July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Predicting peak spectral sensitivities of vertebrate cone visual pigments using atomistic molecular simulations
Author Affiliations & Notes
  • Deborah L Stenkamp
    Biological Sciences, University of Idaho, Moscow, Idaho, United States
  • Jagdish S Patel
    Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States
  • Celeste J Brown
    Biological Sciences, University of Idaho, Moscow, Idaho, United States
    Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States
  • F. Marty Ytreberg
    Physics, University of Idaho, Moscow, Idaho, United States
    Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States
  • Footnotes
    Commercial Relationships   Deborah Stenkamp, None; Jagdish Patel, None; Celeste Brown, None; F. Marty Ytreberg, None
  • Footnotes
    Support  NIH R01 EY012146, NIH P20 GM104420
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 3080. doi:
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      Deborah L Stenkamp, Jagdish S Patel, Celeste J Brown, F. Marty Ytreberg; Predicting peak spectral sensitivities of vertebrate cone visual pigments using atomistic molecular simulations. Invest. Ophthalmol. Vis. Sci. 2018;59(9):3080.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Vision is the dominant sensory modality in many organisms for foraging, predator avoidance, and social behaviors including mate selection. Vertebrate visual perception is initiated when light strikes rod and cone photoreceptors within the neural retina of the eye. Peak spectral sensitivities (λmax) of pigments are a function of chromophore type, and the amino acid sequence of the associated opsin protein. For cone pigments, minor sequence differences can result in large differences in λmax. However, with a few minor exceptions (e.g., the “five sites rule” for some LWS pigments), there is currently no method to accurately predict cone pigment λmax directly from sequence data.

Methods : To determine mechanisms underlying λmax differences in similar pigments we selected a spectrally-diverse group of 14 teleost Rh2 pigments for which sequences and λmax are experimentally known. Classical molecular dynamics simulations were performed after embedding chromophore-associated homology structures within explicit bilayers and water. Statistical models were built using structural parameters obtained from the simulations, and were tested for their abilities to predict λmax.

Results : Simulations revealed structural features of pigments, particularly within the chromophore, that contributed to diverged λmax, as well as features that were not strongly predictive. Statistical tests performed on structural parameters associated with the chromophore generated a two-term, first-order regression model, which accurately predicted (R2=0.94) pigment λmax over 452–528 nm. This efficient and simple approach did not require site-by-site molecular modifications or complex quantum mechanics models.

Conclusions : These studies identify structural features associated with the chromophore that explain diverged λmax of cone visual pigments, and provide a platform for functionally predictive pigment modeling. A functionally predictive, genome to phenome approach for vertebrate cone opsins has been elusive for decades, and now carries high potential for future applications in evolutionary biology, biophysics, bio-engineering, and vision science.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

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