Quantum Decoherence of Green Fluorescence Proteins Público

Abstract

Quantum batteries, rooted in quantum mechanics, are revolutionizing energy storage with their superextensive charging capability, which allows them to charge rapidly as their capacity increases. The previous prototype, utilizing an organic microcavity with polaritons as "qubits," faces limitations due to exciton-exciton annihilation. To address this, we propose replacing organic dyes in the microcavity with fluorescent proteins, leveraging their protein cylinder as a "molecular bumper" to mitigate annihilation. For practical battery function, the fluorescent protein-based microcavity must delay quantum decoherence, where the quantum system collapses into classical states due to environmental interactions. Inspired by biological systems with enduring quantum coherence, our research explores how protein and solvent environments influence energy gap fluctuations and quantum decoherence. The hypothesis posits that mutations reducing interactions and collisions can delay quantum decoherence. Using hybrid quantum mechanics-molecular mechanics (QMMM) simulations with both explicit and implicit models, we studied three GFP mutants—citrine, eGFP, and GFP.  

The findings reveal that citrine, with mutations (Q65M and T199Y) introducing π-π interactions and steric clashes, exhibits a shorter estimated decoherence time. In contrast, eGFP shows prolonged coherence, attributed to lower water density surrounding its chromophore. Explicit models also show shorter decoherence times due to dynamic solvent-chromophore interactions. Additionally, we establish a correlation between structural dynamics metrics like RMSD and RMSF and quantum decoherence, emphasizing their value for future rational design. The model is validated with experimental absorption spectra using time-dependent density functional theory (TDDFT). In conclusion, this study enhances our understanding of quantum decoherence in fluorescent proteins, providing a knowledge base for the rational design of quantum batteries. 

Table of Contents

Table of Contents 

Introduction ______________________________________________________________________ 1 

Quantum Battery _________________________________________________________________ 1 

Fig. 1 Quantum battery implemented with an ensemble of qubits coupled with external energy source 7. ______________________________________________________________________ 2 

Quantum Battery Built from Microcavity ______________________________________________ 2 

Fig. 2 Quantum battery prototype implemented with microcavity. ________________________ 3 

Exciton-Exciton Annihilation Necessitates the Use of Fluorescent Protein ____________________ 3 

Fig. 3 Exciton-exciton annihilation and green fluorescent protein. ________________________ 4 

Fig. 4 An innovative quantum battery prototype: fluorescent protein-based microcavity. _______ 5 

Energy Retention Issue – Quantum Decoherence ________________________________________ 5 

Fig. 5 Quantum decoherence on a quantum superposition state of 'heads' and 'tails'. ___________ 5 

Fig. 6 Light-harvesting complexes Fenna−Matthews−Olson (FMO) with multiple chromophores 16. ___________________________________________________________________________ 6 

Fig. 7 Energy gap fluctuation and quantum decoherence.________________________________ 7 

Rational Design of Fluorescent Proteins to Minimize Decoherence __________________________ 7 

Fig. 8 Mutants of GFP and their surrounding environments. _____________________________ 9 

Theory ___________________________________________________________________________ 9 

Quantum Decoherence _____________________________________________________________ 9 

Fig. 9 Math theories behind quantum decoherence represented with Schrödinger’s cat. _______ 11 

Decoherence Time and Energy Gap Fluctuation ________________________________________ 11 

Molecular Dynamics (MD) ________________________________________________________ 12 

Fig. 10 Statistical ensembles in Molecular Dynamics26. ________________________________ 14 

Fig. 11 Explicit versus Implicit solvent models in Molecular Dynamics 26. _________________ 14 

Fig. 12 Periodic boundary condition in Molecular Dynamics 28. _________________________ 15 

Quantum Mechanics−Molecular Mechanics (QM/MM) __________________________________ 15 

Unsupervised Learning in Chemistry Simulation _______________________________________ 17 

Time-Dependent Density Functional Theory (TDDFT) __________________________________ 17 

Computational Details _____________________________________________________________ 18 

Fig. 13 Hybrid quantum mechanics-molecular mechanics (QM/MM) workflow 30. __________ 19 

Protein Preparation _______________________________________________________________ 19 

Table 1. Amino acid mutations in GFP mutants.______________________________________ 19 

Molecular Dynamics (MD) ________________________________________________________ 20 

Quantum Mechanical−Molecular Mechanical (QM/MM) ________________________________ 21 

Simulation Analysis ______________________________________________________________ 21 

Time-Dependent Density Functional Theory (TDDFT) __________________________________ 22 

Results and Discussion _____________________________________________________________ 22 

Simulation Overview _____________________________________________________________ 22 

Fig. 14 Total energy and temperature over time.______________________________________ 23 

Fig. 15 QM region energy and HOMO-LUMO gap over time. __________________________ 24 

Fig. 16 Autocorrelation function of HOMO-LUMO gap. _______________________________ 25 

Larger Energy Gap Fluctuation in Explicit Solvent Model and Citrine ______________________ 25 

Table 2. Mean values and standard deviations of HOMO-LUMO gaps, as well as estimated decoherence times. _____________________________________________________________ 26 

Correlation between Structural Dynamics and Decoherence ______________________________ 26 

Fig. 17 Root mean square deviation (RMSD) analysis. ________________________________ 28 

Higher Structural Dynamics of Individual Residues in Explicit Solvent Models _______________ 28 

Fig 18. Root Mean Square Fluctuation (RMSF) analysis. ______________________________ 29 

Q65M and T199Y in Citrine Increase Protein-Chromophore Contact _______________________ 29 

Fig 19. Native contacts between the protein and the chromophore. _______________________ 31 

Table 3. Mean values and standard deviations of contact distance between chromophore and the protein at selected residues. ______________________________________________________ 31 

Greater Water Density Around GFP _________________________________________________ 31 

Fig. 20 Solvent radial distribution function. _________________________________________ 32 

Dimensionality Reduction and Clustering Analysis of Simulations _________________________ 32 

Fig 21. tICA and clustering analysis of the simulations. ________________________________ 32 

Mismatch between TDDFT Results and Experimental Absorption Spectrum _________________ 32 

Table 4. Experimental absorption spectrum. 32 ______________________________________ 33 

Fig. 22 TDDFT modeled absorption spectrum. _______________________________________ 34 

Conclusion _______________________________________________________________________ 34 

Supplement Code _________________________________________________________________ 39 

About this Honors Thesis

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School	Emory College
Department	Chemistry
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Physics, Quantum Chemistry, Physical
Palabra Clave	Green Fluorescent Protein Quantum mechanics/molecular mechanics Simulation Quantum Decoherence
Committee Chair / Thesis Advisor	Fang Liu, Emory University
Committee Members	Jed Brody, Emory University Francesco Evangelista, Emory University

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