Computational tools to aid the design of glycomimetic agents

项目摘要 碳水化合物（也称为聚糖）和蛋白质之间的特定相互作用是引发或 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，碳水化合物加工酶（糖基转移酶和糖苷酶）是重要的靶标 然而，对于治疗性干预， 碳水化合物结合位点具有独特的挑战性。糖模拟物抑制剂的优化涉及 合成和筛选化学类似物，试图增加抑制潜力和生物活性。 活动鉴于碳水化合物的合成是出了名的费力， 随着亲和力的逐渐增加， 发展尽管存在挑战，但采用天然碳水化合物作为支架的好处是， 本质上赋予所需的特异性。糖模拟物的基本挑战是， 预测哪些修饰会导致增强的亲和力而不损害特异性。 专门设计用于筛选碳水化合物类似物的计算方法可以 对提高综合选择的客观性和确定综合努力的优先次序都有宝贵的帮助 所需的糖模拟物的发展。虚拟筛选是常见的主流药物化学 并导致发现了具有纳摩尔亲和力的非糖模拟小分子抑制剂（12，29）。 然而，它还没有被广泛应用于糖模拟物的设计。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 非糖科学家，以及对糖科学家来说，缺乏对复杂建模方法的熟悉。 在本申请中，我们提出开发、验证和实施配体的替代策略。 对接，利用计算建模和结构生物学的好处。具体来说，我们将 开发一种在线计算方法，使用碳水化合物-蛋白质共晶体（或NMR）结构， 通过原位修饰结合的寡糖来优化先导化合物的基础。我们召集了一群 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。这些科学家有超过200年的时间 具有糖模拟物合成和评价的经验。 目标的成功完成将导致一个有效的计算工具，以帮助发现， 靶向碳水化合物-蛋白质相互作用的治疗剂的优化， 正在进行的对抗多重耐药细菌的战斗。

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.