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© 2015. Published by The Company of Biologists Ltd | The Journal of Experimental Biology (2015) 218, 1801-1811 doi:10.1242/jeb.114298
REVIEW
Adaptations of protein structure and function to temperature: there
is more than one way to ‘skin a cat’
Peter A. Fields1, *, Yunwei Dong2, Xianliang Meng3 and George N. Somero4
Sensitivity to temperature helps determine the success of organisms in
all habitats, and is caused by the susceptibility of biochemical
processes, including enzyme function, to temperature change. A
series of studies using two structurally and catalytically related
enzymes, A4-lactate dehydrogenase (A4-LDH) and cytosolic malate
dehydrogenase (cMDH) have been especially valuable in determining
the functional attributes of enzymes most sensitive to temperature, and
identifying amino acid substitutions that lead to changes in those
attributes. The results of these efforts indicate that ligand binding affinity
and catalytic rate are key targets during temperature adaptation: ligand
affinity decreases during cold adaptation to allow more rapid catalysis.
Structural changes causing these functional shifts often comprise only
a single amino acid substitution in an enzyme subunit containing
approximately 330 residues; they occur on the surface of the protein in
or near regions of the enzyme that move during catalysis, but not in the
active site; and they decrease stability in cold-adapted orthologs by
altering intra-molecular hydrogen bonding patterns or interactions with
the solvent. Despite these structure–function insights, we currently are
unable to predict a priori how a particular substitution alters enzyme
function in relation to temperature. A predictive ability of this nature
might allow a proteome-wide survey of adaptation to temperature and
reveal what fraction of the proteome may need to adapt to temperature
changes of the order predicted by global warming models. Approaches
employing algorithms that calculate changes in protein stability in
response to a mutation have the potential to help predict temperature
adaptation in enzymes; however, using examples of temperatureadaptive mutations in A4-LDH and cMDH, we find that the algorithms we
tested currently lack the sensitivity to detect the small changes in
flexibility that are central to enzyme adaptation to temperature.
KEY WORDS: A4-lactate dehydrogenase, Cytosolic malate
dehydrogenase, Protein stability, Temperature adaptation
Introduction
Environmental temperature is of overriding importance in
determining whether ectotherms can survive, grow and reproduce
in a particular habitat. As a result, temperature plays an important
role in determining biogeographic range limits of many organisms,
and therefore the species composition and diversity of communities
(Pörtner, 2002; Somero, 1995; Angilletta, 2009). Underlying this
sensitivity of biological systems to temperature is the impact that
changes in the thermal energy of the environment have on
biochemical and thus physiological processes (Hochachka and
1
Biology Department, Franklin & Marshall College, Lancaster, PA 17603, USA.
State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen
3
361005, China. Yellow Sea Fisheries Research Institute, Chinese Academy of
4
Fishery Sciences, Qingdao 266071, China. Hopkins Marine Station, Department of
Biology, Stanford University, Pacific Grove, CA 93940, USA.
2
*Author for correspondence ( [email protected])
Somero, 2002). The effects of temperature on most chemical
reactions, including those underlying metabolism, are encapsulated
in a simple equation derived by Svante Arrhenius in the latter half of
the nineteenth century:
k ¼ AeEa =ðRT Þ ;
ð1Þ
in which the rate of a reaction, k, increases exponentially with
temperature, T (where R is the universal gas constant, A is a reactionspecific pre-exponential constant and Ea represents the Arrhenius
activation energy of the reaction). Depending on the value of Ea, rates
of most metabolic processes will increase on the order of 2- to 3-fold
with a 10°C increase in environmental temperature, giving rise to the
familiar ‘Q10’ relationship of thermal physiology. And indeed, when
an enzyme is assayed in vitro across a range of temperatures
encompassing the normal physiological temperatures of the organism,
it usually will show the expected exponential increase in reaction rate,
at least until a ‘break point’ is reached and activity begins to decline
due to loss of the protein’s native structure. However, when metabolic
rates of species adapted to different temperatures are compared, the
Arrhenius relationship does not hold; that is, a cold-adapted polar fish
living at 0°C does not have a metabolic rate 20-times lower than that of
a desert lizard living at 40°C. Thus, there must be a compensatory
mechanism available, acting on an evolutionary time scale, that allows
natural selection to alter rates of metabolic reactions as organisms
adapt to new environments. Because most metabolic reactions are
universally shared, physiologists recognized that it was likely the
catalysts themselves – that is, the enzymes – that would be altered
to allow appropriate metabolic rates at different physiological
temperatures.
It was not until the 1960s, however, that experimental evidence
began to accrue showing that enzyme orthologs [that is, enzymes
encoded by a common (homologous) gene from different species]
indeed did display modified function correlating with the
physiological temperatures the species experienced. These early
data consisted mainly of temperatures of maximal enzyme activity
and thermal denaturation temperatures (Tm) (e.g. Licht, 1964;
Vroman and Brown, 1963; Read, 1964). However, work later in the
decade began to show that more functionally relevant kinetic
properties, most notably ligand affinity as measured by apparent
Michaelis–Menten constants (Km), also were modified through
selection to environmental temperatures (e.g. Hochachka and
Somero, 1968).
In the time since these first studies correlating enzyme function
and adaptation temperature, research on temperature adaptation in
enzymes has accelerated and has begun to incorporate a fuller
understanding of the protein structural changes that underlie shifts
in enzyme function. In this review, we describe studies spanning
the past four decades confirming that small differences in habitat
temperature can lead to selection for physiologically significant
changes in enzyme function. We also describe studies that have
pinpointed the location and amount of amino acid change necessary
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ABSTRACT
The Journal of Experimental Biology (2015) 218, 1801-1811 doi:10.1242/jeb.114298
List of symbols and abbreviations
A4-LDH
cMDH
IDH
kcat
Km
Km,NADH
Km,pyr
LDH-A
NADH
PSP
α-HADH
ΔGf
ΔΔG
A4-lactate dehydrogenase (skeletal isoform), tetrameric
cytosolic malate dehydrogenase
isocitrate dehydrogenase
enzyme turnover number
Michaelis–Menten constant
Michaelis–Menten constant for the cofactor NADH
Michaelis–Menten constant for the substrate pyruvate
lactate dehydrogenase-A (skeletal isoform), monomer
nicotinamide adenine dinucleotide, reduced
protein stability prediction
α-hydroxy acid dehydrogenase
Gibbs free energy of protein folding
difference in ΔGf values between two proteins
to lead to these functional changes. Substitutions at multiple sites in
a sequence are shown to equivalently enable adaptive change: there
is more than one way to skin the proverbial cat.
