GlycoTorch Vina: Docking Designed and Tested for Glycosaminoglycans

蛋白质数据库 对接(动物) 化学 盐桥 糖苷键 糖胺聚糖 计算生物学 计算机科学 立体化学 生物化学 生物 医学 护理部 突变体 基因
作者
Eric D. Boittier,Jed M. Burns,Neha S. Gandhi,Vito Ferro
出处
期刊:Journal of Chemical Information and Modeling [American Chemical Society]
卷期号:60 (12): 6328-6343 被引量:43
标识
DOI:10.1021/acs.jcim.0c00373
摘要

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 1C4 to 2SO 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 2SO 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.
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