# Tool for Predicting Glycosaminoglycan Recognition of Proteins

> **NIH NIH U01** · VIRGINIA COMMONWEALTH UNIVERSITY · 2020 · $281,994

## Abstract

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.

## Key facts

- **NIH application ID:** 9971486
- **Project number:** 5U01CA241951-02
- **Recipient organization:** VIRGINIA COMMONWEALTH UNIVERSITY
- **Principal Investigator:** Umesh Ramanlal Desai
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $281,994
- **Award type:** 5
- **Project period:** 2019-07-03 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9971486

## Citation

> US National Institutes of Health, RePORTER application 9971486, Tool for Predicting Glycosaminoglycan Recognition of Proteins (5U01CA241951-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9971486. Licensed CC0.

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