# Differential Scanning Fluorimetry (DSF) Methods for Studying Protein Stability

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $390,539

## Abstract

Abstract. There is great interest in technologies that measure protein stability, because many devastating
diseases (e.g. cystic fibrosis, Alzheimer’s disease) are linked to protein misfolding and instability. One especially
promising way to treat these diseases is to use small molecules, termed “correctors” that bind to the damaged
protein and partially restore its folding. Multiple correctors have received FDA approval (e.g. ivacaftor, tafamadis,
migalastat), but there are hundreds of additional misfolding diseases. What are the hurdles to the rapid discovery
of additional correctors? One important barrier is that previous correctors have been uncovered through
prolonged searches, using specialized (i.e. target-specific) technologies that are not versatile enough for use
across many proteins-of-interest (POIs). Here, we propose next-generation Differential Scanning Fluorimetry
(DSF) to fill this gap. In a typical DSF experiment, a POI is heated in a qPCR instrument and its un-folding is
monitored by its binding to a solvatochromatic dye (e.g. Sypro Orange, SO). The resulting temperature vs.
fluorescence curves are then used to estimate the melting transition (Tm), with putative correctors identified by
their effect on this value (DTm). DSF is versatile because it does not require protein labeling or structural
knowledge. Moreover, unlike comparable platforms, such as circular dichroism (CD) or differential scanning
calorimetry (DSC), DSF is amenable to 384-well plate format, facilitating large-scale chemical screens. While
DSF has the potential to transform corrector discovery, there are major hurdles to overcome. For example, DSF
often fails because SO does not bind the target protein or it binds to hydrophobic patches on the native state,
obscuring the Tm. Further, for some POIs, the temperature-fluorescence curves are complex, with multiple
transitions, and therefore not readily analyzed or fit using standard equations. Based on our preliminary screens
of ~50 different proteins, these issues cause DSF to fail in more than 60% of cases. We propose to solve these
issues through disruptive innovations: (SA1) Design and synthesis of next-generation dye libraries that
significantly expand the scope of DSF and (SA2) Theory- and experiment-driven, dramatic improvements in data
analysis, enabled by machine learning and made publicly available through a web portal (DSFWorld).
Encouraged by preliminary success, we also propose to: (SA3) Expand the scope of DSF applications by
pioneering studies of multi-protein complexes and conformational changes. Importantly, we will benchmark each
of these innovations against current state-of-the-art approaches, with a focus on a critical understanding of
strengths and weaknesses. Together, these studies are expected to dramatically expand the scope of DSF
technology.

## Key facts

- **NIH application ID:** 10184149
- **Project number:** 1R01GM141299-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Jason E Gestwicki
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $390,539
- **Award type:** 1
- **Project period:** 2021-08-05 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10184149

## Citation

> US National Institutes of Health, RePORTER application 10184149, Differential Scanning Fluorimetry (DSF) Methods for Studying Protein Stability (1R01GM141299-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10184149. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*