Effects of enzyme-ligand interactions on the photoisomerization of a light-regulated chemotherapeutic drug
Bibliographic record
Abstract
Molecular photoswitches permit using light to control protein activity with high spatiotemporal resolutions, thereby alleviating the side effects of conventional chemotherapy. However, due to the challenges in probing ultrafast photoisomerization reactions in biological environments, it remains elusive how the protein influences the photochemistry of the photoswitches, which hampers the rational design of light-regulated therapeutics. To overcome this challenge, we employed first-principles non-adiabatic dynamics simulations to characterize the photodynamics of the phototrexate (PTX), a recently developed photoswitchable anticancer chemotherapeutic that reversibly inhibits its target enzyme dihydrofolate reductase (DHFR). Our simulations show that the protein environment impedes the trans to cis photoisomerization of the PTX. The confinement in the ligand-binding cavity slows down the isomerization kinetics and quantum yield of the photoswitch by reshaping its conical intersection, increasing its excited-state free energy barrier, and quenching its local density fluctuations. Therefore, we predict that the PTX’s trans to cis photoisomerization in solution precedes its binding with the protein, despite the favorable binding energy of the trans isomer. Our findings highlight the importance of the protein environment on the photochemical reactions of the molecular photoswitches. As such, our work represents an important step towards the rational design of light-regulated drugs in photopharmacology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".