Revolutionize your protein stability screening with SUPR-DSF: The Benefits of Detecting Intrinsic Fluorescence

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Differential Scanning Fluorimetry (DSF) is a valuable and widely-used technique that monitors protein unfolding with increasing temperature by detecting changes in fluorescence. However, the conventional workflow for DSF uses extrinsic dyes that may influence the thermal stability of the protein under investigation. This can affect the quality of your data by generating false positives or negatives during screening.

In this webinar, we’ll show you an alternative solution: SUPR-DSF. The SUPR-DSF avoids the use of dyes by using the intrinsic fluorescence of a protein’s tryptophan or tyrosine residues to detect structural changes. This streamlines sample preparation and removes the risks associated with small molecule dyes. It also maintains the high throughput possible with plate-based assays. This not only speeds protein and formulation screening but gives increased confidence in the results.

We will discuss the use of Differential Scanning Fluorimetry (DSF) for assessing protein stability as part of candidate selection and formulation development. We’ll also consider the advantages of using intrinsic protein fluorescence as a detection method for DSF, with particular attention to the simplified workflow of the new SUPR-DSF system.

Join us as we explain how SUPR-DSF could revolutionize your protein stability screening for drug discovery!

Speakers

  • Natalia Markova - Head of Science, Malvern Panalytical
  • Janel Dempsey - Application Scientist, Applied Photophysics

More information

Who should attend? 

Scientists and researchers working on discovery and development of biologics and small molecule drugs and interested in:

  • Construct Screening
  • Formulation Development 
  • Binding Affinities

What will you learn? 

  • Learn how to ensure data quality and increase your drug screening throughput  
  • Discover how to scan up to 384 samples in one single 90-minute experiment
  • Understand why DSF provides complementary data to other techniques