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Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics
Published version of a preprint
Abstract
We engineered light-gated channelrhodopsins (ChRs) whose current strength and light sensitivity enable minimally invasive neuronal circuit interrogation. Current ChR tools applied to the mammalian brain require intracranial surgery for transgene delivery and implantation of fiber-optic cables to produce light-dependent activation of a small volume of tissue. To facilitate expansive optogenetics without the need for invasive implants, our engineering approach leverages the substantial literature of ChR variants to train statistical models for the design of high-performance ChRs. With Gaussian process models trained on a limited experimental set of 102 functionally characterized ChRs, we designed high-photocurrent ChRs with high light sensitivity. Three of these, ChRger1–3, enable optogenetic activation of the nervous system via systemic transgene delivery. ChRger2 enables light-induced neuronal excitation without fiber-optic implantation; that is, this opsin enables transcranial optogenetics. An engineering approach guided by machine learning results in high-performance channelrhodopsin variants that are suitable for systemic viral delivery and illumination through a thinned skull.
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“To demonstrate generality, we applied our same learning paradigm to predict the brightness of fluorescent proteins based on protein sequence features (STAR Methods), potentially enabling an algorithm that can optimize, in silico, the fluorescence of an existing protein design (Bedbrook et al., 2019; Yang et al., 2019).”
Method
Metadata
- Authors
- Bedbrook, C. N., Yang, K. K., Robinson, J. E., Mackey, E. D., Gradinaru, V., & Arnold, F. H.
- Venue
- Nature Methods
- Published
- 2019-10-14
- Volume
- 16
- Issue
- 11
- Pages
- 1176-1184
- ISSN
- 1548-7091
- 1548-7105
- Cited by
- 205
- References
- 50
- Language
- en
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
- Funders
- U.S. Department of Defense (W911NF-09-0001)
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (R01MH117069)
- U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (DP2NS087949)
- U.S. Department of Health & Human Services | NIH | NIH Office of the Director (DP1OD025535, OT2OD023848)
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- Bedbrook, C. N., Yang, K. K., Robinson, J. E., Mackey, E. D., Gradinaru, V., & Arnold, F. H. (2019). Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics. Nature Methods. https://doi.org/10.1038/s41592-019-0583-8
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