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 (TGF2), 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.