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Predicting peak spectral sensitivities of vertebrate cone visual pigments using atomistic molecular simulations

Overview of attention for article published in PLoS Computational Biology, January 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

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Article details
Title
Predicting peak spectral sensitivities of vertebrate cone visual pigments using atomistic molecular simulations
Published in
PLoS Computational Biology, January 2018
DOI 10.1371/journal.pcbi.1005974
Pubmed ID
Authors
Abstract

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. Sensitivity to individual colors, i.e., peak spectral sensitivities (λmax) of visual pigments, are a function of the type of chromophore and the amino acid sequence of the associated opsin protein in the photoreceptors. Large differences in peak spectral sensitivities can result from minor differences in amino acid sequence of cone opsins. To determine how minor sequence differences could result in large spectral shifts we selected a spectrally-diverse group of 14 teleost Rh2 cone opsins for which sequences and λmax are experimentally known. Classical molecular dynamics simulations were carried out after embedding chromophore-associated homology structures within explicit bilayers and water. These simulations revealed structural features of visual pigments, particularly within the chromophore, that contributed to diverged spectral sensitivities. Statistical tests performed on all the observed structural parameters associated with the chromophore revealed that a two-term, first-order regression model was sufficient to accurately predict λmax over a range of 452-528 nm. The approach was accurate, efficient and simple in that site-by-site molecular modifications or complex quantum mechanics models were not required to predict λmax. These studies identify structural features associated with the chromophore that may explain diverged spectral sensitivities, and provide a platform for future, functionally predictive opsin modeling.

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X Demographics

The data shown below were collected from the profiles of 22 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 10 24%
Researcher 8 19%
Student > Master 7 17%
Student > Bachelor 4 10%
Student > Doctoral Student 3 7%
Other 2 5%
Unknown 8 19%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 13 31%
Biochemistry, Genetics and Molecular Biology 9 21%
Neuroscience 4 10%
Arts and Humanities 1 2%
Mathematics 1 2%
Other 4 10%
Unknown 10 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 June 2018.
All research outputs
#3,754,550
of 34,376,207 outputs
Outputs from PLoS Computational Biology
#2,756
of 10,412 outputs
Outputs of similar age
#64,183
of 494,019 outputs
Outputs of similar age from PLoS Computational Biology
#58
of 153 outputs
Altmetric has tracked 34,376,207 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,412 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.7. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 494,019 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 153 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.