# DMS/NIGMS 1: Topological Dynamics Models of Protein Function

> **NIH NIH R01** · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · 2024 · $197,205

## Abstract

It is now well accepted that structured governed dynamics modulate function, yet we still don’t know how a 
few changes (e.g., mutations) in sequence modify dynamics to alter function. Understanding this interplay is 
a key step to engineering proteins with desired function to address disease, and viral evolution to fight with 
endemics or future pandemics, as well as many other bioengineering applications. Despite the work of many 
researchers, the connection between sequence, structure and dynamics remains elusive. This is partly 
because there is no powerful methods that can accurately quantify each amino-acid position’s contribution 
to structure and dynamics. We propose to fill this gap by using an innovative, interdisciplinary method based 
on mathematical topology and physics based protein dynamics modeling. The guiding hypothesis is that the 
topological landscape of proteins governs conformational dynamics and that it can be modified with sitespecific mutations. To test this hypothesis, we will create the mathematical framework upon which the local 
and global topology of proteins and conformational dynamics can be rigorously associated and the evolution 
of the topological landscape can be quantified. This work is particularly timely for two reasons: (1) 
conformational dynamics have established a connection between structure and function and evolution at the 
proteotome scale and (2) methods from mathematical topology have shown evidence of being able to 
characterize protein structure.
This research advances knowledge in mathematics and biology and breaks existing barriers in: (1) topology 
and geometry, (2) quantitative characterizations of protein structure, (3) connecting microscopic effects to 
the macroscopic properties of proteins and (4) providing a novel framework that enables not only to uncover 
the molecular mechanism of protein function and evolution based on fundamental mathematical and physical 
concepts, but also enables to design novel proteins with desired function. This is achieved by (1) creating
novel measures of topological complexity and a mathematical topological framework for characterizing multiscale protein structure, by (2) coarse-grained modeling and dynamical analysis of proteins and (3) by 
combining the two to establish the connection between conformational dynamics and the topological 
landscape of proteins. This integrated novel framework will be tested on different protein systems with 
available deep scanning mutational experimental data. The successful completion of this work could lead to 
a breakthrough that would enable to predict and modulate protein function based on structural dynamics.

## Key facts

- **NIH application ID:** 10934374
- **Project number:** 5R01GM152735-02
- **Recipient organization:** ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
- **Principal Investigator:** Eleni Panagiotou
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $197,205
- **Award type:** 5
- **Project period:** 2023-09-25 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10934374

## Citation

> US National Institutes of Health, RePORTER application 10934374, DMS/NIGMS 1: Topological Dynamics Models of Protein Function (5R01GM152735-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10934374. Licensed CC0.

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