Structural foundations of optogenetics: Determinants of channelrhodopsin ion selectivity
Bibliographic record
Abstract
The structure-guided design of chloride-conducting channelrhodopsins has illuminated mechanisms underlying ion selectivity of this remarkable family of light-activated ion channels. The first generation of chloride-conducting channelrhodopsins, guided in part by development of a structure-informed electrostatic model for pore selectivity, included both the introduction of amino acids with positively charged side chains into the ion conduction pathway and the removal of residues hypothesized to support negatively charged binding sites for cations. Engineered channels indeed became chloride selective, reversing near -65 mV and enabling a new kind of optogenetic inhibition; however, these first-generation chloride-conducting channels displayed small photocurrents and were not tested for optogenetic inhibition of behavior. Here we report the validation and further development of the channelrhodopsin pore model via crystal structure-guided engineering of next-generation light-activated chloride channels (iC++) and a bistable variant (SwiChR++) with net photocurrents increased more than 15-fold under physiological conditions, reversal potential further decreased by another ∼ 15 mV, inhibition of spiking faithfully tracking chloride gradients and intrinsic cell properties, strong expression in vivo, and the initial microbial opsin channel-inhibitor-based control of freely moving behavior. We further show that inhibition by light-gated chloride channels is mediated mainly by shunting effects, which exert optogenetic control much more efficiently than the hyperpolarization induced by light-activated chloride pumps. The design and functional features of these next-generation chloride-conducting channelrhodopsins provide both chronic and acute timescale tools for reversible optogenetic inhibition, confirm fundamental predictions of the ion selectivity model, and further elucidate electrostatic and steric structure-function relationships of the light-gated pore.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".