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Stylus: A System for Evolutionary Experimentation Based on a Protein/Proteome Model with Non-Arbitrary Functional Constraints

Overview of attention for article published in PLOS ONE, June 2008
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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 (96th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

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2 blogs
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4 X users
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7 Facebook pages
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2 Wikipedia pages
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3 YouTube creators

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30 Mendeley
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Title
Stylus: A System for Evolutionary Experimentation Based on a Protein/Proteome Model with Non-Arbitrary Functional Constraints
Published in
PLOS ONE, June 2008
DOI 10.1371/journal.pone.0002246
Pubmed ID
Authors

Douglas D. Axe, Brendan W. Dixon, Philip Lu

Abstract

The study of protein evolution is complicated by the vast size of protein sequence space, the huge number of possible protein folds, and the extraordinary complexity of the causal relationships between protein sequence, structure, and function. Much simpler model constructs may therefore provide an attractive complement to experimental studies in this area. Lattice models, which have long been useful in studies of protein folding, have found increasing use here. However, while these models incorporate actual sequences and structures (albeit non-biological ones), they incorporate no actual functions--relying instead on largely arbitrary structural criteria as a proxy for function. In view of the central importance of function to evolution, and the impossibility of incorporating real functional constraints without real function, it is important that protein-like models be developed around real structure-function relationships. Here we describe such a model and introduce open-source software that implements it. The model is based on the structure-function relationship in written language, where structures are two-dimensional ink paths and functions are the meanings that result when these paths form legible characters. To capture something like the hierarchical complexity of protein structure, we use the traditional characters of Chinese origin. Twenty coplanar vectors, encoded by base triplets, act like amino acids in building the character forms. This vector-world model captures many aspects of real proteins, including life-size sequences, a life-size structural repertoire, a realistic genetic code, secondary, tertiary, and quaternary structure, structural domains and motifs, operon-like genetic structures, and layered functional complexity up to a level resembling bacterial genomes and proteomes. Stylus is a full-featured implementation of the vector world for Unix systems. To demonstrate the utility of Stylus, we generated a sample set of homologous vector proteins by evolving successive lines from a single starting gene. These homologues show sequence and structure divergence resembling those of natural homologues in many respects, suggesting that the system may be sufficiently life-like for informative comparison to biology.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 7%
United States 2 7%
Spain 2 7%
Netherlands 1 3%
United Kingdom 1 3%
Unknown 22 73%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 40%
Student > Ph. D. Student 3 10%
Other 3 10%
Student > Master 2 7%
Librarian 2 7%
Other 5 17%
Unknown 3 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 47%
Biochemistry, Genetics and Molecular Biology 4 13%
Computer Science 2 7%
Earth and Planetary Sciences 2 7%
Business, Management and Accounting 1 3%
Other 4 13%
Unknown 3 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 25. 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 04 March 2024.
All research outputs
#1,557,559
of 25,804,096 outputs
Outputs from PLOS ONE
#19,206
of 224,989 outputs
Outputs of similar age
#3,795
of 98,519 outputs
Outputs of similar age from PLOS ONE
#52
of 397 outputs
Altmetric has tracked 25,804,096 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 224,989 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.8. This one has done particularly well, scoring higher than 91% 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 98,519 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 397 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.