Abstract
Multi-photon absorption properties, particularly two-photon absorption (2PA), of fluorescent proteins (FPs) have made them attractive tools in deep-tissue clinical imaging. Although the diversity of photophysical properties for FPs is wide, there are some caveats predominant among the existing FP variants that need to be overcome, such as low quantum yields and small 2PA cross-sections. From a computational perspective, Salem et al. () suggested the inclusion of non-canonical amino acids in the chromophore of the red fluorescent protein DsRed, through the replacement of the tyrosine amino acid. The 2PA properties of these new non-canonical chromophores (nCCs) were determined in vacuum, i.e., without taking into account the protein environment. However, in the computation of response properties, such as 2PA cross-sections, the environment plays an important role. To account for environment and protein–chromophore coupling effects, quantum mechanical/molecular mechanical (QM/MM) schemes can be useful. In this work, the polarizable embedding (PE) model is employed along with time-dependent density functional theory to describe the 2PA properties of a selected set of chromophores made from non-canonical amino acids as they are embedded in the DsRed protein matrix. The objective is to provide insights to determine whether or not the nCCs could be developed and, thus, generate a new class of FPs. Results from this investigation show that within the DsRed environment, the nCC 2PA cross-sections are diminished relative to their values in vacuum. However, further studies toward understanding the 2PA limit of these nCCs using different protein environments are needed.
Graphical Abstract
1. Introduction
The work of Shimomura and co-workers on the bioluminescent crystal jellyfish (Shimomura, ) had among its principal outcomes the discovery of the green fluorescent protein (GFP) (Shimomura et al., ), a barrel-shaped protein in which a chromophore (Shimomura, ; Cody et al., ) is located and responsible for its bright green color. The presence of such a barrel-shaped protein was later discovered not to be exclusive to the crystal jellyfish; indeed, similar fluorescent proteins (FPs) were also found among corals and some other species of the Anthozoa class. FPs of the Anthozoa species exhibit a red-shift in their absorption and emission properties with respect to their GFP homologs, and therefore they were called red fluorescent proteins (RFPs). One example is the DsRed RFP, which is found in the anemone Discosoma striata (Matz et al., ). The chromophore structure in FPs is characterized by an imidazole ring, made from the cyclization of three amino acids, which in the case of DsRed are glutamine 66, tyrosine 67, and glycine 68 (Gln66, Tyr67, and Gly68, respectively). The RFP chromophore structure is shown in Figure 1 (bottom right-hand side corner).
Figure 1
Fluorescent proteins have been used as dyes and clinical markers over the last two decades and their multi-photon absorption properties have allowed them to be applied in deep-tissue clinical imaging at low phototoxicity (Chudakov et al.,
Beyond expanding the color span or improving the features of existing FPs, the persistent efforts on creating novel FPs are motivated by their utility as bioimaging tools (Zhao and Campbell,
One- and two-photon absorption (1PA and 2PA, respectively) properties of FPs and their chromophores have been addressed using different computational tools, mainly quantum mechanical (QM) methods based on time-dependent density functional theory (TD-DFT) (Nifosì et al.,
QM/MM approaches involve sectioning the entire system into different parts (two or more layers) where each part is modeled using a different computational approach. The part of the system where bond-breaking or -forming takes place, or excited-state processes occur, is described through QM methods. The rest of the layer(s) can be treated using an MM model or a less rigorous (computationally less expensive) QM method. QM/MM methods are used to reduce the cost of computations of large systems that would not be feasible to study using pure QM means with current computational resources.
