A computational tool for designing FRET protein biosensors by rigid-body sampling of their conformational space.

Biosensors relying on the fluorescence resonance energy transfer (FRET) between fluorescent proteins have been used for live-cell imaging of cellular events including Ca(2+) signaling. The efficiency of energy transfer between the donor and acceptor fluorescent proteins depends on the relative distance and orientation between them, which become altered by conformational changes of a fused sensory protein caused by a cellular event. In this way, changes in FRET efficiency of Ca(2+) biosensors can be correlated with Ca(2+) concentrations. The design of these FRET biosensors can be improved by modeling conformational changes before and after a cellular event. Hence, a computational tool called FPMOD was developed to predict FRET efficiency changes by constructing FRET biosensors and sampling their conformational space through rigid-body rotation. We showed with FPMOD that our computational modeling approach can qualitatively predict the FRET efficiencies of a range of biosensors, which had strong agreement with experimental results.