# Dissecting the peptide motifs controlling coronavirus infections

> **NIH NIH R21** · LOYOLA UNIVERSITY CHICAGO · 2023 · $222,279

## Abstract

PROJECT SUMMARY/ABSTRACT
This proposal aims to evaluate coronavirus assembly and egress. These late infection stages are
understudied relative to coronavirus entry replication. Additional research is necessary to
reveal how host cell machineries facilitate assembly and egress. Therefore, this proposal
specifically focuses on coronavirus membrane proteins, their interactions with host cell
components, and the relevance of these contacts to efficient virion formation and emergence
from infected cells. Guided by biochemical and protein structural data documenting interfaces
between viral peptide motifs and host coatomer and retromer complexes, we will construct
recombinant murine coronaviruses and corona virus‐like particles with alternative motifs.
Comparisons of recombinant virus infections, along with reductionist approaches assessing the
formation and subcellular transport of virus‐like particles, will reveal how coatomer and
retromer‐sorting nexins operate in controlling viral membrane protein trafficking, virus particle
formation, and particle egress pathways. By expanding the studies to human pathogenic
coronaviruses, we expect to identify commonly utilized host machineries that might be
targeted by antiviral therapeutics.

## Key facts

- **NIH application ID:** 10648391
- **Project number:** 1R21AI176252-01
- **Recipient organization:** LOYOLA UNIVERSITY CHICAGO
- **Principal Investigator:** Thomas Miller Gallagher
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $222,279
- **Award type:** 1
- **Project period:** 2023-08-10 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10648391

## Citation

> US National Institutes of Health, RePORTER application 10648391, Dissecting the peptide motifs controlling coronavirus infections (1R21AI176252-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10648391. Licensed CC0.

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