Apologies for the long delay between updates. I’ve been writing up and editing my work, also spent the last 3 weeks at the CSHL Ion Channel Physiology course. Jeff Diamond, Mark Farrant, Kenton Swartz and Michael Hausser ran a very informative and entertaining program. So, what’s new in FRET sensor land?
Producing useful FRET sensors requires some structural insight, theoretical knowledge, patience to screen many variants, and luck. Making a new sensor that works marginally well is often not that difficult. However, making a reversible sensor with high speed and S/N takes a lot of time and effort. How does a researcher select the protein substrate, linker sequences and fluorescent proteins from the vast space available? A new paper and software from Kevin Truong’s group aims to help guide the researcher towards good candidates.
In the paper, the authors present a simple computational strategy for determining optimal sensor constituants. A PDB file of the sensor substrate is fused to two FP structures via fully flexible linkers. A large number of possible conformations is then sampled, with predicted FRET efficiency calculated for each, then averaged. The authors compared the simulated mean emission ratios between several variations of genetically encoded calcium sensors. In general, there was poor correlation between the simulated emission ratios and experimentally determined ratios across different classes of sensor. However, within sensors grouped by substrate, the change in simulated ratio between variations was qualitatively predictive of the difference in experimentally determined ratios.
They also investigate the effects of various cpVenus substituations into YC2.1. These change the relative orientation of the chromophores and can lead to improved sensors. Again, the changes in simulated ratios were qualitatively predictive of experimentally determined ratios. Interestingly, the simulated ratios only changed from 0.25% to 10%, while experimentally, they varied from 0 to 210%. Although the simulations match the rank order of experimental response, why is there such a disconnect in the magnitude of the change? Perhaps this is due to the flexibility in the modeled linkers.
How useful will this FPMOD software be to developers of FRET sensors? It doesn’t (yet) incorporate linker rigidity, FP dimerization tendency, interactions with endogenous proteins, conformational kinetics or bleaching. Without these factors, will it be useful in predicting those rare “super-responders” like YC3.60, not to mention the current concerns with optimization of in vivo performance? How well does it work for ligands other than calcium?
Luckily, the Truong group has made the software binaries freely available for all of us to download and play with. It would be great to publish the source code to see how it all works. Perhaps as a group, the scientific community could dramatically improve the functionality of the package. Although the GUI front end is clean and functional, I’d also like to see documentation of each parameter, as it is confusing for a newbie like me to figure out how to run the package properly. Just a few more hours work on documentation would yield a great increase in usefulness of the software. Even with these caveats, FPMOD looks like a significant step forward towards rational design of FRET sensors. Any serious FRET sensor developer would be remiss to not carefully read the paper and test how well the software works in his or her system.