Schrodinger, Inc. Department of Life Sciences, New York, NY, 10036, USA.
Sci Rep. 2018 Apr 26;8(1):6585. doi: 10.1038/s41598-018-24766-5.
While macrocyclization of a linear compound to stabilize a known bioactive conformation can be a useful strategy to increase binding potency, the difficulty of macrocycle synthesis can limit the throughput of such strategies. Thus computational techniques may offer the higher throughput required to screen large numbers of compounds. Here we introduce a method for evaluating the propensity of a macrocyclic compound to adopt a conformation similar that of a known active linear compound in the binding site. This method can be used as a fast screening tool for prioritizing macrocycles by leveraging the assumption that the propensity for the known bioactive substructural conformation relates to the affinity. While this method cannot to identify new interactions not present in the known linear compound, it could quickly differentiate compounds where the three dimensional geometries imposed by the macrocyclization prevent adoption of conformations with the same contacts as the linear compound in their conserved region. Here we report the implementation of this method using an RMSD-based structural descriptor and a Boltzmann-weighted propensity calculation and apply it retrospectively to three macrocycle linker optimization design projects. We found the method performs well in terms of prioritizing more potent compounds.
虽然将线性化合物环化以稳定已知的生物活性构象可以是增加结合效力的有用策略，但大环合成的难度可能会限制此类策略的通量。因此，计算技术可能提供筛选大量化合物所需的更高通量。在这里，我们介绍了一种评估大环化合物在结合部位采用与已知活性线性化合物相似构象的倾向的方法。该方法可作为通过利用以下假设作为快速筛选工具来对大环进行优先级排序：已知生物活性亚结构构象的倾向与亲和力相关。虽然该方法无法识别在已知线性化合物中不存在的新相互作用，但它可以快速区分其中的化合物三维几何形状由环化引起，阻止了与线性化合物的保守区域相同的构象的采用。在这里，我们报告了使用基于 RMSD 的结构描述符和 Boltzmann 加权倾向计算来实现该方法，并将其回顾性地应用于三个大环连接优化设计项目。我们发现该方法在优先考虑更有效的化合物方面表现良好。