In particular, for the set of nCCs mentioned above, previous attempts of addressing the environment effects on their 2PA properties have been made using the self-consistent reaction field (SCRF) polarizable continuum model (PCM) (Salem et al.,
The local field acting on a chromophore that is embedded in a polarizable environment is generally different from the externally applied field due to the polarization of the environment by the external field. In the PE model, this effect is modeled through the so-called external effective field (EEF) approach (List et al.,
Using the PE model, including local-field effects, we investigated the 1PA and 2PA properties of a selected set of ten nCCs taken from a more complete set previously studied by Salem et al. (
2. Computational Methods
2.1. Modeling the Protein-Chromophore Structures
The main challenge in the construction of nCC-protein model structures is the fact that none of the nCCs shown in Figure 1 have been matured in a red fluorescent-type protein experimentally. Thus, there are no experimental protein crystal structures that can be used either directly or as initial structures for geometry optimization. Only one of the nCCs (no. 20) has been successfully expressed in the gold fluorescent protein and its 1PA properties have been evaluated (Bae et al.,
TagRFP (Merzlyak et al.,
Figure 2

Depiction of how nCC-DsRed (1ZGO) models were created. The native chromophore (CRQ) in the DsRed protein matrix is replaced by the non-canonical chromophore (nCC) model.
The nCC-DsRed structures were optimized using a two-layer ONIOM (Dapprich et al.,
The CAM-B3LYP functional was chosen based on a comparison of the 14-DsRed model optimized using three different functionals, i.e., CAM-B3LYP, ωB97XD (Chai and Head-Gordon,
2.2. Two-Photon Absorption Cross-Section Computations
All computations of 2PA cross-sections were carried out using the Dalton program (Aidas et al.,
where a0 is the Bohr radius, α is the fine structure constant, c0 is the speed of light, Γ is the lifetime broadening factor, which is derived from a Lorentzian function and assumed to be 3.675·10−3 Hartree (or 0.1 eV) to facilitate comparison to experiment (as well as previous computational results), ω is the excitation energy (Hartree/photon), which for 2PA is half the energy difference between the excited and ground states, and δ2PA is the 2PA transition strength. The resulting σ2PA is given in cm2 · s · photon−1 or GM (Göppert-Mayer after Maria Göppert-Mayer) (Mayer Göppert,
The QM region consisted in all cases mainly of the nCC. Compared to previous models (Salem et al.,
Figure 3

Two-layer QM/MM partitioning in each of the nCC-DsRed systems. In the 2PA computations, the QM region includes the chromophore and the neighboring residues serine (red) and phenylalanine (blue), while the classical MM region includes the protein structure only.
Computation of 2PA cross-sections was carried out using the CAM-B3LYP functional, while different Pople basis sets [6-31G(d), 6-31+G(d), and 6-31+G(d,p)] and a segmented polarization-consistent basis set (pcseg-2) Jensen (
Figure 4

Charge redistribution scheme for the N-terminal and C-terminal sides in the nCC-DsRed systems. Atom(s) in blue represent the charge(s) to be redistributed to the atom site(s) in green.
3. Results and Discussion
3.1. Geometry Optimization
The impact of the protein environment on the optimized geometries of the nCCs was determined by comparing each structure obtained using the ONIOM QM/MM CAM-B3LYP/6-31+G(d,p):Amber scheme to the experimental structure of the canonical chromophore in crystal DsRed (Tubbs et al.,
Figure 5

Superposition of nCC 17 structures (a) optimized using PBE0/6-31+G(d,p) Salem et al. (
Figure 6

Tilt (top) and twist (bottom) angle deviations in degrees between structures optimized using CAM-B3LYP/6-31+G(d,p) (in protein), PBE0/6-31+G(d,p) (Salem et al.,
Apart from applying the ONIOM QM/MM method for the optimization of the nCC-DsRed systems, the QM region used here was also larger than the structures optimized by Salem et al. (
Model nCC 20, one of the nCCs with the largest tilt and twist angle deviations from the DsRed crystal structure and 2PA cross-section computed in vacuum (Salem et al.,
3.2. 2PA Cross-Sections
One- and two-photon absorption cross-sections in all nCCs were computed both in vacuum and in protein using PE to model the effects of the protein environment. The QM region in these computations included the chromophore and its two neighboring covalently bonded amino acids, serine and phenylalanine (see Figure 1), whereas the rest of the protein was treated classically. The two charge redistribution schemes depicted in Figure 4 were evaluated using the 14-DsRed model employing CAM-B3LYP and different basis sets [6-31G(d), 6-31+G(d), 6-31+G(d,p), and pcseg-2], in order to establish a suitable approach. The results are provided in Table S2 together with corresponding molecular orbital (MO) plots in Figures S4–S6. For comparison, MO plots of nCC 14 in vacuum are provided in Figure S3.
