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50 years of amino acid hydrophobicity scales: revisiting the capacity for peptide classification

Overview of attention for article published in Biological Research, July 2016
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  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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Title
50 years of amino acid hydrophobicity scales: revisiting the capacity for peptide classification
Published in
Biological Research, July 2016
DOI 10.1186/s40659-016-0092-5
Pubmed ID
Authors

Stefan Simm, Jens Einloft, Oliver Mirus, Enrico Schleiff

Abstract

Physicochemical properties are frequently analyzed to characterize protein-sequences of known and unknown function. Especially the hydrophobicity of amino acids is often used for structural prediction or for the detection of membrane associated or embedded β-sheets and α-helices. For this purpose many scales classifying amino acids according to their physicochemical properties have been defined over the past decades. In parallel, several hydrophobicity parameters have been defined for calculation of peptide properties. We analyzed the performance of separating sequence pools using 98 hydrophobicity scales and five different hydrophobicity parameters, namely the overall hydrophobicity, the hydrophobic moment for detection of the α-helical and β-sheet membrane segments, the alternating hydrophobicity and the exact ß-strand score. Most of the scales are capable of discriminating between transmembrane α-helices and transmembrane β-sheets, but assignment of peptides to pools of soluble peptides of different secondary structures is not achieved at the same quality. The separation capacity as measure of the discrimination between different structural elements is best by using the five different hydrophobicity parameters, but addition of the alternating hydrophobicity does not provide a large benefit. An in silico evolutionary approach shows that scales have limitation in separation capacity with a maximal threshold of 0.6 in general. We observed that scales derived from the evolutionary approach performed best in separating the different peptide pools when values for arginine and tyrosine were largely distinct from the value of glutamate. Finally, the separation of secondary structure pools via hydrophobicity can be supported by specific detectable patterns of four amino acids. It could be assumed that the quality of separation capacity of a certain scale depends on the spacing of the hydrophobicity value of certain amino acids. Irrespective of the wealth of hydrophobicity scales a scale separating all different kinds of secondary structures or between soluble and transmembrane peptides does not exist reflecting that properties other than hydrophobicity affect secondary structure formation as well. Nevertheless, application of hydrophobicity scales allows distinguishing between peptides with transmembrane α-helices and β-sheets. Furthermore, the overall separation capacity score of 0.6 using different hydrophobicity parameters could be assisted by pattern search on the protein sequence level for specific peptides with a length of four amino acids.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 137 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 <1%
Unknown 136 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 20%
Student > Bachelor 19 14%
Student > Master 17 12%
Researcher 15 11%
Student > Doctoral Student 6 4%
Other 18 13%
Unknown 34 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 39 28%
Chemistry 21 15%
Agricultural and Biological Sciences 16 12%
Physics and Astronomy 6 4%
Computer Science 3 2%
Other 10 7%
Unknown 42 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 06 September 2022.
All research outputs
#7,968,106
of 25,394,764 outputs
Outputs from Biological Research
#92
of 642 outputs
Outputs of similar age
#123,196
of 369,888 outputs
Outputs of similar age from Biological Research
#2
of 8 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 642 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done well, scoring higher than 84% 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 369,888 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.