kinSoftChallenge 2019


So, you just measured the smFRET traces of your life, and now you want to make sense out of the striking single-molecule kinetics you see there?

>> Well, which tool do you choose?

>> Which tool will quantify your kinetics most accurately?

This is currently an unsolved question. We aim to change this with the kinSoftChallenge2019 :

Do you accept the friendly challenge?? Here is how it works.


Single molecule FRET (smFRET) is an precise and popular tool to study the dynamics of biomolecules. It converts nanometer distance changes into modulations of optically measurable fluorescence signals, which are detectable at the single-molecule level. This provides unparalleled insights, e.g. into the functioning of proteins – the molecular machines in the human body.

Observing the movements of one single molecule over time provides the information to disentangle not only reaction velocities, but also the underlying energetic origin of protein function at the (sub-)molecular level. Yet, experimental & fundamental limitations complicate the extraction of this important information. In particular, photo-bleaching of the fluorescent probes limits the observation time per molecule, leading to an extremely narrow bandwidth of typically two orders of magnitude in time. The inherently low signal-to-noise ratio presents an additional challenge.

As a result, smart analysis tools are needed to extract quantitative kinetic information from the experimental raw data. The literature holds several proposed solutions. But they have never been compared side by side in a comparison study, making it virtually impossible for a user to make a rational choice for one software tool. This will change with the kinSoftChallenge2019. Our goals are:

  • to show the world the accuracy of smFRET-based kinetic analysis.
  • to provide the single-molecule (FRET) community a way to judge the different software tools out there.
  • to assess & communicate, which theoretical analysis tricks actually pay off.

We invite all experts and developers of relevant software to join in, and participate in this blind study based on synthetic smFRET data. Thereby, we will obtain solid and comparable results that can serve smFRET researchers as a sound basis to choose the most accurate tool to solve their specific research question. In addition, we hope to trigger further progress in the smFRET kinetics field, which will lower the barrier to use this versatile technique in protein research. Both aspects will produce more accurate, experimental results on protein kinetics, and thus fuel a more detailed understanding of protein function in general.

Example data: protein smFRET trajectories from Schmid et al.