Tool for Predicting Glycosaminoglycan Recognition of Proteins

项目总结 GAG呈现出相当大的结构多样性，这使得对单个GAG序列的研究 在人类看来是不可能的。到目前为止，大多数研究都依赖于不同种类的GAG成分，如肝素 和硫酸软骨素。最近，几十种寡糖已经可以在商业上买到 (Sigma(美国)、DExtra(英国)和Iduon(英国))。然而，即使是购买一个小的、相当多样化的图书馆 低聚糖非常昂贵(每个几微克到几毫克的价格约为200-300美元)。更重要的是，低聚糖 这些公司提供的通常是通用序列，并不代表存在的多样性 在自然界中。GAG寡糖的合成是具有挑战性的，只有少数几个小组有经验 合成技术。我们已经开发了一种计算工具，可以帮助预测关键的Gag序列 识别具有高亲和力的蛋白质。我们的工具已经对包括抗凝血酶、成纤维细胞在内的蛋白质进行了验证 生长因子-1及其受体、转化生长因子-2、凝血酶、组蛋白 乙酰转移酶p300，人中性粒细胞弹性蛋白酶和趋化因子CXCL13。我们建议将此工具免费制作 提供给研究界，以便许多小组可以通过计算评估他们的蛋白质是否 兴趣是搞笑的纽带。我们的两个目标包括：1)在网络服务器上开发图形用户界面 使研究人员能够利用我们的计算工具来研究Gag-蛋白质相互作用；以及2)先进 预测商用GAG序列相互作用的计算工具(HP/HS 和CS/DS)与蛋白质结合。这两个目标通过使我们的内部工具“显著”直接解决RFA 对于非专业人员来说更直截了当、更容易理解“。在产出方面，这项工作将提出一个网络-- 支持携带GAG序列文库和适当算法的工具，供远程研究人员使用 网站。这将进一步推动葡聚糖工具的不断民主化，以实现更有效的多糖研究。在……里面 在知识贡献方面，我们的计算工具将有助于加强对GAG是如何 被蛋白质识别，特别是那些属于凝血、炎症和生长/形态发生的蛋白质 系统。

PROJECT SUMMARY GAGs present considerable structural diversity, which has made the study of individual GAG sequences humanly impossible. Most studies performed to date rely on heterogeneous GAG compositions, such as heparin and chondroitin sulfate. Few dozen GAG oligosaccharides have become commercially available in recent times (Sigma (US), Dextra (UK), and Iduron (UK)). Yet, purchasing even a small, reasonably diverse library of these oligosaccharides is very expensive (~$200–300 for few µg to mg each). More importantly, the oligosaccharides available from these companies are generally the common sequences and do not represent the diversity present in nature. Synthesis of GAG oligosaccharides is challenging and only a handful of groups have experience with synthesis technology. We have developed a computational tool that helps predict key GAG sequence that recognize protein with high affinity. Our tool has been validated for proteins including antithrombin, fibroblast growth factor-1 & its receptor (FGF-1/FGFR1), transforming growth factor 2 (TGF2), thrombin, histone acetyltransferase p300, human neutrophil elastase and chemokine CXCL13. We propose to make this tool freely available to the research community so that many groups can computationally assess whether their protein of interest binds GAGs. Our two aims include 1) develop a graphical user interface (GUI) on a web-server to enable researchers utilize our computational tool for studying GAG–protein interactions; and 2) advance the computational tool for predicting the interaction of commercially available GAG sequences (HP/HS and CS/DS) with proteins. These two aims directly address the RFA by making our in-house tool “significantly more straightforward and accessible for non-specialists”. In terms of output, this work will put forward a web- enabled tool carrying libraries of GAG sequences and appropriate algorithms for use by researchers from remote sites. It will add to the continuing democratization of glycan tools to enable more effective glycan research. In terms of knowledge contribution, our computational tool would help enhance understanding on how GAGs are recognized by proteins, especially those belonging to coagulation, inflammation and growth/morphogenesis systems.