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. Author manuscript; available in PMC: 2023 Feb 20.
Published in final edited form as: Annu Rev Neurosci. 2022 Mar 22;45:273–294. doi: 10.1146/annurev-neuro-110520-031137

Figure 3: Computational modeling to guide sensor optimizations.

Figure 3:

(a) Modeling of effective sensitivity of GRAB5-HT1.0 and iSeroSnFR at resting 5-HT concentration ([5-HT]r) of 200nM, 20nM, 2nM, and 200pM. (b) A 100-fold (green) or greater increase in sensor kd relative to that of the native receptor minimizes ligand buffering effect when sensor and receptor expression is equal (left) and when sensor expression is 10 times higher than receptor expression (right). (c) Left panel: a practical optimization for GRAB5-HT1.0 to maintain or enhance sensitivity while minimizing competition is to increase both the kd and the maximum dynamic range of the parent sensor (blue). According to the model, this can be achieved with a 100-fold increase in kd and a 5-fold increase in maximum dynamic range (green). Right panel: the optimized sensor is more tolerant to changes in [5-HT]r compared to the parent GRAB5-HT1.0.