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Molecular models of human visual pigments: insight into the atomic bases of spectral tuning

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The cycle of vision is a chain of biochemical reactions that occur after exposure of the pigments to the light. The known mechanisms of the transduction of the light pulse derive mainly from studies on bovine rhodopsin. The objective of this work is to construct molecular models of human rhodopsin and opsins, for which threedimensional structures are not available, to analyze the retinal environment and identify the similarities and differences that characterize the human visual pigments. One of the main results of this work is the identification of Glu102 as the probable second counterion of the Schiff base in M opsin (green pigments) and L opsin (red pigments). Further, the analysis of the molecular models allows uncovering the molecular bases of the different absorption maxima of M and L opsins with respect to rhodopsin and S opsin. These differences appear to be due to both an increase in the polarity of the retinal environment and specific electrostatic interactions, which determine a reorganization of the electronic distribution of retinal by selectively stabilizing one of the two resonance forms.
Słowa kluczowe
Rocznik
Strony
141--146
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
  • Department of Sciences, Roma Tre University, Rome, Italy
  • Department of Sciences, Roma Tre University, Viale G. Marconi 446, I-00146 Rome, Italy
  • National Institute of Nuclear Physics, Roma Tre Section, I-00146 Rome, Italy
Bibliografia
  • 1. Yokoyama S. Molecular bases of color vision in vertebrates. Genes Genet Syst 1999;74:189–99.
  • 2. Yokoyama S. Molecular genetic basis of adaptive selection: examples from color vision in vertebrates. Annu Rev Genet 1997;31:315–36.
  • 3. Ebrey T, Koutalos Y. Vertebrate photoreceptors. Prog Ret Eye Res 2001;20:49–94.
  • 4. Saam J, Tajkhorshid E, Hayashi S, Schulten K. Molecular dynamics investigation of primary photoinduced events in the activation of rhodopsin. Biophys J 2002;83:3097–112.
  • 5. Palczewski K. G protein-coupled receptor rhodopsin. Annu Rev Biochem 2006;75:743–67.
  • 6. Nathans J. The evolution and physiology of human color vision: insights from molecular genetic studies of visual pigments. Neuron 1999;24:299–312.
  • 7. Wang W, Geiger JH, Borhan B. The photochemical determinants of color vision: revealing how opsins tune their chromophore’s absorption wavelength. Bioessays 2014;36:65–74.
  • 8. Sakmar TP, Franke RR, Khorana HG. Glutamic acid-113 serves as the retinylidene Schiff base counterion in bovine rhodopsin. Proc Natl Acad Sci USA 1989;86:8309–13.
  • 9. Zhukovsky EA, Oprian DD. Effect of carboxylic acid side chains on the absorption maximum of visual pigments. Science 1989;246:928–30.
  • 10. Nathans J. Determinants of visual pigment absorbance: identification of the retinylidene Schiff’s base counterion in bovine rhodopsin. Biochemistry 1990;29:9746–52.
  • 11. Nathans J. Determinants of visual pigment absorbance: role of charged amino acids in the putative transmembrane segments. Biochemistry 1990;29:937–42.
  • 12. Chan T, Lee M, Sakmar TP. Introduction of hydroxyl-bearing amino acids causes bathochromic spectral shifts in rhodopsin: amino acid substitutions responsible for red- green color pigment spectral tuning. J Biol Chem 1992;267:9478–80.
  • 13. Lamb TD, Collin SP, Pugh EN Jr. Evolution of the vertebrate eye: opsins, photoreceptors, retina and eye cup. Nat Rev Neurosci 2007;8:960–76.
  • 14. Kuwayama S, Imai H, Hirano T, Terakita A, Shichida Y. Conserved proline residue at position 189 in cone visual pigments as a determinant of molecular properties different from rhodopsins. Biochemistry 2002;41:15245–52.
  • 15. Hunt DM, Carvalho LS, Cowing JA, Parry JW, Wilkie SE, Davies WL, et al. Spectral tuning of shortwave-sensitive visual pigments in vertebrates. Photochem Photobiol 2007;83:303–10.
  • 16. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997;25:3389–402.
  • 17. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The Protein Data Bank. Nucleic Acids Res 2000;28:235–42.
  • 18. Palczewski K, Kumasaka T, Hori T, Behnke CA, Motoshima H, Fox BA, et al. Crystal structure of rhodopsin: a G protein-coupled receptor. Science 2000;289:739–45.
  • 19. Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinform 2008;9:40.
  • 20. Roy A, Kucukural A, Zhang Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat Prot 2010;5:725–38.
  • 21. Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER Suite: protein structure and function prediction. Nat Methods 2015;12:7–8.
  • 22. Zhang Y, Skolnick J. Scoring function for automated assessment of protein structure template quality. Proteins 2004;57:702–10.
  • 23. Guex N, Peitsch MC. SWISS-Model and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis 1997;18:2714–23.
  • 24. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera – a visualization system for exploratory research and analysis. J Comput Chem 2004;25:1605–12.
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Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-85cff072-3c24-4345-899b-b5862f5a8e47
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