings, expected
performance, and complementarity of the methods.

本研究提出了一种用于探索大环化合物构象空间的新型方法——大环构象采样Prime-MCS（Prime macrocycle conformational sampling），并结合现有可用算法（分子动力学、MOE内置的LowModeMD以及MacroModel基线搜索算法）对其进行了评估。本研究从剑桥晶体结构数据库（Cambridge Structural Database）、蛋白质数据银行（Protein Data Bank）以及生物活性分子参考词典（Biologically Interesting Molecule Reference Dictionary）中筛选得到具备多样性的208个大环化合物，以此构建数据集对各算法开展基准测试。评估指标包括准确性（复刻晶体结构的能力）、多样性（构象空间覆盖范围）以及计算速度。当均方根偏差（Root Mean Square Deviation, RMSD）阈值大于1.0 Å时，Prime-MCS能够最可靠地复刻晶体结构；其生成的构象集合通常具有最高的多样性，且多数情况下是计算速度最快的算法。本研究还对典型案例与异常案例进行了详细分析与考察，以揭示各算法的特性、局限性、预期性能及互补性。