Download Tutorial section Hydropathy — A window on the evasion of water

Tutorial section
Hydropathy — A window on
the evasion of water
‘Proteome’ is currently the word of
choice to represent those functioning
polypeptides that result from the
translation of a gene transcript. Only a
small number of the proteins that make
up this group have been fully annotated,
and researchers worldwide are working to
increase this volume. Structure and
function are closely related in terms of
understanding what these proteins do and
how they govern processes within an
organism. In the absence of structural
information derived from X-ray
crystallography or other experimental
means, bioinformatics has sought to make
up the shortfall by enabling calculations of
many parameters that would contribute to
an understanding of the final structure of a
protein. Hydropathy is one such
parameter and is used to determine the
hydrophobicity of various regions of the
molecule.
As a protein is created from the
translation of an mRNA template it is
subject to a variety of forces, enabling it
to fold in a way relevant to its future
function. In addition to the chaperone
proteins known to aid this process,
chemical attractions and interactions
between molecules are all seen to
influence the final product. One of these
forces is the repulsion of water by
various hydrophobic residues on the
emerging polypeptide chain — indeed
many chaperones also function using
hydrophobic interactions. Hydrophilic
residues are necessary to fill the void.
For soluble, globular proteins in
particular, the energy required to bury
hydrophobic residues is one of the main
driving forces behind the folding of the
polypeptide molecule. Once folded,
these hydrophobic interactions are
instrumental in retaining the tertiary
structure of the protein. Those regions
that repel water are the hydrophobic
ones and are calculated in terms of
‘hydropathy’ — the score generated for
a specified region of the protein, taking
both the number of hydrophobic and
the number of hydrophilic residues into
account. Hydrophobicity may be
represented by a positive or a negative
score depending on the experimental
data used to determine it. Such
hydrophobic regions may indicate a
buried core within the protein structure,
or perhaps a feature such as a transmembrane segment.
Computational calculations of the
hydropathic nature of a protein from its
secondary structure may reveal areas of
high hydrophobicity, hinting at possible
structural features. The most common
programs determine the hydrophobicity
of a region based on the calculation of
experimentally determined values for each
residue — the hydropathy index. A
window (a run of adjacent residues within
the sequence) is selected — often by the
user on the basis of the feature that is
being studied — and the hydrophobicity
scores for each residue within that
window added together. Often the
window size for these and many other
programs using this technique is an odd
number of residues. This is to offer an
obvious mid-point within the window for
a dot or plot to be displayed if required.
The resulting window total is then
divided by the number of residues within
the window to create an average (mean).
It is this average that is shown as the
hydropathy score of that region of the
sequence. Once the first hydropathy value
has been registered, the window moves
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along one and an average is calculated
using the second set of adjacent residues,
producing a second hydropathy score.
This process is repeated until the entire
window has moved from the N terminus
to the C terminus, or between the
specified regions of a sequence, creating a
score for each window-sized section of
the protein. Each of these scores is then
plotted on a graph, which is displayed as
the result of the calculation. As a general
rule, the more positive the score, the
greater the hydrophobic content of the
window, however, some hydropathy
indices represent the hydrophobic regions
with a more negative score.