However, despite these advances in our understanding of
structure–function relationships, we currently remain unable to
predict how a particular amino acid change will impact temperature
sensitivity of enzyme function. Such an a priori predictive capacity
would be extremely valuable for conducting proteome-wide surveys
of temperature adaptation. These analyses might reveal how
widespread adaptation is among diverse categories of proteins,
and would be helpful in predicting how much adaptation to
temperature by proteins will be required to allow ectotherms to cope
with global climate change. We thus conclude this review with a
brief description of current bioinformatics approaches to predict
stability changes in enzymes in response to specified amino acid
mutations. Some of these algorithms have explained adaptive
variation in protein stability among species differing very widely in
adaptation temperature (see Gu and Hilser, 2009). However, our
results suggest that bioinformatics approaches may not yet be
sensitive enough to consistently determine how relatively nonperturbing amino acid changes, such as those involved in adaptation
to small differences in habitat temperature, might alter ligand
binding and catalytic rate through modifications of flexibility in
localized areas of protein molecules.
Protein stability, temperature and catalytic function
To understand the necessity of biochemical adaptation in
ectotherms to the thermal habitat each occupies, it is important to
realize that a single enzyme cannot efficiently catalyze reactions
across the entire range of temperatures found in the biosphere (i.e.
−20 to +113°C or more; D’Amico et al., 2003), even if the enzyme
should possess adequate stability to allow its native structure to
persist across this broad range of thermal conditions. This is because
catalysis often requires some amount of motion or rearrangement
of protein structure during binding of ligands, formation of the
catalytic vacuole (the ‘pocket’ in the enzyme where the actual
covalent chemistry occurs) and release of products (Fields and
Somero, 1998; Fields, 2001). Although there are many hundreds of
stabilizing non-covalent interactions (such as hydrophobic
interactions, hydrogen bonds, salt bridges and van der Waals
interactions) in a typical enzyme (Jaenicke, 2000), these stabilizing
forces are counterbalanced by entropic factors, which favor the
unfolding of the secondary and tertiary structures of the enzyme. It
is apparent that most enzyme molecules are under selection to
maintain a balance between stabilization and flexibility – stability so
that the molecule does not unfold and potentially aggregate under
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normal physiological conditions, and flexibility to allow those
motions necessary for catalysis at a rate appropriate to maintain
metabolic flux (Fields, 2001; Somero, 2010).
Temperature is a significant complicating factor in the
maintenance of this enzyme stability–flexibility balance (Feller,
2010). Rising temperatures tend to weaken the stabilizing
interactions responsible for maintaining the native folded state of
the enzyme. Consequently, as temperature increases, enzyme
reaction rates first tend to accelerate, as stabilizing bonds break
and reform more rapidly, and the conformational changes in the
protein necessary for catalysis occur at a faster pace. However, as
temperature continues to rise, enough stabilizing interactions are
disrupted that the enzyme no longer can maintain a conformation
with the three-dimensional geometry required to bind ligands (this
is observed as a rapid increase in Km), and, ultimately, as secondary
and tertiary structure disintegrates, enzyme function is lost. In
contrast, if temperature decreases from the range to which an
enzyme is adapted, the reduced thermal kinetic energy in the
medium leads to over-stabilization of the catalytically mobile
regions of the protein, as weak interactions tend to strengthen. The
enzyme maintains a binding-competent conformation for a greater
proportion of time (seen as a decrease in Km), but because the
motions necessary for catalysis are inhibited, the rate of catalysis
(kcat) also decreases, ultimately to the point that flux through the
pathway can no longer support cellular metabolism. We note that,
whereas hydrophobic interactions may weaken in the cold, such
destabilization seems insufficient to override the increases in
rigidity that arise from cold stabilization of other classes of noncovalent bonds.
Based on the above model of temperature effects on enzyme
function, which was developed in part through the studies described
below, it is apparent that any one enzyme ortholog will not be able
to efficiently catalyze its reaction across the temperature range in
which life is found. Furthermore, this model predicts that enzyme
adaptation to temperature over evolutionary time should involve
amino acid changes that lead to stabilization (in the case of
adaptation to higher temperatures) or destabilization (adaptation to
colder temperatures) in areas of the molecule whose motions are
necessary for catalysis.
A4-lactate dehydrogenase and cytosolic malate
dehydrogenase as models for enzyme adaptation to
temperature
In the past few decades, physiologists and biochemists have used a
wide variety of enzymes from a diversity of taxa to explore the
mechanisms of biochemical adaptation to environmental
temperature. Proteins from bacteria and Archaea, for example,
often have served to elucidate broad patterns in protein adaptation
across wide temperature ranges, and have allowed researchers to
define general criteria for structural adaptation to temperature
(D’Amico et al., 2003; Siddiqui and Cavicchioli, 2006; Feller,
2008, 2010; Gu and Hilser, 2009). In contrast, studies of enzyme
orthologs from closely related species of ectothermic metazoans,
such as phosphoglucose isomerase in butterflies (Watt et al., 2003),
pyruvate kinase in fish (Low and Somero, 1976) and isocitrate
dehydrogenase in mussels (Lockwood and Somero, 2012), have the
potential to reveal the minimum amount of environmental
temperature change necessary to induce modifications in enzyme
function and structure, and what types and magnitudes of amino
acid substitutions are sufficient to allow adaptation to a new thermal
environment. Of these latter types of studies focusing on orthologs
from closely related species, among the most fruitful have been a
The Journal of Experimental Biology
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The Journal of Experimental Biology (2015) 218, 1801-1811 doi:10.1242/jeb.114298
series of studies using A4-lactate dehydrogenase (A4-LDH) and
cytosolic malate dehydrogenase (cMDH). Both of these enzymes
are α-hydroxy acid dehydrogenases (α-HADHs), which catalyze the
reversible reduction or oxidation of the α-carbon of a small,
metabolically essential carboxylic acid (Birktoft et al., 1982).
A4-LDH, the LDH isoform found in skeletal muscle of vertebrates,
is responsible for reducing pyruvate to lactate during bursts
of anaerobiosis, with the cofactor NADH (nicotinamide adenine
dinucleotide, reduced) providing reducing potential. In comparison,
cMDH reduces oxaloacetate to malate in the cytosol (i.e. it is not the
MDH isoform involved in the mitochondrial Krebs cycle) in a
reaction similar to that of LDH; in fact, the main difference in the
reactants is an additional carboxylic acid group in oxaloacetate that
is absent in pyruvate (Dunn et al., 1991). Despite the similarity in
catalytic chemistry between the reactions mediated by these two
enzymes, the proteins themselves are quite dissimilar, at least on the
primary structural (amino acid sequence) level. An amino acid
alignment indicates only 18% of the amino acid residues are
identical between the porcine forms of the two enzymes (Fig. 1).
