As a scientist, you have preferred, go-to methods for testing molecular interactions, such as surface plasmon resonance (SPR), Bio-Layer Interferometry (BLI), ELISA, or fluorescence labeling. So, what is the best method to use if you’re measuring a small molecule therapeutic drug interacting with a targeted protein?
One way of characterizing a system is to consider only its inputs and outputs with no concern for its inner workings. This opaque system is called a black box, and black boxes are useful shortcuts when you need fast answers. However, when your input doesn’t give you the correct output, then what do you do? As a scientist, knowing how the black box works and how to modify it can help you have confidence in the results.
Scientists have been dealing with biomolecules for decades, and several strategies have been developed to reduce background signals that contribute to poor assay performance. Even better, new technologies are being released to market that make the most finicky aspects of blocking a thing of the past.
Reference samples can consume sensor cells and precious biomaterial, and any difference between the reference run and experiment run adds inaccuracy to the data. Isn’t getting accurate data hard enough already? Reduce or eliminate bulk responses from solvent effects in your measurements!