Nagarajan Balaji, Sankaranarayanan Nehru Viji, Patel Bhaumik B, Desai Umesh R
Institute for Structural Biology, Drug Discovery and Development and Department of Medicinal Chemistry, Virginia Commonwealth University, Richmond, Virginia, United States of America.
Hunter Holmes Muire VA Medical Center, Richmond, Virginia, United States of America.
PLoS One. 2017 Feb 9;12(2):e0171619. doi: 10.1371/journal.pone.0171619. eCollection 2017.
Glycosaminoglycans (GAGs) are key natural biopolymers that exhibit a range of biological functions including growth and differentiation. Despite this multiplicity of function, natural GAG sequences have not yielded drugs because of problems of heterogeneity and synthesis. Recently, several homogenous non-saccharide glycosaminoglycan mimetics (NSGMs) have been reported as agents displaying major therapeutic promise. Yet, it remains unclear whether sulfated NSGMs structurally mimic sulfated GAGs. To address this, we developed a three-step molecular dynamics (MD)-based algorithm to compare sulfated NSGMs with GAGs. In the first step of this algorithm, parameters related to the range of conformations sampled by the two highly sulfated molecules as free entities in water were compared. The second step compared identity of binding site geometries and the final step evaluated comparable dynamics and interactions in the protein-bound state. Using a test case of interactions with fibroblast growth factor-related proteins, we show that this three-step algorithm effectively predicts the GAG structure mimicking property of NSGMs. Specifically, we show that two unique dimeric NSGMs mimic hexameric GAG sequences in the protein-bound state. In contrast, closely related monomeric and trimeric NSGMs do not mimic GAG in either the free or bound states. These results correspond well with the functional properties of NSGMs. The results show for the first time that appropriately designed sulfated NSGMs can be good structural mimetics of GAGs and the incorporation of a MD-based strategy at the NSGM library screening stage can identify promising mimetics of targeted GAG sequences.
糖胺聚糖（GAGs）是关键的天然生物聚合物，具有包括生长和分化在内的一系列生物学功能。尽管功能多样，但由于异质性和合成问题，天然GAG序列尚未产生药物。最近，有几种同质非糖糖胺聚糖模拟物（NSGMs）被报道为具有重大治疗前景的药物。然而，硫酸化NSGMs在结构上是否模拟硫酸化GAGs仍不清楚。为了解决这个问题，我们开发了一种基于三步分子动力学（MD）的算法，用于比较硫酸化NSGMs和GAGs。在该算法的第一步中，比较了两个高度硫酸化分子在水中作为自由实体所采样的构象范围相关的参数。第二步比较了结合位点几何形状的一致性，最后一步评估了蛋白质结合状态下的可比动力学和相互作用。通过与成纤维细胞生长因子相关蛋白相互作用的测试案例，我们表明这种三步算法有效地预测了NSGMs的GAG结构模拟特性。具体而言，我们表明两种独特的二聚体NSGMs在蛋白质结合状态下模拟六聚体GAG序列。相比之下，密切相关的单体和三聚体NSGMs在自由或结合状态下均不模拟GAG。这些结果与NSGMs的功能特性非常吻合。结果首次表明，经过适当设计的硫酸化NSGMs可以很好地模拟GAG的结构，并且在NSGM库筛选阶段采用基于MD的策略可以识别出有前景的靶向GAG序列模拟物。
