# Retrotransposon virus-like particle assembly

> **NIH NIH F32** · UNIVERSITY OF GEORGIA · 2021 · $68,562

## Abstract

PROJECT SUMMARY/ABSTRACT
Retrotransposons and retroviruses shape genome evolution and impact host fitness. Over 40% of the human
genome is comprised of mobile DNA elements. These elements, once considered dormant, molecular fossils,
are in fact remarkably influential across a diverse array of medically-relevant processes. Retroelements and
domesticated components of mobile elements play important roles in neurodegeneration, cancer, pluripotent
cells, innate immunity, and synaptic signaling. The retrovirus-like transposon Ty1 is an informative model for
understanding mechanisms employed by retrotransposons and retroviruses across their replication cycles. This
proposal leverages the genetic, biochemical, and cell biological toolkit developed for studying Ty1 and combines
innovative conceptual frameworks from retrovirology, biophysics, protein biology, and cell biology. The proposed
project will identify parameters governing Ty1 Gag’s role in virus-like particle assembly. This will advance our
understanding of retroelement replication cycles and establish mechanisms that may be utilized by other
disease-relevant retrotransposons, retroviruses, and domesticated capsid genes. In addition, identification of
novel mechanisms used by retroelements has the potential to define a new class of antiviral drug targets.

## Key facts

- **NIH application ID:** 10241947
- **Project number:** 5F32GM139247-02
- **Recipient organization:** UNIVERSITY OF GEORGIA
- **Principal Investigator:** Sean Beckwith
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $68,562
- **Award type:** 5
- **Project period:** 2020-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10241947

## Citation

> US National Institutes of Health, RePORTER application 10241947, Retrotransposon virus-like particle assembly (5F32GM139247-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10241947. Licensed CC0.

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