Interestingly, however, despite the lack of congruence in primary
structure, the two enzymes share significant overlap in the location
of secondary structural features, i.e. β-sheets and α-helices (Fig. 1).
Furthermore, the three-dimensional structures of pig A4-LDH and
cMDH can be superimposed tightly, including the position of each
enzyme’s ligands (Fig. 1), indicating that the process of substrate
reduction or oxidation is catalyzed through the same mechanism,
and with similar conformational rearrangements, in each enzyme.
A
The similarity of catalytic mechanism and higher order structures
between A4-LDH and cMDH, contrasted with the significant
divergence in amino acid sequence, provides a fascinating
opportunity to examine, in two independently evolving proteins,
how natural selection modifies enzyme structure to balance structural
stability and flexibility and allow catalysis at appropriate rates in
differing thermal regimes. In the studies focusing on A4-LDH and
cMDH described below, the comparison between these α-HADHs
has allowed researchers to address the following general questions
regarding enzyme adaptation to relatively slight but physiologically
significant changes in temperature: how much change in amino acid
sequence is needed to produce a physiologically significant change in
function with respect to temperature?; where do amino acid
substitutions occur in the three-dimensional structure of the
molecule?; do independent instances of temperature adaptation, in
both A4-LDH and cMDH, repeatedly exhibit the same types of
structural modifications?; and finally, are we able to predict where,
how many and what types of amino acid changes are needed to adapt
an enzyme to a change in habitat temperature?
Temperature adaptation in teleost A4-LDHs
LDHs, and A4-LDH in particular, have a number of attributes
making them attractive models for the study of structure–function
relationships in enzymes adapting to new thermal habitats. The
enzyme is ubiquitous in vertebrates, and relatively easy to purify
from skeletal muscle; its catalytic chemistry is straightforward and,
as a result, in vitro assays are uncomplicated; and LDH has been
1
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9ldt, chain A
5MDH.pdb, chain A 31 K D Q P I I L V L L
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A
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5MDH.pdb, chain A
B
Fig. 1. Similarity in pig A4-lactate dehydrogenase
and cytosolic malate dehydrogenase secondary
and tertiary structures despite divergence in amino
acid sequences. (A) Structure-based alignment of
amino acid sequences of pig A4-lactate
dehydrogenase (A4-LDH, top; PDB 9LDT) and
cytosolic malate dehydrogenase (cMDH, bottom; PDB
5MDH) (approximately 18% amino acid identity), with
positions of α-helices (orange) and β-sheets (blue)
superimposed. (B) Crystal structures of pig cMDH
(lavender; PDB 5MDH) and A4-LDH (orange; PDB
9LDT) overlaid to illustrate similarity in conformation.
Ligands of cMDH (NADH and the malate analog
α-ketomalonate) are shown in red; ligands of A4-LDH
(NADH and the lactate analog oxamate) are shown in
yellow. UCSF-Chimera (Pettersen et al., 2004) was
used for structure-based alignment and molecular
visualization.
LDH-A/cMDH overlay
Pig (Sus scrofa) cMDH
monomer (PDB 5MDH)
Pig (Sus scrofa) LDH-A
monomer (PDB 9LDT)
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The Journal of Experimental Biology
REVIEW
1804
A
0.5
S. lucasana
S. idiastes
0.4
0.3
0.2
0.1
10
15
20
25
30
10
15
20
B
0.6
C. aceratus
C. ace Q317V
C. ace E233M
G. mirabilis
0.5
0.4
0.3
0.2
0.1
0
5
C
0.6
C. punctipinnis
C. caudalis
C. xanthochira
0.5
0.4
0.3
0.2
0.1
5
10
15
20
25
30
Assay temperature (°C)
35
Fig. 2. Impact of amino acid substitutions on substrate affinity (Km,pyr) in
A4-LDH orthologs from three groups of teleost fishes. (A) N8D is the only
amino acid difference between Sphyraena lucasana (average habitat
temperature: 23°C) and Sphyraena idiastes (18°C) A4-LDHs, and decreases
Km,pyr (increases substrate affinity) in the S. lucasana ortholog at assay
temperatures from 15 to 30°C (data taken from Holland et al., 1997). (B) Q317V
and E233M each decrease Km,pyr in notothenioid (Caenocephalus aceratus;
average habitat temperature <0°C) A4-LDH; E233M is sufficient to bring Km,pyr
values close to those of A4-LDH from Gillichthys mirabilis, a warm–temperate
teleost (∼23°C) (data from Fields and Houseman, 2004). (C) Km,pyr values of
A4-LDH orthologs of temperate damselfish Chromis punctipinnis (16°C) and
the two tropical species (C. caudalis and C. xanthochira; 29°C) (data from
Johns and Somero, 2004). T219A is the only substitution that significantly
decreases Km,pyr (data not shown). Note the varying axis scales in each panel.
Error bars indicate ±1 s.e.
pocket (active site) of the enzyme. At the time, full interpretation of
the effects of this substitution on enzyme function could not be
made because of limited information on A4-LDH’s structural
alterations during binding and catalysis.
Soon after the study by Holland and colleagues, Fields and
Somero, using notothenioid fishes, further described how A4-LDH
temperature sensitivity could be modified by specific amino acid
substitutions (Fields and Somero, 1998). The notothenioids are a
suborder of teleosts (order Perciformes), most of which have
The Journal of Experimental Biology
studied extensively by physical biochemists interested in describing
the detailed relationships between enzyme structure and catalysis
(e.g. Abad-Zapatero et al., 1987; Dunn et al., 1991; Gerstein and
Chothia, 1991). As a result, highly resolved crystal structures of
vertebrate A4-LDHs are available to allow correlation between types
of amino acid substitutions, their locations and their impacts on the
catalytic process.
