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.

本研究提出一种用于探索大环化合物（macrocycle）构象空间的全新方法——Prime大环构象采样法（Prime macrocycle conformational sampling，Prime-MCS），并结合现有公开算法（分子动力学（Molecular Dynamics）、MOE软件中的LowModeMD以及MacroModel基线搜索（MacroModel Baseline Search））对其开展性能对比评估。研究团队从剑桥结构数据库（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）中筛选得到兼具结构多样性的208个大环化合物，以此构建基准测试数据集。本次评估从三项指标展开：精度（即重现晶体结构的能力）、构象多样性（即构象空间的覆盖范围）以及计算耗时。在均方根偏差（Root Mean Square Deviation，RMSD）阈值大于1.0 Å的条件下，Prime-MCS能够最可靠地重现晶体结构；其生成的构象集合通常具备最优的多样性，且多数情况下计算耗时最短。本研究还针对典型案例与异常案例展开了详尽分析与考察，以揭示各方法的特性、局限性、预期性能及方法间的互补性。