Using a point-charge redistribution distance of 1.5 Å, results in an unexpected low-intensity transition at around 3.2–3.3 eV, which is most likely due to over-polarization effects. Indeed, an inspection of the MOs (Figure S5) reveals that this is not a relevant transition as the main contribution is from an occupied MO that is not localized on the chromophore. Even the intense transition, which is to the second state, involves a main contribution from an occupied MO that has large components outside of the chromophore. Using instead a redistribution distance of 0.5 Å, we find the expected intense π → π* transition as the lowest state. However, for the small 6-31G(d) basis, we find that the two lowest states are quite close in energy, thus resulting in shared intensity between the two transitions. Adding diffuse functions, i.e., using 6-31+G(d), or using the larger pcseg-2 basis set, increases the separation between the states and thus largely avoids the issue. Comparing the results obtained using 6-31+G(d), 6-31+G(d,p), and pcseg-2, we observe very small differences for the two lowest states, but the third state differs significantly. This is not necessarily an issue, since we are mainly interested in the lowest intense transition. However, it may be an indication of issues with over-polarization or electron spill-out. Nonetheless, it is clear that the point-charge redistribution distance of 0.5 Å is superior in this case and we therefore only include results based on this choice for the following analyses.
To further investigate the role of the basis set, we take a closer look at the MOs. The six highest occupied MOs and six lowest unoccupied MOs of nCC 14 in vacuum and in the protein (14-DsRed) are provided in Figures S3 and S4, respectively. A comparison of the MOs reveals rather large differences, and in particular the unoccupied MOs depend strongly on whether diffuse functions are used or not, and whether they are determined in vacuum or in the protein. For nCC 14 in vacuum, the diffuse functions, which are present in 6-31+G(d) and 6-31+G(d,p), result in Rydberg-like unoccupied MOs, except for the lowest unoccupied MO (LUMO). Such Rydberg-like orbitals would be expected to be much higher in energy when embedded in an environment (if at all present) due to Pauli repulsion. However, since the PE model does not include Pauli repulsion, the use of diffuse functions or large basis sets is not always straightforward. Indeed, for computations in the protein environment, we observe spurious unoccupied MOs when the 6-31+G(d) and 6-31+G(d,p) were used. Similar effects are not observed, at least not to the same degree, for the unoccupied MOs obtained using pcseg-2 or 6-31G(d), which suggests that the diffuse functions have a negative effect on the MOs when the protein is involved.
Typically, the transition of interest in 2PA processes in FPs is to the lowest-lying excited state, S1. The 1PA results for nCC 14 using the functional and basis sets cited above show that this transition is dominated by the highest occupied MO (HOMO) and the LUMO. Therefore, the presence of spurious MOs beyond the HOMO and LUMO might not be considered important and either 6-31+G(d,p) or pcseg-2 can be used. However, the role of the rest of the MOs, especially those with unphysical descriptions, on σ2PA, is unknown. They may be important contributors to σ2PA, as the expression for the 2PA transition moment involves a sum over all excited states, thus, in principle, involving all MOs. The excitation energies, oscillator strengths, and main MO contributions for S1 for all nCCs in vacuum and embedded within the protein matrix are given in Table S3. The excitation to S1 in most of the remaining nCCs, besides nCC 14, also involve mainly the HOMO and LUMO. However, in nCC 19, it is HOMO−1 that dominates, while in nCCs 21 and 22 it is primarily HOMO−2. In these cases, the troublesome scenario discussed above, appears to be present even with the redistribution distance of 0.5 Å. The highest occupied and lowest unoccupied MOs of models 19, 21, and 22 are provided in Figures S7–S9. These cases further emphasize that care must be taken when evaluating σ2PA using the PE model and QM/MM approaches in general, particularly, when diffuse functions or large basis sets are used. Besides possible issues present at the QM/MM interface where bonds have been broken, we also suspect that this is a symptom of over-polarization or electron spill-out because of the proximity of the point-charges surrounding the electronic density.
