Developing a Computer Model for Improved Disease Understanding

Srirupa Chakraborty

ChE Assistant Professor Srirupa Chakraborty was awarded a $1.99 million NIH R35 MIRA award for “Modeling the mucosal glycopeptide mesh for improved disease understanding and mucin-inspired biomaterial design.” This grant will be awarded over the course of five years. This award is designed to provide long-term support and increased flexibility to experienced and accomplished researchers, allowing them to pursue high-impact research programs.

Abstract Source: NIH

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.

Related Departments:Chemical Engineering