Wolfson Institute for Biomedical Research, University College London, Gower Street, London WC1E 6BT, UK.
Vernalis (R&D) Ltd., Granta Park, Abington, Cambridge CB21 6GB, UK.
Bioorg Med Chem. 2020 Jan 1;28(1):115143. doi: 10.1016/j.bmc.2019.115143. Epub 2019 Oct 15.
Drug discovery is increasingly tackling challenging protein binding sites regarding molecular recognition and druggability, including shallow and solvent-exposed protein-protein interaction interfaces. Macrocycles are emerging as promising chemotypes to modulate such sites. Despite their chemical complexity, macrocycles comprise important drugs and offer advantages compared to non-cyclic analogs, hence the recent impetus in the medicinal chemistry of macrocycles. Elaboration of macrocycles, or constituent fragments, can strongly benefit from knowledge of their binding mode to a target. When such information from X-ray crystallography is elusive, computational docking can provide working models. However, few studies have explored docking protocols for macrocycles, since conventional docking methods struggle with the conformational complexity of macrocycles, and also potentially with the shallower topology of their binding sites. Indeed, macrocycle binding mode prediction with the mainstream docking software GOLD has hardly been explored. Here, we present an in-depth study of macrocycle docking with GOLD and the ChemPLP scores. First, we summarize the thorough curation of a test set of 41 protein-macrocycle X-ray structures, raising the issue of lattice contacts with such systems. Rigid docking of the known bioactive conformers was successful (three top ranked poses) for 92.7% of the systems, in absence of crystallographic waters. Thus, without conformational search issues, scoring performed well. However, docking success dropped to 29.3% with the GOLD built-in conformational search. Yet, the success rate doubled to 58.5% when GOLD was supplied with extensive conformer ensembles docked rigidly. The reasons for failure, sampling or scoring, were analyzed, exemplified with particular cases. Overall, binding mode prediction of macrocycles remains challenging, but can be much improved with tailored protocols. The analysis of the interplay between conformational sampling and docking will be relevant to the prospective modelling of macrocycles in general.
药物研发越来越多地针对分子识别和可成药性方面具有挑战性的蛋白质结合位点,包括浅层和溶剂暴露的蛋白质-蛋白质相互作用界面。大环类化合物作为调节这些位点的有前途的化学类型正在出现。尽管大环类化合物具有化学复杂性,但它们包含重要的药物,并且与非环类似物相比具有优势,因此最近在大环类化合物的药物化学中出现了推动力。大环类化合物或组成片段的阐述可以从它们与靶标的结合模式的知识中受益匪浅。当无法从 X 射线晶体学中获得此类信息时,计算对接可以提供工作模型。然而,由于传统的对接方法难以处理大环类化合物的构象复杂性,并且还可能处理其结合位点的浅层拓扑结构,因此很少有研究探索大环类化合物的对接方案。实际上,主流对接软件 GOLD 对大环类化合物结合模式的预测几乎没有被探索过。在这里,我们对 GOLD 和 ChemPLP 评分进行了深入的大环类化合物对接研究。首先,我们总结了对 41 个蛋白质-大环类化合物 X 射线结构的测试集的彻底策展,提出了与这些系统的晶格接触问题。在不存在晶体水的情况下,对已知的生物活性构象进行刚性对接是成功的(三个排名最高的构象),对于 92.7%的系统。因此,在没有构象搜索问题的情况下,评分表现良好。然而,当使用 GOLD 内置的构象搜索时,对接成功率下降到 29.3%。然而,当为 GOLD 提供广泛的刚性对接构象集合时,成功率增加到 58.5%。对失败的原因(采样或评分)进行了分析,并以特定案例为例进行了说明。总体而言,大环类化合物的结合模式预测仍然具有挑战性,但通过定制的方案可以大大改善。构象采样和对接之间相互作用的分析将与大环类化合物的总体建模相关。