# Technologies to predict and probe glycosyl transfer

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2020 · $460,220

## Abstract

Technologies to Predict and Probe Glycosyl Transfer
PROJECT SUMMARY
Glycosylation is fundamental to all life. Glycans add an additional layer of information to biomolecules, affect
both conformation and dynamics, have diverse modulatory roles, such as stabilizing protein folds and signaling
stem-cell fate, and feature prominently in disease. As our knowledge of glycosylation's mechanistic role
continues to grow, so do the opportunities for therapeutic exploitation. Emerging technologies have opened the
door to facile, scalable biosynthesis of defined glycoconjugates whose glycan moieties can be tailored for
different applications. At the same time, a set of tools has emerged to predict the 3D structures of biomolecules
rapidly and accurately and to design new biomolecules and variants. Structure prediction and design tools have
the potential to transform glycobiology by providing structural insights into the effects of glycans and additionally
by enabling design of altered and novel glycoconjugates to create new functions.
Our overarching goal is to develop complementary computational and experimental methods to probe the
structural and environmental contexts that affect glycosylation and elongation. Our research will address the
technical barriers needed to achieve these tools, namely developing methods to sample and score diverse
glycoconjugate structures and to scan alternate glycoforms in experiment. Our work will create computational
algorithms to predict glycosylation sites and elongation products and experimental tools to probe protein-wide
glycoforms in specific targets. A computational tool to design and predict favorable candidates for
experimentation would reduce costs and speed the advance of glycoscience. Experimental approaches to
synthesize diverse glycoforms will enable functional testing of alternate glycoforms.
To develop our technologies, we will computationally predict and experimentally generate constructs with
oligosaccharyl transfer to all possible sites in the Im7 and RNase A model proteins. We will then proceed to
computationally predict and experimentally test alternate elongation schemes, which result in different glycan
structures, for these constructs. Together, our tool set will test how three-dimensional structure alters
glycosylation, elongation, and glycoprotein function. When complete, these technologies will enable biologists to
probe and design alternate glycoforms both computationally and experimentally for (1) research on the biology
of glycosylation and glycosylated molecules and (2) applications to vaccines and therapeutic biomolecule design.

## Key facts

- **NIH application ID:** 9986780
- **Project number:** 5R01GM127578-03
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** JEFFREY J GRAY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $460,220
- **Award type:** 5
- **Project period:** 2018-08-01 → 2021-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9986780

## Citation

> US National Institutes of Health, RePORTER application 9986780, Technologies to predict and probe glycosyl transfer (5R01GM127578-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9986780. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*