Work in the late 1960s by Hochachka and Somero (1968), which
examined orthologs from varied teleost species, provided the first
evidence that A4-LDH function could be modified evolutionarily in
response to habitat temperature, and furthermore suggested that
ligand binding affinity (as quantified by binding affinity for the
substrate pyruvate, Km,pyr) was an important and perhaps universal
target of temperature adaptation. A decade later, a foundational
paper by Yancey and Somero confirmed Km,pyr as a key target of
selection, and described the appropriate experimental methods to
accurately assess binding affinity in an enzyme across a range of
temperatures, using an alpha-stat approach to buffer pH (Yancey
and Somero, 1978). Most importantly, the authors examined
A4-LDH function in a broad variety of fish from thermal habitats
as diverse as the Antarctic Ocean and the Amazon River, showing
that Km,pyr remained in a relatively narrow range, from ∼0.15 to
0.35 mmol l−1, when each isoform was assayed within its
physiological temperature range. These results demonstrated that
maintenance of ligand binding affinity in the face of temperature
change is under strong selection, and that orthologs of the A4-LDH
enzyme can accommodate structural changes allowing functional
compensation, at least across a range of temperatures from −2 to
40°C. However, what, where and how extensive these structural
changes might be remained to be elucidated, as did the amount of
environmental temperature change necessary to induce selection to
modify enzyme function. Subsequent studies on three separate
groups of teleost fishes were able to address these questions.
Work by Graves and Somero (1982) was the first to confirm that a
relatively small change in habitat (=adaptation) temperature was
sufficient to alter enzyme function in A4-LDH orthologs from closely
related species. Using members of the genus Sphyraena (barracuda)
from the eastern Pacific Ocean, the authors showed that Km,pyr varied
in a temperature-adaptive manner; that is, values shifted upward in the
more cold-adapted species such that when measured at physiological
temperatures, Km,pyr values were comparable (Graves and Somero,
1982). Because the A4-LDH orthologs were from congeners living in
habitats whose mid-range temperatures only varied by 3–8°C, the
authors inferred that structural differences between the enzymes must
be small. However, at the time the study was performed, determining
protein sequence was a daunting task, limiting the ability of the
authors to associate amino acid changes with the functional changes
they had measured.
A subsequent study, however, extended the work of Graves and
Somero on barracuda A4-LDHs, and for the first time was able to
associate a specific amino acid substitution with a measurable,
significant difference in a temperature-associated functional
attribute, Km,pyr (Holland et al., 1997). In this study, A4-LDHs of
five Sphyraena congeners from differing thermal habitats were
examined, but two orthologs produced especially noteworthy
results. The A4-LDHs of Sphyraena lucasana and Sphyraena
idiastes showed a temperature-adaptive divergence in Km,pyr (i.e.
higher Km,pyr values from the ortholog of S. idiastes, which
occupies the colder habitat) (Fig. 2A), and only a single amino acid
difference, N8D, was found between the orthologs. In a result that
would be repeated in all subsequent studies, the authors found that
the substituted residue was not a component of the ligand-binding
The Journal of Experimental Biology (2015) 218, 1801-1811 doi:10.1242/jeb.114298
Km,pyr (mmol l–1)
REVIEW
adapted to the cold and thermally stable waters surrounding
Antarctica. The Km,pyr values of Antarctic notothenioid orthologs
follow the expected pattern of temperature adaptation, showing
higher values than temperate teleost A4-LDHs at any measurement
temperature (Fig. 2B), which allows these orthologs to maintain
appropriate affinity at the very low temperatures the notothenioids
experience. Additionally, the authors showed that turnover numbers
(kcat) for notothenioid A4-LDHs were higher than those measured
for orthologs from more warm-adapted vertebrates, indicating that
the enzymes had evolved increased flexibility to maintain metabolic
flux at colder temperatures (Fields and Somero, 1998).
Non-conservative amino acid substitutions occurred at eight
positions in the Antarctic notothenioid LDH-A consensus
sequence relative to a consensus of temperate teleost LDH-A,
and subsequent work using expressed recombinant enzymes and
site-directed mutagenesis was able to show that only two of these
substitutions, E233M and Q317V, were responsible for the coldadapted functional attributes of notothenioid A4-LDHs (Fields and
Houseman, 2004). Furthermore, through molecular modeling
techniques, the notothenioid consensus sequence could be
threaded onto a high-resolution three-dimensional LDH-A
structure from dogfish (Protein Data Bank file PDB 6LDH;
Abad-Zapatero et al., 1987); this structural analysis revealed that
these two substitutions were associated with secondary structural
elements that move during the catalytic cycle (Gerstein and
Chothia, 1991). Specifically, two relatively rigid α-helices that
bracket the active site, αG and αH (Fig. 3), must move laterally to
accommodate entry of cofactor and substrate and exit of product;
these movements are rate limiting for the LDH reaction (Gerstein
and Chothia, 1991). From these observations, the authors posited
that temperature-adaptive substitutions were likely to target
regions of the molecule associated with structures surrounding
the active site, which in A4-LDH includes helices αG and αH as
well as an additional ‘major mover’, the catalytic loop (Fig. 3), and
whose motion was necessary for catalysis (Fields and Somero,
1998; Fields and Houseman, 2004). Thus, by increasing the
flexibility of these regions the kcat of the enzyme could be
increased at low temperatures, but at the cost of decreased ligand
affinity.
These conclusions were strengthened and extended in a final
study on teleost A4-LDHs, focusing on a congeneric group of
damselfish (genus Chromis) (Johns and Somero, 2004). The
authors examined orthologs from two tropically distributed
species, Chromis xanthochira and Chromis caudalis, and one
temperate species adapted to significantly cooler temperatures,
Chromis punctipinnis. Again, the expected temperature-adaptive
differences in substrate affinity were found (Fig. 2C), with the coldadapted C. punctipinnis A4-LDH having higher Km,pyr values than
the more warm-adapted orthologs; kcat was higher at each
measurement temperature for the C. punctipinnis ortholog as well.
