Macrocycles represent an important class of medicinally relevant small molecules due to their interesting biological properties. Therefore, a firm understanding of their conformational preferences is important for drug design. Given the importance of macrocycle-protein modelling in drug discovery, we envisaged that a systematic study of both classical and recent specialized methods would provide guidance for other practitioners within the field. In this study we compare the performance of the general, well established conformational analysis methods Monte Carlo Multiple Minimum (MCMM) and Mixed Torsional/Low-Mode sampling (MTLMOD) with two more recent and specialized macrocycle sampling techniques: MacroModel macrocycle Baseline Search (MD/LLMOD) and Prime macrocycle conformational sampling (PRIME-MCS). Using macrocycles extracted from 44 macrocycle-protein X-ray crystallography complexes, we evaluated each method based on their ability to (i) generate unique conformers, (ii) generate unique macrocycle ring conformations, (iii) identify the global energy minimum, (iv) identify conformers similar to the X-ray ligand conformation after Protein Preparation Wizard treatment (X-rayppw), and (v) to the X-rayppw ring conformation. Computational speed was also considered. In addition, conformational coverage, as defined by the number of conformations identified, was studied. In order to study the relative energies of the bioactive conformations, the energy differences between the global energy minima and the energy minimized X-rayppw structures and, the global energy minima and the MCMM-Exhaustive (1,000,000 search steps) generated conformers closest to the X-rayppw structure, were calculated and analysed. All searches were performed using relatively short run times (10,000 steps for MCMM, MTLMOD and MD/LLMOD). To assess the performance of the methods, they were compared to an exhaustive MCMM search using 1,000,000 search steps for each of the 44 macrocycles (requiring ca 200 times more CPU time). Prior to our analysis, we also investigated if the general search methods MCMM and MTLMOD could also be optimized for macrocycle conformational sampling. Taken together, our work concludes that the more general methods can be optimized for macrocycle modelling by slightly adjusting the settings around the ring closure bond. In most cases, MCMM and MTLMOD with either standard or enhanced settings performed well in comparison to the more specialized macrocycle sampling methods MD/LLMOD and PRIME-MCS. When using enhanced settings for MCMM and MTLMOD, the X-rayppw conformation was regenerated with the greatest accuracy. The, MD/LLMOD emerged as the most efficient method for generating the global energy minima.

中文翻译：

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大环的构象分析：比较一般方法和专门方法。

大环化合物由于其有趣的生物学特性而代表了一类重要的与医学相关的小分子。因此，对其构象偏好的牢固理解对于药物设计很重要。考虑到大环蛋白质建模在药物发现中的重要性，我们设想对经典方法和最新专门方法的系统研究将为该领域的其他从业人员提供指导。在这项研究中，我们将比较完善的常规构象分析方法蒙特卡洛多重最小值（MCMM）和混合扭转/低模式采样（MTLMOD）的性能与两种最新的专业大周期采样技术进行比较：MacroModel大周期基线搜索（MD） / LLMOD）和Prime大环构象采样（PRIME-MCS）。使用从44个大环蛋白X射线晶体复合物中提取的大环，我们根据每种方法的能力评估了每种方法的能力：（i）生成独特的构象异构体，（ii）生成独特的大环构象，（iii）识别全局能量最小值，（iv ）鉴定与蛋白质制备向导处理（X-rayppw）后与X射线配体构象相似的构象异构体，以及（v）与X-rayppw环构象相似的构象异构体。还考虑了计算速度。另外，还研究了由确定的构象数目定义的构象覆盖率。为了研究生物活性构象的相对能量，全局能量最小值和能量最小化X射线结构之间的能量差，以及全局能量最小值和MCMM-Exhaustive（1,000，000个搜索步骤）生成并分析了最接近X-rayppw结构的构象异构体。所有搜索都使用相对较短的运行时间（对于MCMM，MTLMOD和MD / LLMOD，需要10,000个步骤）。为了评估这些方法的性能，将它们与44个宏周期中每个周期使用1,000,000个搜索步骤的详尽MCMM搜索进行了比较（需要大约200倍的CPU时间）。在进行分析之前，我们还研究了通用搜索方法MCMM和MTLMOD是否也可以针对大环构象采样进行优化。综上所述，我们的工作得出的结论是，通过稍微调整环闭合键周围的设置，可以针对大环模型优化更通用的方法。在大多数情况下，与更专业的大循环采样方法MD / LLMOD和PRIME-MCS相比，具有标准设置或增强设置的MCMM和MTLMOD表现良好。当对MCMM和MTLMOD使用增强的设置时，将以最高的精度重新生成X-rayppw构象。MD / LLMOD成为产生全球最低能量的最有效方法。