Possibly the most commonly used
hydropathy index is that derived from
experiments by Kyte and Doolittle in
1982.1 Based on a mix of experimental
work and literature searching it was
discovered that protein residues buried
within the fold corresponded well to
regions of hydrophobicity, while those on
the outside of the fold matched more
hydrophilic residues. These observations
were reflected in their hydropathy index
that, owing to the nature of its
conception, is best suited to research
attempting to determine whether residues
will become buried in the final protein
fold. Although the window size can —
and should — be altered depending on
what the program is attempting to
Table 1: Web-based applications useful for predicting hydropathy
Location
Experimental
Default window
size
http://www.rfcgr.mrc.ac.uk/Software/EMBOSS/
EMBOSS application pepwindow
http://www.rfcgr.mrc.ac.uk/Software/EMBOSS/
EMBOSS application pepinfo
Kyte and Doolittle1
7
Kyte and Doolittle1
Sweet and Eisenberg7
Eisenberg et al.3
White and Whimley6
9
Eisenberg et al.3
10
Kyte and Doolittle1
Eisenberg et al.3
Engelman et al.5
Kyte and Doolittle1
Hopp and Woods8
Kyte and Doolittle1
Kyte and Doolittle1
Abraham and Leo9
Black and Mould10
Bull and Breese11
Chothia12
Eisenberg et al.3
Fauchere and Pliska13
Guy14
Hopp and Woods8
Janin15
Kyte and Doolittle1
Manavalan and
Ponnuswamy16
Miyazawa and Jernigen17
Rao and Argos18
Rose et al.19
Roseman20
Sweet and Eisenberg21
Welling et al.22
Wolfenden et al.23
9;6;20
White & Whimley6
19
http://www.rfcgr.mrc.ac.uk/Software/EMBOSS/
EMBOSS application octanol
http://www.rfcgr.mrc.ac.uk/Software/EMBOSS/
EMBOSS application hmoment
GCG application PepPlot24
http://bioinformatics.weizmann.ac.il/hydroph/plot_hydroph.html
http://fasta.bioch.virginia.edu/fasta/grease.htm
http://www.bio.davidson.edu/courses/compbio/flc/home.html
http://us.expasy.org/cgi-bin/protscale.pl?1/
http://blanco.biomol.uci.edu/mpex/
Produces interactive java based graphic. Requires Java Web
Start to launch
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9
User-defined
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characterise, it has been noted by
experimentalists that a region of buried
residues is best detected using a window
size of 9 and a trans-membrane region
with a window size of 19. These are
commonly used as the defaults of
hydropathy programs searching for these
features.
As an indicator of protein folding,
hydropathy analysis has also been
incorporated into some of the tertiary
structure prediction programs.
Discrimination of protein Secondary
structure Class (DSC),2 for example, uses
the Eisenberg3 hydropathy index, while
TopPred4 employs the scale developed by
Engelman and coworkers5 to classify
residues involved in trans-membrane
helices. Such programs have, however,
given way to prediction software that
makes use of newer analysis methods such
as hidden Markov models or neural
networking, where hydropathy is implicit
in the data they use.
Other hydropathy indices rely on
values derived from different experiment,
and it may make sense to employ one of
these depending on the research being
undertaken. White and Wimley,6 for
example, looked at the free energy change
between water–lipid interactions and also
water–octanol interactions of the protein
solution. They suggested that large
differences between them indicate the
presence of a trans-membrane region in
the protein. As this work has been carried
out specifically for this type of feature, it
would be pertinent to use this
information for any projects involving
trans-membrane proteins. There are,
however, a host of other indices derived
from experiments using trans-membrane
proteins.
There are many applications that use
the sliding window technique and a
reference database of experimental
hydrophobicity or hydrophilicity values
to predict the hydropathy of a protein. In
addition to those applications within the
EMBOSS suite,25 others may be found on
web servers across the world. Table 1 lists
some of these.
Lisa J. Mullan,
MRC Rosalind Franklin Centre
for Genome Research,
Genome Campus, Hinxton,
Cambridge CB10 1SB
Tel: +44 (0) 1223 494500
Fax: +44 (0) 1223 494512
E-mail: [email protected]
References
1.
Kyte, J. and Doolittle, R. F. (1982), ‘A simple
method for displaying the hydropathic
character of a protein’, J. Mol. Biol., Vol. 157,
pp. 105–132.
2.
King, R. D. and Sternberg, M. J. E. (1996),
‘Identification and application of the concepts
important for accurate and reliable protein
secondary structure predication’, Protein Sci.,
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3.
Eisenberg, D., Weiss, R. M. and Terwilliger,
T. C. (1984), ‘The hydrophobic moment
detects periodicity in protein hydrophobicity’,
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140–144.
4.
Heijne, G. von (1992), ‘Membrane protein
structure prediction, hydrophobicity analysis
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5.
Engelman, D. M., Steitz, T. A. and Goldman,
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7.
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8.
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11. Bull, H. B. and Breese, K. (1974), ‘Surface
tension of amino acid solutions: A
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residues’, Arch. Biochem. Biophys., Vol. 161,
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