Tuning Maps for Two-Photon Absorption Cross Sections of Canonical and Non-Canonical Fluorescent Protein Chromophores
Bibliographic record
Abstract
Two-photon absorption (2PA) spectroscopy, which involves the simultaneous absorption of a pair of photons, is an important area of research in biological chromophores. This work offers a perspective on how electrostatic interactions influence the 2PA response of fluorescent protein (FP) chromophores, demonstrating the role of their surrounding environment in modulating 2PA optical properties. In this work, we present electrostatic tuning maps, created by placing partial charges along the Van der Waals radii of eight different chromophores: Green FP (neutral and anion), Red FP (neutral and anion), mBlueberry FP, Kusabira orange FP, and two non-canonical chromophores, a hydroxyquinolone derivative, and the chromophore of Gold FP. 2PA cross sections (σ2PA) were calculated at the CAM-B3LYP(D3BJ)/aug-cc-pVDZ level of theory. The results show that the influence of point charges varies with their location, leading to substantial increases or decreases in the obtained σ2PA. Upon initial investigation, neutral chromophores tend to increase their σ2PA when positive point charges are present close to the imidazolinone group, while lowering their σ2PA when they are present near the benzylidene ring. In contrast, anionic species show an opposite trend. Using a positive or negative point charge has the opposite effect as well, increasing or decreasing σ2PA by relatively the same amount. These maps aid in understanding how electrostatic interactions influence 2PA responses, providing a framework for guiding structural modifications to the protein environment as well as the design and optimization of 2PA probes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".