Johns and Somero found three non-conservative substitutions
between the tropical (C. xanthochira) and temperate orthologs, and
through site-directed mutagenesis were able to show that only one of
these, T219A, was necessary and sufficient to alter pyruvate affinity
from the cold-adapted to the warm-adapted state. Interestingly,
when the authors used molecular modeling techniques to describe
the location of this single substitution within the three-dimensional
structure of the LDH-A monomer, they found that it was associated
with a loop region adjacent to helix αG, one of the two structures
found to be important in temperature adaptation of A4-LDH
function in notothenioids (Fig. 3) (Johns and Somero, 2004). The
combined results of the notothenioid and damselfish A4-LDH
The Journal of Experimental Biology (2015) 218, 1801-1811 doi:10.1242/jeb.114298
A
N8D
Q317V
E233M
T219A
B
Fig. 3. Models of LDH-A showing the locations of temperature-adaptive
substitutions. (A) A single LDH-A monomer is shown with ‘major-mover’
regions (Gerstein and Chothia, 1991) highlighted in red. Amino acid
substitutions in notothenioids that affect Km,pyr (see Fig. 2) (Fields and
Houseman, 2004) are shown in green; the temperature-adaptive substitution in
Chromis species (Johns and Somero, 2004) is shown in pink; the substitution
between Sphyraena idiastes and S. lucasana orthologs (Holland et al., 1997) is
shown in orange. (B) Two monomers of the A4-LDH tetramer, with the
Sphyraena substitution near the N-terminus shown in orange (the other two
monomers of the tetramer are omitted for clarity). This residue has the potential
to hydrogen bond with an Asp residue (green) on a neighboring monomer,
which is part of a β-sheet-loop region ( pink) that leads to and potentially
stabilizes the catalytic major-mover, helix αH (red). Models are based on pig
LDH-A (PDB 9LDT), and were created and visualized with UCSF-Chimera
(Pettersen et al., 2004).
studies suggested that there might be a general pattern to
temperature-adaptive substitutions, at least in this particular
enzyme: substitutions would be solvent exposed, they would
occur in regions of the molecule whose mobility was necessary to
allow catalysis to occur, and, in the direction of cold-to-warm
adaptation, substitutions would replace charged or polar residues
with non-polar residues (i.e. Q317V and E233M in notothenioid
A4-LDHs; T219A in the Chromis ortholog).
Some, but not all of these conclusions are supported in a
retrospective examination of the location of the amino acid
substitution found between A4-LDH orthologs of S. idiastes and
S. lucasana described above (Holland et al., 1997). Position 8 is
close to the N-terminus, on a structure that seems to extend away
from the molecule and shows no intra-molecular interactions when
the LDH-A monomer is examined (Fig. 3A). However, LDH
functions as a tetramer, and when the N8D substitution is visualized
in the context of the multimeric enzyme, it becomes clear that the N1805
The Journal of Experimental Biology
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The Journal of Experimental Biology (2015) 218, 1801-1811 doi:10.1242/jeb.114298
terminus nestles between two neighboring monomers. In fact,
residue 8 is positioned in the homotetramer such that it can
hydrogen bond with residues on a β-sheet from a second monomer
(β-sheet III; Birktoft et al., 1989). β-sheet III anchors an extended
strand leading to helix αH, one of the two ‘major-mover’ regions
shown to be a target of temperature adaptation in the study of
notothenioid A4-LDHs (Gerstein and Chothia, 1991) (Fig. 3B).
Thus, the results of a structural analysis of the barracuda substitution
support the hypotheses that structural changes lead to alterations in
the mobility of key regions of the enzyme, and that these
substitutions occur on the surface rather than in the core of the
enzyme. However, findings from the Sphyraena A4-LDH study fail
to support the hypothesis that temperature adaptation, in the
cold→warm direction, necessarily involves polar/charged→nonpolar substitutions.
As research on temperature adaptation in teleost A4-LDHs
progressed, equally informative work was being conducted on
cMDH orthologs in mollusks. Starting in the early 1990s, evidence
for functional adaptation of the enzyme, in the form of changes to Km
for the cofactor NADH (Km,NADH), was collected using orthologs
from the gastropod mollusk genus Haliotis (abalone) (Dahlhoff and
Somero, 1993). The results of this research were comparable to the
early work on barracuda A4-LDHs (Graves and Somero, 1982):
cMDH Km,NADH values of Haliotis species from higher latitude
(colder) habitats were higher at any assay temperature, but showed
compensation such that Km,NADH values were similar when compared
within each species’ physiological temperature range (Fig. 4A)
(Dahlhoff and Somero, 1993). At the time of the study, however, it
was not possible to determine the amino acid sequences of the five
cMDH orthologs examined, and so the structural changes to the
proteins that underlay the adaptive functional differences were not
determined. We still do not know the amino acid substitutions
responsible for temperature adaptation in Haliotis cMDHs shown in
this early study; this would be a valuable area for future research.
Nevertheless, the work by Dahlhoff and Somero confirmed that
enzyme adaptation to temperature extended beyond the A4-LDHs
already examined in teleosts, that similar patterns of functional
change in enzymes in response to different thermal habitats were
present in highly divergent taxa, and that changes in ligand binding
affinity continued to be a hallmark of biochemical adaptation.
Over a decade later, temperature adaptation in cMDH was
revisited, using a bivalve genus, Mytilus (Fields et al., 2006).
Mytilus trossulus is the blue mussel species native to the eastern
North Pacific Ocean, but over the last century it has begun to be
displaced in the southern portion of its range by Mytilus
galloprovincialis, an invasive species from the Mediterranean
Sea (Geller, 1999). An abundance of evidence, including evidence
on the biochemical and physiological levels, indicates that M.
galloprovincialis is more heat tolerant than M. trossulus (Braby and
Somero, 2006; Tomanek and Zuzow, 2010; Lockwood and
Somero, 2011; Fields et al., 2012), helping to explain the former
species’ invasive success in the warm southern section of the
latter’s range. Fields and colleagues compared cMDHs from
mantle tissue of each congener across a range of temperatures, and
found that the M. trossulus ortholog had higher Km,NADH values,
especially at the highest assay temperatures (Fig. 4B) (Fields et al.,
2006). Furthermore, the authors deduced the amino acid sequence
for each ortholog, and found only a single non-conservative amino
acid difference between the two, V114N (M. trossulus→
M. galloprovincialis). Through site-directed mutagenesis, they
1806
H. kamtschatkana
H. rufescens
50
H. cracherodii
H. corregata
40
H. fulgens
30
20
10
0
0
10
20
30
40
50
30
40
50
B
80
M. trossulus
70
Km,NADH (mmol l–1)
Temperature adaptation in mollusk cMDHs
A
60
M. galloprovincialis
60
V114N
50
40
30
20
10
0
0
40
10
20
C
L. digitalis
L. austrodigitalis
35
30
25
20
15
15
20
25
30
35
Assay temperature (°C)
40
Fig. 4. Impact of amino acid substitutions on cofactor affinity (Km,NADH) in
cMDH orthologs from three groups of mollusks. (A) cMDH orthologs from
five species of Haliotis (abalone) reveal differences in Km,NADH associated
with habitat temperature, Haliotis kamtschatkana (habitat temperature range
4–14°C) and Haliotis rufescens (8–18°C) are more northerly species than
Haliotis cracherodii (12–25°C), Haliotis corregata (12–23°C) or Haliotis fulgens
(14–27°C) (data from Dahlhoff and Somero, 1993). (B) Km,NADH values of cMDH
from the more cold-adapted mussel Mytilus trossulus are higher than those of
M. galloprovincialis. Substitution V114N is sufficient to alter M. trossulus cMDH
Km,NADH values to those of M. galloprovincialis (data from Fields et al., 2006).
