Computational tools to aid the design of glycomimetic agents

项目摘要 碳水化合物(也称为多糖)和蛋白质之间的特定相互作用是启动或 许多疾病的进展。碳水化合物结合蛋白(人、细菌或病毒凝集素和粘附素) 因此，糖类加工酶(糖基转移酶和糖苷酶)是重要的靶标。 然而，对于治疗干预，类药物分子的产生可以竞争性地抑制 碳水化合物结合位点具有独一无二的挑战性。拟糖化抑制剂的优化包括 化学类似物的合成和筛选以提高抑制力和生物学活性 活动。鉴于碳水化合物的合成是出了名的费力，评估无数类似物的任务 随着亲和力的逐渐增加，对拟葡萄糖药物来说，这是一个特别重要的瓶颈 发展。尽管有这些挑战，使用本地碳水化合物作为支架的好处是它 本质上赋予了所需的专一性。创造一种葡萄糖仿生剂的根本挑战是 预测哪些修饰将在不影响特异性的情况下提高亲和力。 专门设计用来筛选碳水化合物类似物的计算方法可能是 对增加综合选择的客观性和确定综合工作的优先顺序都有非常宝贵的帮助 拟糖体发育所必需的。虚拟筛选在主流药物化学中司空见惯 并导致了具有纳摩尔亲和力的非拟糖小分子抑制剂的发现(12，29)。 然而，它还没有被广泛应用于葡萄糖仿生设计。我们认为这是由几个因素造成的， 包括碳水化合物结构和命名的复杂性，这为 非糖类科学家，以及对于糖类科学家来说，对复杂的建模方法缺乏熟悉。 在本应用中，我们建议开发、验证和实施一种替代Ligand的策略 利用计算建模和结构生物学的优势的对接。具体来说，我们将 开发一种使用碳水化合物-蛋白质共晶(或核磁共振)结构的在线计算方法 通过原位修饰结合的低聚糖为铅的优化奠定了基础。我们已经召集了一批 实验糖生物学家和化学家同意提供数据并独立验证 我们正在开发的工具的预测准确性。这些科学家在过去的200多年里 有糖类药物合成和评价经验。 AIMS的成功完成将导致一个经过验证的计算工具来帮助发现和 针对碳水化合物-蛋白质相互作用的治疗剂的优化，这些相互作用在 正在进行的与耐多药细菌的战斗。

Project Summary Specific interactions between carbohydrates (also known as glycans) and proteins underlie the initiation or progression of many diseases. Carbohydrate-binding proteins (human, bacterial or viral lectins and adhesins) and carbohydrate-processing enzymes (glycosyltransferases and glycosidases) are therefore important targets for therapeutic intervention, however the creation of drug-like molecules that can competitively inhibit carbohydrate-binding sites is uniquely challenging. The optimization of a glycomimetic inhibitor involves the synthesis and screening of chemical analogs in an attempt to increase the inhibitory potential and biological activity. Given that carbohydrate synthesis is notoriously laborious, the task of evaluating innumerable analogs with incrementally increasing affinities introduces a particularly significant bottleneck for glycomimetic development. Despite the challenges, the benefit of employing the native carbohydrate as a scaffold is that it intrinsically confers the desired specificity. The fundamental challenge in the creation of a glycomimetic is that of divining which modifications will lead to enhanced affinity without compromising specificity. Computational approaches that are specifically designed to screen analogs of carbohydrates could be invaluable aids to both increasing the objectivity of the synthetic choices and to prioritizing the synthetic effort required for glycomimetic development. Virtual screening is commonplace in mainstream medicinal chemistry and has led to the discovery of non-glycomimetic small molecule inhibitors with nanomolar affinities (12,29). However, it has yet to be widely applied in glycomimetic design. We believe that this is due to several factors, including the complexity of carbohydrate structure and nomenclature, which creates a significant barrier for non-glycoscientists, and, for glycoscientists, a lack of familiarity with sophisticated modeling methods. In the present application, we propose to develop, validate, and implement an alternative strategy to ligand docking that leverages the benefits of computational modeling and structural biology. Specifically, we will develop an online computational approach that uses carbohydrate-protein co-crystal (or NMR) structures as the basis for lead optimization by modifying the bound oligosaccharide in situ. We have assembled a group of experimental glycobiologists and chemists who have agreed to provide data and independently validate the predictive accuracy of the tools we are developing. These scientists have over 200 years of combined experience in glycomimetic synthesis and evaluation. Successful completion of the aims will lead to a validated computational tool to aid in the discovery and optimization of therapeutic agents that target carbohydrate-protein interactions that are particularly relevant in the ongoing battle against multidrug resistant bacteria.