bioRxivBiochemistryPreprint
Engineered blue-shifted melanopsins for subcellular optogenetics
D. Wijayaratna, F. Sacchetta, L. Pedraza Gonzalez, F. Fanelli, T. Sugihara, M. Koyanagi, S. Piyawardana, K. Ghotra, W. Thotamune, A. Terakita, M. Olivucci, A. Karunarathne
Abstract
Melanopsin (MeOp) is a G protein-coupled Receptor (GPCR) family photopigment, expressed in intrinsically photosensitive retinal ganglion cells (ipRGCs) that display remarkable functional diversity. In addition to non-image-forming visual functions, MeOp also controls signaling underlying the retina development, circadian clock, mood, and behavior. MeOp is bistable, recycles retinal, and can function under low retinaldehyde availability. It also activates multiple G protein heterotrimers. Though MeOp could be a versatile optogenetic tool, its potential, especially its utility for subcellular signaling control, is hampered by the broader spectral sensitivity spanning the entire visible range. Here, we use a recently reported in silico technology called Automatic Rhodopsin Modeling (ARM) to identify blue-shifting mutations of MeOp and, ultimately, allow for imaging biosensors with red light without activating the opsin. Accordingly, ARM was used to construct validated quantum mechanics/molecular mechanics (QM/MM) models for mouse MeOp (mMeOp) to search and optimize a set of mutants featuring a blue-shifted light absorption. We demonstrate that four mutants of such can be successfully expressed and display the required resistance to activation by red light; however, they are activated by yellow, green, and blue light. Localized subcellular optical activation of these mutants in macrophage cells showed localized PIP3 generation and cell migration. Further characterization showed that MeOp blue-shifted mutants are also bistable. Altogether, our data demonstrate the computer-aided engineering feasibility of opsins with desired spectral properties for subcellular optogenetic applications.
- organism: mouse
- assay: proteomics
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
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