(C) There is a single amino acid difference between the cMDHs of Lottia digitalis
and L. austrodigitalis, G291S, which reduces Km,NADH values in the more warmadapted L. austrodigitalis ortholog (data from Dong and Somero, 2009). Note
varying axis scales in each panel. Error bars indicate ±1 s.e.
were able to show that this single amino acid substitution was
sufficient to shift the Km,NADH–temperature relationship of the
cold-adapted M. trossulus ortholog to that of the warm-adapted
M. galloprovincialis (Fig. 4B). Notably, unlike some of the
substitutions found in A4-LDH that shifted from polar/
charged→non-polar in the cold→warm direction, V114N in
Mytilus cMDH represents a non-polar→polar change.
When the location of the mutation at residue 114 was visualized
within the three-dimensional structure of cMDH via molecular
modeling, it was found to associate with neither helix αG nor αH, as
The Journal of Experimental Biology
REVIEW
had been described for temperature-sensitive mutations in A4-LDH;
instead, residue 114 is located at the margin of the region that moves
most during catalysis, the catalytic loop (Fig. 5) (Fields et al., 2006).
Thus, in some aspects, the results of the functional and structural
analysis of Mytilus cMDH confirmed patterns of temperature
adaptation first seen in teleost A4-LDHs – a single amino acid
substitution on the surface of the protein is sufficient to adapt an
ortholog to a new temperature regime, and the substitution is at the
edge or ‘hinge’ of a secondary structural element that must move
significantly during the catalytic cycle. However, the association of
the Mytilus cMDH substitution with the catalytic loop rather than
either of the two helices, αG or αH, as had been found in A4-LDHs,
indicated that there were likely to be many sites within the enzyme
molecule where substitutions could lead to adaptively important
changes in local mobility.
The most recent research examining temperature adaptation in
molluscan cMDHs continues to demonstrate the ubiquity of enzyme
adaptation to even small changes in environmental temperature, at
least in α-HADHs, while expanding the number of regions within
the protein that may be targets of temperature-adaptive selection.
Dong and Somero examined cMDH from foot muscle of six
congeners of the limpet Lottia (class Gastropoda), which occupy
different latitudinal and vertical intertidal ranges along the west
coast of North America (Dong and Somero, 2009). Of particular
interest was one species pair, Lottia digitalis and Lottia
austrodigitalis, which are morphologically very similar [until the
1970s, they were considered a single species (Murphy, 1978)], but
which have relatively discrete biogeographic ranges. Lottia
digitalis, the northern form, is found from the Aleutian Islands
south to Point Conception, while L. austrodigitalis extends from the
Monterey Peninsula to Baja California (Murphy, 1978; Morris et al.,
1980). When comparing cMDH Km,NADH values from foot muscle
of the two species, the authors found the expected signature of
temperature adaptation – lower ligand affinity (higher Km values) at
each assay temperature for the cold-adapted enzyme relative to the
warm-adapted one (Fig. 4C). Not surprisingly, given the close
relatedness of the two species, this functional difference was the
result of only a single amino acid difference between the two
orthologs, G291S (L. digitalis→L. austrodigitalis) (Dong and
Somero, 2009).
When the location of this substitution was visualized via
molecular modeling (Fig. 5), it was found to be far from any of
the major-mover structures (helices αG or αH or the catalytic loop)
that had appeared to be targets for temperature adaptation in earlier
studies, whether of A4-LDH or cMDH. However, a close analysis of
the effects of the replacement of a glycyl with a seryl residue at this
location provides a mechanism for the altered cofactor affinity that
the authors found. Position 291 sits within β-sheet III, adjacent to a
β-turn. In this area, the seryl residue allows the formation of five
hydrogen bonds, which help stabilize the β-sheet. In contrast, when
a glycyl residue is inserted at position 291, as found in the more
cold-adapted L. digitalis cMDH, only two hydrogen bonds are
formed, leading to destabilization of the structure (Dong and
Somero, 2009). As noted above in the context of Sphyraena A4LDH, this β-sheet is attached via a short extended strand to the
catalytically mobile region helix αH (Fig. 5); the replacement of a
seryl by a glycyl residue thus has the potential to increase the
conformational freedom of αH, reducing ligand affinity but
increasing catalytic rate. Thus, despite its physical distance from
the major mover structures within Lottia cMDHs, residue 291 may
still be appropriately positioned to affect the thermodynamics of
catalytic motions in a temperature-adaptive manner. Remarkably,
The Journal of Experimental Biology (2015) 218, 1801-1811 doi:10.1242/jeb.114298
V114N
G291S
Fig. 5. Models of cMDH showing the locations of temperature-adaptive
substitutions. (A) A single cMDH monomer is shown with ‘major-mover’
regions (Gerstein and Chothia, 1991) highlighted in red. The V114N
substitution found between Mytilus congeners borders the catalytic loop, and is
shown in orange. (B) One cMDH monomer with the G291S substitution
(orange) from Lottia orthologs is shown. This residue is a component of the
β-sheet III region that via an extended strand ( pink) leads to and potentially
stabilizes the catalytic major-mover, helix αH (red). Models are based on pig
cMDH (PDB 5MDH), and were created and visualized with UCSF-Chimera
(Pettersen et al., 2004).
despite the significantly different locations of A4-LDH residue 8
and cMDH residue 291 in each monomer, and despite the very
different amino acid compositions between the two α-HADHs
in those regions, the similarity of the locations of these two
substitutions in the multimeric enzymes strengthens the conclusion
that the small β-sheet N-terminal to helix αH can be a target of
temperature adaptation.
Comparing these studies of temperature adaptation in molluscan
cMDH orthologs with research focusing on teleost A4-LDHs allows
us to draw a number of conclusions. First, a small change in habitat
temperature, of the order of a few degrees, repeatedly has been
sufficient to elicit selection for functional changes in the enzymes.
