Evidence map›Paper›PMID 27378087›Full record

ArticleBiological research2016

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

Stefan Simm, Jens Einloft, Oliver Mirus, Enrico Schleiff

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Article in Biological research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed
3.4field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

55 citing papers in PubMed, 124 citations in OpenAlex.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Stefan SimmDepartment of Biosciences, Molecular Cell Biology of Plants, Goethe University, Max von Laue Str. 9, 60438, Frankfurt/Main, Germany.
Jens EinloftMolecular Bioinformatics, Cluster of Excellence Frankfurt "Macromolecular Complexes", Institute of Computer Science, Faculty of Computer Science and Mathematics, Goethe-University Frankfurt, Robert-Mayer-Str. 11-15, 60325, Frankfurt/Main, Germany.
Oliver MirusDepartment of Biosciences, Molecular Cell Biology of Plants, Goethe University, Max von Laue Str. 9, 60438, Frankfurt/Main, Germany.
Enrico SchleiffDepartment of Biosciences, Molecular Cell Biology of Plants, Cluster of Excellence Frankfurt (CEF) and Buchmann Institute of Molecular Life Sciences (BMLS), Goethe University, Max von Laue Str. 9, 60438, Frankfurt/Main, Germany. schleiff@bio.uni-frankfurt.de.
Goethe University Frankfurt · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPhysicochemical 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.

resultsMost 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.

conclusionIt 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.

Indexed as

Hydrophobic and Hydrophilic InteractionsAlgorithmsAmino AcidsAmino Acid SequenceMembrane ProteinsPredictive Value of TestsProtein Conformation, alpha-HelicalProtein Conformation, beta-StrandReference ValuesReproducibility of ResultsTime FactorsWeights and MeasuresAmino AcidsMembrane ProteinsAlternate hydrophobicityAmino acid patternBeta-sheetHydrophobicity scaleTransmembrane helixTransmembrane sheets

Identifiers

PMID27378087
PMCPMC4932767
OpenAlexW2463717668

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.