School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland 4072, Australia.
Chemistry and Physics, Centre for Genomics and Personalised Health, Faculty of Science and Engineering, Queensland University of Technology, Brisbane, Queensland 4000, Australia.
J Chem Inf Model. 2020 Dec 28;60(12):6328-6343. doi: 10.1021/acs.jcim.0c00373. Epub 2020 Nov 5.
Glycosaminoglycans (GAGs) are a family of anionic carbohydrates that play an essential role in the physiology and pathology of all eukaryotic life forms. Experimental determination of GAG-protein complexes is challenging due to their difficult isolation from biological sources, natural heterogeneity, and conformational flexibility-including possible ring puckering of sulfated iduronic acid from C to S conformation. To overcome these challenges, we present GlycoTorch Vina (GTV), a molecular docking tool based on the carbohydrate docking program VinaCarb (VC). Our program is unique in that it contains parameters to model S sugars while also supporting glycosidic linkages specific to GAGs. We discuss how crystallographic models of carbohydrates can be biased by the choice of refinement software and structural dictionaries. To overcome these variations, we carefully curated 12 of the best available GAG and GAG-like crystal structures (ranging from tetra- to octasaccharides or longer) obtained from the PDB-REDO server and refined using the same protocol. Both GTV and VC produced pose predictions with a mean root-mean-square deviation (RMSD) of 3.1 Å from the native crystal structure-a statistically significant improvement when compared to AutoDock Vina (4.5 Å) and the commercial software Glide (5.9 Å). Examples of how real-space correlation coefficients can be used to better assess the accuracy of docking pose predictions are given. Comparisons between statistical distributions of empirical "salt bridge" interactions, relevant to GAGs, were compared to density functional theory (DFT) studies of model salt bridges, and water-mediated salt bridges; however, there was generally a poor agreement between these data. Water bridges appear to play an important, yet poorly understood, role in the structures of GAG-protein complexes. To aid in the rapid prototyping of future pose scoring functions, we include a module that allows users to include their own torsional and nonbonded parameters.
糖胺聚糖（GAGs）是一类阴离子碳水化合物，在所有真核生命形式的生理和病理中都起着至关重要的作用。由于它们难以从生物来源中分离、天然异质性以及构象灵活性（包括可能的硫酸艾杜糖醛酸从 C 到 S 构象的环褶皱），因此实验测定 GAG-蛋白复合物具有挑战性。为了克服这些挑战，我们提出了 GlycoTorch Vina（GTV），这是一种基于碳水化合物对接程序 VinaCarb（VC）的分子对接工具。我们的程序的独特之处在于，它包含了对 S 糖进行建模的参数，同时还支持 GAG 特有的糖苷键。我们讨论了如何通过选择精修软件和结构字典来使碳水化合物的晶体模型产生偏差。为了克服这些差异，我们精心整理了来自 PDB-REDO 服务器的 12 个可用的最佳 GAG 和 GAG 样晶体结构（范围从四糖到八糖或更长），并使用相同的方案进行了精修。GTV 和 VC 都产生了与天然晶体结构的平均均方根偏差（RMSD）为 3.1 Å 的构象预测——与 AutoDock Vina（4.5 Å）和商业软件 Glide（5.9 Å）相比，这是一个统计学上的显著改进。给出了如何使用实空间相关系数来更好地评估对接构象预测准确性的示例。比较了与 GAG 相关的经验“盐桥”相互作用的统计分布与模型盐桥和水介导的盐桥的密度泛函理论（DFT）研究之间的关系，但这些数据之间通常存在较差的一致性。水桥似乎在 GAG-蛋白复合物的结构中起着重要但尚未被充分理解的作用。为了帮助快速原型设计未来的构象评分函数，我们包含了一个允许用户包含自己的扭转和非键参数的模块。
