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Quantum–classical simulations of rhodopsin reveal excited-state population splitting and its effects on quantum efficiency


The activation of rhodopsin, the light-sensitive G-protein-coupled receptor responsible for dim-light vision in vertebrates, is driven by an ultrafast excited-state double-bond isomerization with a quantum efficiency of almost 70%. The origin of such light sensitivity is not understood and a key question is whether in-phase nuclear motion controls the quantum efficiency value. In this study we used hundreds of quantum–classical trajectories to show that, 15 fs after light absorption, a degeneracy between the reactive excited state and a neighbouring state causes the splitting of the rhodopsin population into subpopulations. These subpopulations propagate with different velocities and lead to distinct contributions to the quantum efficiency. We also show here that such splitting is modulated by protein electrostatics, thus linking amino acid sequence variations to quantum efficiency modulation. Finally, we discuss how such a linkage that in principle could be exploited to achieve higher quantum efficiencies would simultaneously increase the receptor thermal noise leading to a trade-off that may have played a role in rhodopsin evolution.

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Fig. 1: Theory of Rh photoisomerization.
Fig. 2: Rh population dynamics.
Fig. 3: Analysis of the subpopulation dynamics.
Fig. 4: Analysis of excited-state mixing.
Fig. 5: Opsin electrostatic potential at MinCIS2/S1.
Fig. 6: The mechanism controlling Φcistrans and its possible origin.

Data availability

The authors declare that the data supporting the findings of this study are available within the main article and the Supplementary Information. Cartesian coordinates generated along the trajectories can be found at Source data are provided with this paper.

Code availability

The authors declare that the present research has been produced with distributed software available to the public, as also detailed in the Supplementary Information.


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We thank R. A. Mathies for directing our attention towards the Rh resonance Raman spectrum, B. Chang for suggesting the idea of a trade-off and S. Haacke for helpful discussions. We also thank N. Ferrè for the frequency calculation code in Molcas/Tinker and L. Barneschi for help in generating the plots in Fig. 5. The research was partially supported by the grants NSF CHE-CLP-1710191, NIH GM126627-01 and USIAS 2015, the Ohio Supercomputer Center, the Ministero dell’Istruzione, dell’Università e della Ricerca (MIUR) for a ‘Dipartimento di Eccellenza 2018–2022’, the Fondazione Banca d’Italia (to M.O.), the Interdisciplinary Thematic Institute QMat (as part of the ITI 2021–2028 program of the University of Strasbourg), the CNRS and Inserm via the IdEx Unistra (ANR 10 IDEX 0002), SFRI STRAT’US (ANR 20 SFRI 0012) and Labex NIE (ANR-11-LABX-0058_NIE) projects of the French Investments for the Future Program (to J.L.).

Author information

Authors and Affiliations



M.O. and X.Y. designed the study. X.Y. carried out the molecular dynamics simulations. T.A. contributed to the resonance Raman spectra simulation. X.Y., M.M., J.L. and S.G. analysed the data. M.O. and X.Y. wrote the paper. All authors discussed and commented on the manuscript.

Corresponding author

Correspondence to Massimo Olivucci.

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The authors declare no competing financial interests.

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Peer review information

Nature Chemistry thanks Todd Martinez, Young Min Rhee and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Non-adiabatic coupling evolution.

Non-adiabatic coupling evolution. Time evolution of the magnitude of the S2/S1 NADC modulus (see color legend) along a 3D cut of the S1 PES. The cut is represented by four 2D cross-sections corresponding to different α values and spanning the δop and BLA coordinates.

Source data

Supplementary information

Supplementary information

Supplementary Discussion (Sections 1–17), Tables 1 and 2, Figs. 1–22 and input for GROMACS and Molcas/Tinker molecular dynamics.

Supplementary Data 1

Equilibrium structure of the rhodopsin QM/MM model in Cartesian coordinates.

Source data

Source Data Fig. 2

Source data for Fig. 2a–d.

Source Data Fig. 3

Source data for Fig. 3a–f.

Source Data Fig. 4

Source data for Fig. 4a–e.

Source Data Fig. 5

Source data for Fig. 5a–b.

Source Data Extended Data Fig. 1

Statistical data for Extended Data Fig. 1.

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Yang, X., Manathunga, M., Gozem, S. et al. Quantum–classical simulations of rhodopsin reveal excited-state population splitting and its effects on quantum efficiency. Nat. Chem. 14, 441–449 (2022).

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