# Synthetic adenovirus libraries for vector optimization

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2022 · $368,438

## Abstract

Synthetic adenovirus libraries for vector optimization
Adenoviruses have been extensively utilized to address fundamental questions in biomedical research. These
small DNA viruses rely on numerous host proteins for their own replication, and have been useful probes to
explore the fundamental workings of the mammalian cell. Genetically tractable, recombinant adenoviruses
have become widely used as vectors for gene delivery. Both rational and unbiased methods have been
employed to enhance and modify their functional characteristics. Rational approaches to vector optimization
are constrained by our incomplete understanding of many viral functions, while unbiased methods are
technically limited by the size of the genome and the inherent difficulty of mutagenizing specific genes of
interest. This proposal is focused on the development of a novel platform for the creation of customized
adenovirus libraries. The critical innovation that underlies this project is a recently developed system for the in
vitro assembly of adenovirus genomes from compact modules that can be individually manipulated and then
reassembled. In this project, synthetic methods will be employed to generate focal regions of high diversity
across the genome of human adenovirus 5, the most widely-employed vector serotype. The resulting DNA
sub-libraries will be assembled into complete viral genomes, with each assembly assigned a unique barcode
designed to facilitate library characterization and the tracking of individual mutants. The construction and
characterization of high-content, focal adenoviral libraries, and their utility, will be demonstrated by a simple
screen for mutant viruses that evade recognition by a neutralizing monoclonal antibody. The long term goal of
this project is to develop a resource that can be shared and continually developed to meet the evolving needs
of scientists across many disciplines.

## Key facts

- **NIH application ID:** 10460635
- **Project number:** 5R01GM135485-04
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** FRED BUNZ
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $368,438
- **Award type:** 5
- **Project period:** 2019-09-20 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10460635

## Citation

> US National Institutes of Health, RePORTER application 10460635, Synthetic adenovirus libraries for vector optimization (5R01GM135485-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10460635. Licensed CC0.

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