# Machine-Learning Aided Design of Avidity-Based Biosensors of Ubiquitin Signaling

> **NIH NIH R01** · COLORADO STATE UNIVERSITY · 2024 · $449,628

## Abstract

PROJECT SUMMARY
This proposal is to develop a generalizable method to make genetically-encoded biosensors to monitor the
dynamics, abundance, and positions of specific post-translational modifications (PTMs) of proteins. The strategy
is based on our previous success in generating avidity-driven biosensors for distinct types of ubiquitin (Ub)-
modified nucleosomes; each sensor is comprised of a nucleosome-recognizing Anchor, a Ub-binding UBD
domain, and a Linker developed to maximize affinity and specificity. For Ub-modified proteins in particular,
development of antibodies or other site-specific detection reagents has been notoriously difficult. Our goal is to
extend the avidity-based strategy to engineer sensors able to detect heretofore intractable molecular targets
such as the multiple and functionally distinct Ub signals found on nucleosomes. To accomplish this, our team
will develop rational protein design technology that embraces multi-valent binding with tunable molecular
flexibility. In Aim 1, we will design and test Linkers that deliver tunable domain geometry and motion. We will
adapt new machine-learning algorithms to design fusion proteins that fix constituent Anchor and UBD domains
in conformational space to maximize avidity and specificity. Furthermore, we will explore two new approaches
to expand the Anchor repertoire and broaden applicability of the avidity strategy. In Aim 2, we will adopt the
splitFAST fluorogen system to install a transferrable Anchor-binding tag on the substrate, and In Aim 3 we will
develop customized Anchors from a yeast-display DARPin library. The efficacy and utility of the new sensors will
be evaluated in vitro and in cells where they will be used to probe signaling associated with DNA damage repair
pathways. In Aim 4, we will test and optimize conditions to use the sensors in genomic applications such as
CUT&RUN assays. The reagents we develop in this project will allow researchers to probe otherwise invisible
live-cell processes that are difficult or impossible to image with existing technologies. Our innovative approach
directly addresses the challenge of binding to a highly flexible multi-domain protein target. As such, the resulting
technology and design workflow will find application for diverse ubiquitinated targets, and more generally for
binding targets that would otherwise be inaccessible due to high flexibility.

## Key facts

- **NIH application ID:** 10882725
- **Project number:** 1R01CA283904-01A1
- **Recipient organization:** COLORADO STATE UNIVERSITY
- **Principal Investigator:** Robert Cohen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $449,628
- **Award type:** 1
- **Project period:** 2024-05-01 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10882725

## Citation

> US National Institutes of Health, RePORTER application 10882725, Machine-Learning Aided Design of Avidity-Based Biosensors of Ubiquitin Signaling (1R01CA283904-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10882725. Licensed CC0.

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