Two-photon absorption cross-sections for the nCCs in vacuum and embedded in the DsRed protein (nCC-DsRed) are shown in Table 1. For the latter, two different approaches were considered: including or excluding local-field effects (denoted PE(+EEF) and PE(−EEF) respectively).
Table 1
| Vacuum | PE(−EEF) | PE(+EEF) | ||||
|---|---|---|---|---|---|---|
| nCC | 6-31+G(d,p)† | 6-31+G(d,p)§ | pcseg-2§ | 6-31+G(d,p) | 6-31+G(d,p) | pcseg-2 |
| 13 | 19.2 | 59.7 | 56.1 | 23.4 | 5.7 | 5.8 |
| 14 | 19.7 | 58.7 | 55.4 | 23.8 | 5.9 | 5.8 |
| 16a | 17.2 | 32.2 | 31.1 | 10.5 | 2.6 | 2.7 |
| 16b | 15.4 | 48.0 | 46.4 | 6.9 | 1.8 | 1.9 |
| 17 | 21.7 | 67.8 | 64.1 | 63.7 | 16.2 | 16.3 |
| 18 | 29.0 | 88.1 | 83.0 | 55.6 | 14.1 | 16.9 |
| 19 | 20.5 | 76.8 | 71.8 | 12.2 | 3.1 | 3.1 |
| 20 | 43.9 | 85.6 | 82.9 | 43.3 | 10.9 | 15.9 |
| 21 | 15.0 | 70.6 | 67.6 | 4.2 | 1.1 | 1.3 |
| 22 | 3.0 | 6.6 | 6.8 | 7.3 | 2.0 | 1.5 |
Two-photon absorption cross-sections (σ2PA) for all non-canonical chromophores (nCCs) shown in Figure 1 computed using CAM-B3LYP and 6-31+G(d,p) or pcseg-2 for nCCs in vacuum and nCC-DsRed systems (protein with non-canonical chromophore).
For the nCC-DsRed systems, the PE model is used to include the effects from the protein either with effective external field effects [PE(+EEF)] or without [PE(−EEF)]. For comparison, σ2PA results reported by Salem et al. (
Salem et al. (
Results obtained in this work for the isolated chromophores using the QM/MM-optimized geometries.
As discussed in the previous subsection, the chromophore geometries, and in particular the tilt and twist angles, obtained in this work differ from the ones determined by Salem et al. (
Figure 7

Two-photon absorption cross-sections for all non-canonical chromophores shown in Figure 1 computed (†) in vacuum by Salem et al. (
The inclusion of local-field effects through the EEF approach [PE(+EEF)] led to an additional reduction in σ2PA on top of the reduction already induced by the direct electrostatic interactions [PE(−EEF)], in comparison with the values obtained in vacuum. For the computations including EEF effects, the σ2PA's are not affected by the basis set choice to any significant extent. In most cases, the difference between results determined using 6-31+G(d,p) and pcseg-2 is 0.1-0.2 GM, and the largest deviation is 5 GM for nCC 20. Previously, List et al. (
4. Conclusions
The inclusion of protein effects in the geometry optimization of the nCC-DsRed systems studied here suggest that the identity of the substituent R- in the non-canonical chromophore (see Figure 1) does not have a significant impact on the geometry of the chromophore if such optimizations are carried out in vacuum, i.e., without the protein environment. More realistic pictures of the conformation of the non-canonical chromophores studied in this work needed to be addressed by including the protein environment in QM/MM strategies.
