# Modeling the mucosal glycopeptide mesh for improved disease understanding and mucin-inspired biomaterial design

> **NIH NIH R35** · NORTHEASTERN UNIVERSITY · 2024 · $400,000

## Abstract

ABSTRACT
Mucins and other densely glycosylated proteins play critical roles in a number of biological processes, disease
conditions, and therapeutics. The functioning of these sugar-coated molecular machines depends on their
structure, dynamics, and conformational transitions. Experimental techniques for capturing such structural
dynamics, however, can be extremely challenging and resource intensive. We seek to improve upon some of
the existing glycan modeling computational tools as well as design new in silico techniques, as robust alternatives
to experimental studies. These tools will be used to build interconnected mucin glycoprotein gel systems with
native glycosylation patterns, and obtain understanding of functional underpinnings at the molecular level. Effects
of perturbations in terms of pH variance, varying glycosylation patterns, and charge distribution changes will be
investigated. This will enable detailed comprehension of the physical properties of mucins that drive their
function, as well as the molecular elucidation of disease conditions of cystic fibrosis, mucosal inflammation, and
mucin-mediated cancers. A multi-modal approach will be employed to study these mucin networks in different
scales – (i) first-principles based atomistic modeling to capture the equilibrium structure-dynamics; (ii)
biophysics-based coarse-grained methods to describe bulk properties and transitions, and (iii) data-driven
machine learning approaches to predict topology and intermolecular interactions. Inspired from mucosal gels,
we will use these tools to design novel mucin-like nanomaterials constructed from glycan-peptide heteropolymer
networks to target different biomedical applications. We aim to optimize a machine learning (ML)-driven
combinatorics method for glycan arrangement in these polymers that will provide enhanced control over material
properties – a molecular LEGO of glycans geared towards customizable mucin-mimetic biomaterials.

## Key facts

- **NIH application ID:** 10931398
- **Project number:** 5R35GM151231-02
- **Recipient organization:** NORTHEASTERN UNIVERSITY
- **Principal Investigator:** Srirupa Chakraborty
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $400,000
- **Award type:** 5
- **Project period:** 2023-09-20 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10931398

## Citation

> US National Institutes of Health, RePORTER application 10931398, Modeling the mucosal glycopeptide mesh for improved disease understanding and mucin-inspired biomaterial design (5R35GM151231-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10931398. Licensed CC0.

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