Second, ligand affinity appears to be a universal target of
temperature adaptation in the α-HADHs, with more cold-adapted
enzymes exhibiting lower ligand affinities (higher Km values) at a
common temperature of measurement, but similar affinities to those
of more warm-adapted orthologs when comparisons are made at
physiologically relevant temperatures. We interpret the lower
1807
The Journal of Experimental Biology
REVIEW
REVIEW
A
The Journal of Experimental Biology (2015) 218, 1801-1811 doi:10.1242/jeb.114298
>L_digitalis_cMDH
MSEPVKVLVTGAAGQIAYSLLYSIAKGDVFGE
KQPISLVLLDIEPMMAVLDGVVMELQDCALPL
LADVIPTADESAAFKDIDVALLVGAQPRRQGM
ERKDLLKANVKIFKSQGAALDAHAKKTVKVVV
VGNPANTNALVIAQFAPSIPKENFSCLTRLDQ
NRAQAQVASRLGISNENVQRTIIWGNHSSTQF
PDLAHAVVHVNGKLMPAQEAIKDDDWVKNDFI
KTVQTRGGAVIQARKLSSAMSAAKAICDHVRD
WWFGTGERWVSMGIISKGDYGIKEGLMYSFPV
QIGTDRVVKVVPGLTISNFAREKMDATQAELI
EEKDCAFSFLEA
Existing 3D template
C
Homology detection
and structure matching
Sequence information
Structure information
B
Introduce mutation
e.g. G291S
Fig. 6. Prediction of changes to protein
stability upon mutation, using
bioinformatics tools. Amino acid
sequence information in FASTA format
(L. digitalis cMDH is shown, with residue
291 highlighted in red; A) can be used
directly in some protein stability prediction
algorithms (e.g. I-Mutant 2.0; B) to allow
prediction of ΔΔG. Alternatively, amino acid
sequence information can be used to create
a three-dimensional structure employing
automated homology modeling (e.g. Phyre;
C); the combination of sequence and
structural information can then be used by
PSP algorithms to predict ΔΔG (D).
D
affinity of a cold-adapted ortholog seen at a common temperature of
measurement to be a consequence of an increase in conformational
flexibility; this higher flexibility leads to a smaller fraction of the
population of the cold-adapted enzyme molecules possessing a
binding-competent conformation. However, this higher flexibility
allows faster catalysis at lower temperatures. Third, the structural
changes necessary to elicit these functional differences are small,
usually a single amino acid. And fourth, amino acid substitutions
always occur on the surface of the enzyme, associated directly or
indirectly (via a network of hydrogen bonds) with structures that
must move during catalysis. Nevertheless, despite elucidating these
general rules of enzyme temperature adaptation, at least in αHADHs, the studies described above on A4-LDHs and cMDHs have
not provided us with the ability to predict, a priori, whether an
amino acid substitution at a particular site will lead to a level of
change in conformational flexibility appropriate to a specific level
of environmental temperature change.
Can we predict temperature-sensitive changes in enzyme
function from sequence alone?
A great deal of effort is necessary to accomplish the type of research
described above associating functional changes in enzymes with
their underlying structural modifications. Typically, the workflow
involves measurement of enzyme kinetics across a range of
temperatures from purified enzyme or tissue homogenates,
sequencing of cDNA of each ortholog to identify differences in
amino acid sequence, site-directed mutagenesis and expression of
recombinant protein, and further kinetics assays to confirm that
candidate amino acid substitutions indeed are responsible for the
differences in function that have been ascribed to them. The process
can be time consuming, technically challenging and expensive.
Thus, a valuable addition to research on enzyme adaptation to
temperature would be an in silico approach that would allow us to
predict whether amino acid substitutions between enzyme orthologs
of two species adapted to different temperatures lead to the expected
direction and magnitude of change in a functionally important
attribute like ligand binding affinity. Such a predictive capacity
might allow in silico screening for adaptive change across the
proteome.
In the past 10 years, a number of algorithms have been developed
that may have the potential to allow these types of predictions.
These algorithms are designed to assess how specified amino acid
substitutions will affect global protein stability, specifically by
estimating the Gibbs free energy change as the protein transitions
1808
from the unfolded to the folded state (ΔGf ) – a process that should be
energetically favorable (because the folded state is more stable than
the unfolded state under physiological conditions), leading to a
negative ΔGf value. These calculations are based on a set of rules
determining thermodynamic changes expected as a specified amino
acid sequence folds, and can use sequence data alone or incorporate
information on the three-dimensional structure of the protein. When
a protein with a particular amino acid at position X is compared with
the same protein with a new amino acid at that position, two ΔGf
values can be estimated, one for the ‘wild-type’ and one for
the ‘mutant’ protein, and the difference, ΔΔG, will indicate whether
the amino acid substitution is predicted to stabilize or destabilize the
folded protein. When the mutant is stabilized relative to the wildtype, the mutant ΔGf will have a negative value of greater magnitude
than the ΔGf of the wild-type, resulting in a positive ΔΔG value.
The studies described above focusing on temperature adaptation
in A4-LDHs and cMDHs provide an excellent opportunity to test the
usefulness of these protein stability prediction (PSP) algorithms in
assessing enzyme adaptation to small differences in habitat
temperature. Thus, we can compare six independent examples of
one-amino-acid changes associated with small but physiologically
significant changes in enzyme function (ligand affinity): in A4-LDH
the substitutions (in the cold→warm direction) are N8D
(Sphyraena), E233M and Q317V (notothenioids) and T219A
(Chromis); in cMDH the substitutions are V114N (Mytilus) and
G291S (Lottia).
To test the utility of PSP algorithms for prediction of enzyme
adaptation to small changes in environmental temperature, we used
the six substitutions listed above in four of the many algorithms
available, which use a variety of approaches to predict ΔΔG. Briefly,
algorithms may compute potential energy functions of the proteins
to calculate ΔΔG (for example, physical potentials or statistically
derived potentials), or may utilize machine-learning methods, in
which they are trained using large datasets of proteins for which
mutation–stability data are already available, and then apply the
patterns of stabilization ‘learned’ from these test proteins to novel
mutations. Algorithms can also be divided into those that use amino
acid sequence directly to compute ΔΔG (i.e. not incorporating any
information about the three-dimensional structure of the protein),
versus those that utilize three-dimensional protein models (PDB
files) that can be generated automatically from amino acid
sequences (Fig. 6). There are a number of reviews of PSP
algorithms and their relative performance that provide greater
detail on the variety of algorithms available, and the methods they
The Journal of Experimental Biology
Predict change in protein stability (i.e. value of ΔΔG)
REVIEW
The Journal of Experimental Biology (2015) 218, 1801-1811 doi:10.1242/jeb.114298
R destabilizing | stabilizing r
∆∆G (kcal mol–1)
4
Fig. 7. Prediction of ΔΔG of six mutants of A4-LDH or cMDH
by four protein stability prediction algorithms. Each mutant is
given in the direction cold→warm, which we expect to lead to
molecular stabilization. Positive ΔΔG (1 kcal mol−1=4184 J)
indicates stabilization by the specified mutation, negative ΔΔG
indicates destabilization.
