# Directed evolution of broadly fungible biosensors

> **NIH NIH R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2024 · $314,487

## Abstract

Abstract
The development of new protein biosensors has for the most part been dependent on finding a protein that is
already responsive to a known effector. While rational design and directed evolution methods exist for altering
the effector specificity of transcription factors, these methods are in general complex and slow, and have failed
to solve the more general problem of identifying new protein biosensors at will. In particular, it is often difficult to
find a receptor that is both sensitive and specific for a given end product or intermediate, and even when efforts
to generate new sensors are successful, they generally recognize effectors that are structurally quite similar to
their natural counterparts. In particular, for virtually all industrially and medically useful terpenes there exists no
corresponding biosensor. We now propose to develop a combined computational and directed evolution method
that should allow us to proceed from any of a wide variety of ‘generalist’ repressors to create highly sensitive
and specific biosensors for a structurally diverse range of terpenes and terpenoids for which no biosensors are
currently known. To this end, we have developed a novel directed evolution method for altering biosensor
specificities, and propose to synergize these with powerful machine learning tools for improving protein function.
Extensive Preliminary Results show that the TetR family of transcription factors can be readily manipulated to
take on new effector specificities, and that machine learning can be used to improve the function of a wide variety
of proteins. We now further propose to identify semi-specific transcription factors as starting points for biosensor
design and evolution (Aim 1); use neural network approaches to predict new sensor specificities (Aim 2); and
refine these predictions via directed evolution and high-throughput screening (Aim 3).

## Key facts

- **NIH application ID:** 10757936
- **Project number:** 5R01GM146093-02
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Andrew D Ellington
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $314,487
- **Award type:** 5
- **Project period:** 2023-01-05 → 2026-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10757936

## Citation

> US National Institutes of Health, RePORTER application 10757936, Directed evolution of broadly fungible biosensors (5R01GM146093-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10757936. Licensed CC0.

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