Discovery of Light-Responsive Ligands through Screening of a Light-Responsive Genetically Encoded Library
Bibliographic record
Abstract
Light-responsive ligands are useful tools in biochemistry and cell biology because the function of these ligands can be spatially and temporally controlled. Conventional design of such ligands relies on previously available data about the structure of both the ligand and the receptor. In this paper, we describe de novo discovery of light-responsive ligands through screening of a genetically encoded light-responsive library. We ligated a photoresponsive azobenzene core to a random CX7C peptide library displayed on the coat protein of M13 phage. A one-pot alkylation/reduction of the cysteines yielded a photoresponsive library of random heptapeptide macrocycles with over 2 × 10(8) members. We characterized the reaction on-phage and optimized the yield of the modifications in phage libraries. Screening of the library against streptavidin yielded three macrocycles that bind to streptavidin in the dark and cease binding upon irradiation with 370 nm light. All ligands restored their binding properties upon thermal relaxation and could be turned ON and OFF for several cycles. We measured dissociation constants, Kd, by electrospray ionization mass spectrometry (ESI-MS) binding assay. For ligand ACGFERERTCG, the Kd of cis and trans isomers differed by 22-fold; an incomplete isomerization (85%), however, resulted in the apparent difference of 4.5-fold between the dark and the irradiated state. We anticipate that the selection strategy described in this report can be used to find light-responsive ligands for many targets that do not have known natural ligands.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 0.001 |
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".