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Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Phys. Chem. Lett. 2016, 7, 19, 3804-3811
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Geometry, Supertransfer, and Optimality in the Light Harvesting of Purple Bacteria

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† Department of Physics, Sharif University of Technology, Tehran 11155-9161, Iran
‡ Centre for Engineered Quantum Systems and School of Mathematics and Physics, The University of Queensland, Brisbane Queensland 4072, Australia
§ School of Physics, Institute for Research in Fundamental Sciences (IPM), Tehran 19395-5531, Iran
*E-mail: [email protected]
Cite this: J. Phys. Chem. Lett. 2016, 7, 19, 3804–3811
Publication Date (Web):September 9, 2016
https://doi.org/10.1021/acs.jpclett.6b01779
Copyright © 2016 American Chemical Society
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Abstract

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The remarkable rotational symmetry of the photosynthetic antenna complexes of purple bacteria has long been thought to enhance their light harvesting and excitation energy transport. We study the role of symmetry by modeling hypothetical antennas whose symmetry is broken by altering the orientations of the bacteriochlorophyll pigments. We find that in both LH2 and LH1 complexes, symmetry increases energy transfer rates by enabling the cooperative, coherent process of supertransfer. The enhancement is particularly pronounced in the LH1 complex, whose natural geometry outperforms the average randomized geometry by 5.5 standard deviations, the most significant coherence-related enhancement found in a photosynthetic complex.

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This article is cited by 20 publications.

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  16. Harry W. Rathbone, Jeffery A. Davis, Katharine A. Michie, Sophia C. Goodchild, Neil O. Robertson, Paul M. G. Curmi. Coherent phenomena in photosynthetic light harvesting: part two—observations in biological systems. Biophysical Reviews 2018, 10 (5) , 1443-1463. https://doi.org/10.1007/s12551-018-0456-x
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  19. Sotiris Papadatos, Antreas C. Charalambous, Vangelis Daskalakis. A pathway for protective quenching in antenna proteins of Photosystem II. Scientific Reports 2017, 7 (1) https://doi.org/10.1038/s41598-017-02892-w
  20. Florian Häse, Christoph Kreisbeck, Alán Aspuru-Guzik. Machine learning for quantum dynamics: deep learning of excitation energy transfer properties. Chemical Science 2017, 8 (12) , 8419-8426. https://doi.org/10.1039/C7SC03542J

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