Structural foundations of potassium selectivity in channelrhodopsins
Bibliographic record
Abstract
ABSTRACT Kalium channelrhodopsins (KCRs) are light-gated K + channels recently found in the stramenopile protist Hyphochytrium catenoides . When expressed in neurons, KCRs enable high-precision optical inhibition of spiking (optogenetic silencing). KCRs are capable of discriminating K + from Na + without the conventional K + -selectivity filter found in classical K + channels. The genome of H. catenoides also encodes a third paralog that is more permeable for Na + than for K + . To identify structural motifs responsible for the unusual K + selectivity of KCRs, we systematically analyzed a series of chimeras and mutants of this protein. We found that mutations of three critical residues in the paralog convert its Na + selective channel into a K + selective one. Our characterization of homologous proteins from other protists ( Colponema vietnamica, Cafeteria burkhardae and Chromera velia ) and metagenomic samples confirmed the importance of these residues for K + selectivity. We also show that Trp102 and Asp116, conserved in all three H. catenoides orthologs, are necessary, although not sufficient, for K + selectivity. Our results provide the foundation for further engineering of KCRs for optogenetic needs. IMPORTANCE Recently discovered microbial light-gated ion channels (channelrhodopsins) with a higher permeability for K + than for Na + (kalium channelrhodopsins, or KCRs) demonstrate an alternative K + selectivity mechanism, unrelated to well-characterized “selectivity filters” of voltage- and ligand-gated K + channels. KCRs can be used for optogenetic inhibition of neuronal firing, and potentially for the development of gene therapies to treat neurological and cardiovascular disorders. In this study we identify structural motifs that determine the K + selectivity of KCRs that provide the foundation for that provide the foundation for elucidating their K + selectivity mechanism and for their further engineering as optogenetic tools.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".