Department of Pharmaceutical Sciences and Administration, University of New England School of Pharmacy, 716 Stevens Avenue, Portland, ME 04103, USA.
Graduate School of Biomedical Science and Engineering, University of Maine, 5775 Stodder Hall, Orono, ME 04469, USA.
Int J Mol Sci. 2020 Oct 18;21(20):7699. doi: 10.3390/ijms21207699.
Glycosaminoglycans (GAGs) are the linear carbohydrate components of proteoglycans (PGs) and are key mediators in the bioactivity of PGs in animal tissue. GAGs are heterogeneous, conformationally complex, and polydisperse, containing up to 200 monosaccharide units. These complexities make studying GAG conformation a challenge for existing experimental and computational methods. We previously described an algorithm we developed that applies conformational parameters (i.e., all bond lengths, bond angles, and dihedral angles) from molecular dynamics (MD) simulations of nonsulfated chondroitin GAG 20-mers to construct 3-D atomic-resolution models of nonsulfated chondroitin GAGs of arbitrary length. In the current study, we applied our algorithm to other GAGs, including hyaluronan and nonsulfated forms of dermatan, keratan, and heparan and expanded our database of MD-generated GAG conformations. Here, we show that individual glycosidic linkages and monosaccharide rings in 10- and 20-mers of hyaluronan and nonsulfated dermatan, keratan, and heparan behave randomly and independently in MD simulation and, therefore, using a database of MD-generated 20-mer conformations, that our algorithm can construct conformational ensembles of 10- and 20-mers of various GAG types that accurately represent the backbone flexibility seen in MD simulations. Furthermore, our algorithm efficiently constructs conformational ensembles of GAG 200-mers that we would reasonably expect from MD simulations.
糖胺聚糖(GAGs)是蛋白聚糖(PGs)的线性碳水化合物成分,是 PGs 在动物组织中生物活性的主要介质。GAGs 具有异质性、构象复杂和多分散性,包含多达 200 个单糖单元。这些复杂性使得研究 GAG 构象成为现有实验和计算方法的挑战。我们之前描述了一种算法,该算法应用来自非硫酸软骨素 GAG 20 聚体分子动力学(MD)模拟的构象参数(即所有键长、键角和二面角)来构建任意长度的非硫酸软骨素 GAG 的 3D 原子分辨率模型。在当前研究中,我们将我们的算法应用于其他 GAG,包括透明质酸和非硫酸化形式的软骨素、角蛋白和肝素,并扩展了我们的 MD 生成 GAG 构象数据库。在这里,我们表明,透明质酸和非硫酸化软骨素、角蛋白和肝素的 10-和 20-聚体中的单个糖苷键和单糖环在 MD 模拟中随机且独立地起作用,因此,使用 MD 生成的 20-聚体构象数据库,我们的算法可以构建各种 GAG 类型的 10-和 20-聚体构象集合,这些集合准确地代表了 MD 模拟中所见的骨架灵活性。此外,我们的算法高效地构建了我们预计可以从 MD 模拟中得到的 GAG 200-聚体的构象集合。