# OmpG nanopore for single molecule protein sensing

> **NIH NIH R01** · UNIVERSITY OF MASSACHUSETTS AMHERST · 2020 · $306,467

## Abstract

   
DESCRIPTION (provided by applicant): Our central goal is to create a nanopore sensor that can be tuned to specifically detect virtually any of these disease-related protein. The sensor is an engineered form of outer membrane protein G (OmpG) from E. coli. The loops that connect the strands of OmpG's β-barrel are either appended with a ligand or lengthened with a recognition sequence to create the specific sensing elements. Our preliminary results demonstrate that the OmpG nanopore sensor is remarkably sensitive, able to distinguish variants within a mixture of antibodies that were all raised against the same hapten. To expand the utility of the sensor, we will explore the fundamental mechanisms that govern sensor-target interactions. Furthermore, the incorporation of new binding sites within OmpG's loops will proceed by two routes. The first is rational design, where we will incorporate known polypeptide sequences that recognize established targets. The second route takes advantage of OmpG's expression in the E. coli outer membrane. A randomized library of OmpG mutants will be selected for novel target affinity directly from the bacteria using a high- throughput screening and enrichment approach.

## Key facts

- **NIH application ID:** 9901553
- **Project number:** 5R01GM115442-05
- **Recipient organization:** UNIVERSITY OF MASSACHUSETTS AMHERST
- **Principal Investigator:** Min Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $306,467
- **Award type:** 5
- **Project period:** 2016-04-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9901553

## Citation

> US National Institutes of Health, RePORTER application 9901553, OmpG nanopore for single molecule protein sensing (5R01GM115442-05). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9901553. Licensed CC0.

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