Although the nCC-DsRed systems evaluated in this work involve the same computational models of the non-canonical chromophores previously proposed and investigated by Salem et al. (
In this work, the DsRed protein was chosen as the protein host for the set of non-canonical chromophores. Future work could involve the evaluation of 2PA properties of selected nCCs in other RFP hosts and/or a tailored environment, where amino acids surrounding the chromophore can be modified or substituted to tune its 1PA and 2PA properties (List et al.,
Statements
Data availability statement
The datasets generated in this study can be found in the repository Dataset for the article: Two-photon Absorption Cross-sections in Fluorescent Proteins Containing Non-canonical Chromophores Using Polarizable QM/MM (https://doi.org/10.6084/m9.figshare.11886981.v1).
Author contributions
The study was conceived by MR-T and AB. MR-T carried out all simulations under the primary guidance of JO with input from AB. The manuscript was written by MR-T and JO with editorial and scientific contributions from AB.
Funding
Funding employed in the development of the present work includes the NSERC Discovery Grant (Grant #: 2015-05341) given to AB through the Natural Sciences and Engineering Research Council of Canada, Consejo Nacional de Ciencia y Tecnología (CONACYT) PhD scholarship granted to MR-T (Scholarship No. 709745), and the funding provided by the Hylleraas Centre for Quantum Molecular Sciences through their visitors program granted to MR-T, which made this collaboration possible. JO acknowledges financial support from the Research Council of Norway through its Centres of Excellence scheme (Project ID: 262695) and the Norwegian Supercomputing Program (NOTUR) through a grant of computer time (Grant No. NN4654K).
Acknowledgments
The authors would like to thank the Natural Sciences and Engineering Research Council of Canada for supporting this work and to the Hylleraas Centre for having MR-T at UiT The Arctic University of Norway, where this work was principally developed. Special acknowledgment would like to be expressed to Prof. Kenneth Ruud and to Dr. Maarten Beerepoot at UiT for their valuable feedback and the useful discussion held over the composition of this work. Finally, our acknowledgment to Compute/Calcul Canada (www.computecanada.ca), Compute Canada support (support@computecanada.ca), and NOTUR/sigma2 (https://hpc-uit.readthedocs.io/en/latest/) for facilitating all the computational resources and support.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2020.00111/full#supplementary-material
Supplementary Material includes tilt and twist angles computed for the nCC models optimized using the ONIOM scheme, molecular orbitals for nCCs 14, 19, 21, and 22 in vacuum and using the PEM scheme. Excitation energies and oscillator strengths are also provided.
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Summary
Keywords
QM/MM, ONIOM, polarizable embedding, two-photon absorption, chromophores, fluorescent proteins, protein design, non-canonical amino acids
Citation
Rossano-Tapia M, Olsen JMH and Brown A (2020) Two-Photon Absorption Cross-Sections in Fluorescent Proteins Containing Non-canonical Chromophores Using Polarizable QM/MM. Front. Mol. Biosci. 7:111. doi: 10.3389/fmolb.2020.00111
Received
09 March 2020
Accepted
12 March 2020
Published
12 June 2020
Volume
7 - 2020
Edited by
Chong Fang, Oregon State University, United States
Reviewed by
Yong Wang, University of Copenhagen, Denmark; Riccardo Nifosì, National Research Council (CNR), Italy
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© 2020 Rossano-Tapia, Olsen and Brown.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jógvan Magnus Haugaard Olsen jogvan.m.olsen@uit.noAlex Brown alex.brown@ualberta.ca
This article was submitted to Biological Modeling and Simulation, a section of the journal Frontiers in Molecular Biosciences
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