# Molecular Assembly of Bacterial Tripartite Multidrug Efflux Pumps

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2024 · $642,890

## Abstract

Abstract
Multidrug efflux pumps actively expel a wide range of toxic substrates across the cell envelope and play a
significant role in both intrinsic and acquired multidrug resistance. Additionally, overproduction of efflux pumps
is associated with virulence in many pathogenic bacteria. The AcrAB-TolC efflux pump is broadly conserved in
gram-negative bacteria and transports many critical antibiotics used worldwide. We will investigate the assembly
process in situ to understand multidrug efflux across two membranes in the context of the cell wall and cellular
membrane environment. In the proposed approach, genetic, biochemical, and structural experiments will be
used synergistically with new computational methods to characterize how efflux pump inhibitors and antibiotics
impact efflux pumps in multidrug-resistant E. coli clinical isolates that overproduce AcrAB. These studies will
generate a comprehensive picture of the tripartite pump assembly sequence, and the role of the cellular
environment in the transport status of active drug efflux. We anticipate the resulting data will help elucidate the
basis for drug resistance in bacterial pathogens and be an important step toward developing new strategies to
block efflux.

## Key facts

- **NIH application ID:** 10978083
- **Project number:** 1R01AI179879-01A1
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Zhao Wang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $642,890
- **Award type:** 1
- **Project period:** 2024-06-01 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10978083

## Citation

> US National Institutes of Health, RePORTER application 10978083, Molecular Assembly of Bacterial Tripartite Multidrug Efflux Pumps (1R01AI179879-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10978083. Licensed CC0.

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