# Structural Dynamics of Viral Proteins: Development and Application of Multiscale Computational Methods for Studying Viral Capsids, Proteins and Membrane Systems

> **NIH NIH R35** · UNIVERSITY OF CONNECTICUT STORRS · 2022 · $442,750

## Abstract

This proposal presents a vision for my research program over the next five years. My lab is engaged in the
application and development of multiscale computational methods to study biomolecular systems, with main
driver interests in non-enveloped virus capsids, biomembranes and protein dynamics. Through these studies we
will provide modeling tools, gain insights into the regulation and mechanisms of biomolecular functions and apply
deep learning methods to gain a new comprehension of protein communication and conformational changes.
The main motivation in our viral capsid studies is to understand the determinants and molecular mechanisms of
infection processes leading to capsid uncoating/disassembly. Our work on virus systems will expand into the
genus of Enteroviruses, which include severe health threats such as poliovirus, EV-A71 and EV-D68. There are
numerous high resolution structures of mature, uncoating intermediates and genome released states of
enteroviruses which enable this research direction. Significant findings from these studies will include detailed
structural and energetic information regarding viral infection related processes, which will be valuable in the
development of anti-viral therapies against these agents. Our interests in biomembranes are centrally related to
how bilayer lipid composition and shape affect the structure, dynamics and functional properties of lipids,
peptides and proteins. Our work on virus capsids and biomembranes will involve multi-resolution approaches
including atomistically resolved and coarse-grained models. For virus capsids, coarse-grained models will be
advanced to study uncoating and genome release and software will be developed and distributed for this
purpose. Our work on biomembranes will be supported by the continued development and enhancement of our
BUMPy software for constructing curved membrane systems with biologically inspired shapes. We will also study
protein components of the innate immune system, where we will employ supervised machine learning
approaches to perform classification of simulations from different structural or biochemical states. Using these
approaches will allow us to define new collective variables to compute pathways and energetics of molecular
recognition and activation. For all the proposed studies we will employ advanced multiscale modeling methods
and have existing or identified new experimental collaborators with mutual interest in these systems and
questions, to partner with in these investigations.

## Key facts

- **NIH application ID:** 10330791
- **Project number:** 2R35GM119762-06
- **Recipient organization:** UNIVERSITY OF CONNECTICUT STORRS
- **Principal Investigator:** Eric Robert May
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $442,750
- **Award type:** 2
- **Project period:** 2016-07-15 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10330791

## Citation

> US National Institutes of Health, RePORTER application 10330791, Structural Dynamics of Viral Proteins: Development and Application of Multiscale Computational Methods for Studying Viral Capsids, Proteins and Membrane Systems (2R35GM119762-06). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10330791. Licensed CC0.

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