Machine learning-guided channelrhodopsin engineering enables minimally-invasive optogenetics
By Claire N. Bedbrook, Kevin Kaichuang Yang, J. Robinson, Elisha D. W. Mackey, Viviana Gradinaru, Frances H. Arnold. 2019
181 citations recorded by the source corpus.
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.
Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience
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Citations in the corpus
- Machine-learning-guided directed evolution for protein engineering (2018, methodology): “In this approach, Fox et al. randomly recombine mutations previously identified through a classical technique such as random or site-directed mutagenesis 13 .”
- Machine learning in protein engineering (2018, methodology): “…have been combined with a structure-guided recombination library to optimize cytochrome P450 thermostability (Romero et al., 2013b), channelrhodopsin localization to mammalian cell membranes (Bedbrook et al., 2017), and channelrhodopsin light-activated conductance (Bedbrook et al., 2018).”