Computational Prediction of Absorbance Maxima for a Structurally Diverse Series of Engineered Green Fluorescent Protein Chromophores
Bibliographic record
Abstract
By virtue of its self-sufficiency to form a visible wavelength chromophore within the confines of its tertiary structure, the Aequorea victoria green fluorescent protein (GFP) is single-handedly responsible for the ever-growing popularity of fluorescence imaging of recombinant fusion proteins in biological research. Engineered variants of GFP with altered excitation or emission wavelength maxima have helped to expand the range of applications of GFP. The engineering of the GFP variants is usually done empirically by genetic modifications of the chromophore structure and/or its environment in order to find variants with new photophysical properties. The process of identifying improved variants could be greatly facilitated if augmented or guided by computational studies of the chromophore ground and excited-state properties and dynamics. In pursuit of this goal, we now report a thorough investigation of computational methods for prediction of the absorbance maxima for an experimentally validated series of engineered GFP chromophore analogues. The experimental dataset is composed of absorption maxima for 10 chemically distinct GFP chromophore analogues, including a previously unreported Y66D variant, measured under identical denaturing conditions. For each chromophore analogue, excitation energies and oscillator strengths were calculated using configuration interaction with single excitations (CIS), CIS with perturbative correction for double substitutions [CIS(D)], and time-dependent density functional theory (TD DFT) using several density functionals with solvent effects included using a polarizable continuum model. Comparison of the experimental and computational results show generally poor quantitative agreement with all methods attempted. However, good linear correlations between the calculated and experimental excitation energies (R2>0.9) could be obtained. Oscillator strengths obtained with TD DFT using pure density functionals also correlate well with the experimental values. Interestingly, most of the computational methods used in this work fail in the case of nonaromatic Y66S and Y66L protein chromophores, which may be related to a significant contribution of double excitations to their excited-state wavefunctions. These results provide an important benchmark of the reliability of the computational methods as applied to GFP chromophore analogues and lays a foundation for the computational design of GFP variants with improved properties for use in biological imaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".