I-Mutant 3.0 (sequence)
I-Mutant 3.0 (structure)
Eris
PoPMuSiC
2
0
–2
-4
L.
d
G igi
29 ta
1S lis
.t
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V1 oss
1 4 ul
N us
M
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33 ratu
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.a
Q cer
31 a
7V tus
C
s
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T2 ncti
19 pin
A ni
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S.
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N ias
8 D te
s
–6
use to calculate ΔΔG (e.g. Thusberg and Vihinen, 2009; Khan and
Vihinen, 2010).
Our goal was to determine whether researchers who were not expert
in the biophysics and thermodynamics of protein folding could use
available PSP algorithms to determine whether a particular mutation
of interest would be temperature adaptive in a given environmental
setting. From the algorithms available, we chose the four used here
because they were web based (no software needed to be downloaded),
and provided both a direction and magnitude of ΔΔG, expressed in
kcal mol−1 (where 1 kcal=4184 J). The algorithms are I-Mutant 3.0
(sequence based) and I-Mutant 3.0 (structure based), which are
support-vector-based (i.e. machine learning) algorithms (Capriotti
et al., 2005, 2008; http://gpcr2.biocomp.unibo.it/cgi/predictors/
I-Mutant3.0/), Eris (structure based), which uses physical potentials
combined with modeling of changes in backbone and side-chain
conformations (Yin et al., 2007; http://troll.med.unc.edu/eris/)
and PoPMuSiC (structure based), which employs a neural network
trained with a database of protein mutants for which stability changes
are known (Dehouck et al., 2009; http://dezyme.com/). For those
algorithms that were able to utilize structural information, we created
molecular models of each of our six protein/mutant pairs using the
automated Phyre server (Kelley and Sternberg, 2009) under default
settings; Phyre accepts amino acid sequences and automatically
searches for appropriate templates from the Protein Data Bank (http://
rcsb.org) upon which to build a three-dimensional model; the new
model is returned to the user as a PDB file.
The results of our analysis suggest that for the six mutations in
α-HADHs for which we have temperature-adaptive functional data,
the PSP algorithms do not uniformly predict stability changes of the
expected direction or magnitude. Fig. 7 shows the ΔΔG values
predicted by the four algorithms for each of the six mutations. In each
case, the mutation is from the cold to the warm ortholog, which we
expected would lead to stabilization and thus a positive ΔΔG. In most
cases, however, ΔΔG was predicted to be negative (i.e. destabilizing),
or very close to zero. Furthermore, in all cases the magnitude of
change was small. To provide context, a single hydrogen bond is
predicted to provide 3–9 kcal mol−1 of stabilization to a protein
(Fersht, 1985), depending on the types of residues and the atoms
involved. Thus, all of the predicted stability changes fall within or
below the stabilization or destabilization energy changes expected
from the loss or gain of a single hydrogen bond (Fig. 7).
Based on the variable results obtained from these PSP algorithms,
and the relatively small ΔΔG values calculated, we conclude that the
algorithms may not be appropriate tools for the analysis of amino
acid substitutions that are involved in adaptation to relatively small
changes in environmental temperature. The conclusion is supported
by a series of observations. First, the set of six amino acid
substitutions tested here may represent relatively non-perturbing
changes to protein structure, as the resulting changes in enzyme
function are relatively minor – they modify catalytic rate and ligand
affinity in response to shifts in habitat temperature of only a few
degrees, corresponding to about a 1% change in thermal kinetic
energy in the medium (Hochachka and Somero, 2002). Second, the
position of each substitution on the surface of the protein, rather than
in the core, supports the claim that the mutations will not have a
large effect on overall (‘global’) protein stability, as it has been
shown that substitutions in the core are more perturbing than those
on the surface. Finally, many of the PSP algorithms are trained or
tested against a database of protein mutation versus stability that
incorporates examples from a wide range of habitat temperatures,
including Archaeal proteins that may be stabilized at temperatures
approaching 100°C (i.e. the ProTherm database; http://www.abren.
net/protherm). Thus, we may be asking the PSP algorithms to detect
changes in protein stability at a resolution that exceeds their
sensitivity. In any case, our data suggest that current PSP algorithms
may not be suitable for the analysis of stability changes in enzymes
adapted to thermal habitats varying by only a few degrees. Instead,
we must continue to rely on laboratory-based approaches to assess
functional differences in orthologs, and associate them with specific
amino acid substitutions.
Conclusions
Studies of LDH-A and cMDH have elucidated several key aspects
of enzyme adaptation to temperature, including the traits most
sensitive to selection, notably ligand binding and catalytic rate, and
the structural alterations required to effect these adaptations in
function. When we place the comparative studies of enzyme
evolution into the broader context of the entire proteome and ask,
‘How much protein evolution to temperature is likely to be required
for ectotherms to cope adequately with relatively small changes in
temperature, such as those expected to occur with on-going global
warming?’ we face the need to examine proteome-wide differences
1809
The Journal of Experimental Biology
Mutation
in thermal properties of proteins. Whereas we currently cannot
determine what proportion of proteins in the proteome must adapt to
increases in temperature of a few degrees Celsius, there are some
data suggesting that not all proteins may be as sensitive to
temperature as LDH-A and cMDH. For example, in comparisons of
orthologous variants of six enzymes in ATP-generating pathways of
the congeneric blue mussels M. galloprovincialis and M. trossulus,
only two enzymes, cMDH (Fields et al., 2006) and isocitrate
dehydrogenase (IDH) (Lockwood and Somero, 2012), showed
adaptive variation. No differences in temperature versus Km
responses were seen for phosphoglucomutase, phosphoglucose
isomerase, phosphoenolpyruvate carboxykinase or pyruvate kinase
(Lockwood and Somero, 2012). For IDH, two amino acid
substitutions that affected flexibility were found, consistent with
work on LDH-A and cMDH that demonstrated that adaptation may
require minimal change in sequence.
The finding that not all enzymes appear to be equally likely to
undergo adaptation to slight differences in temperature makes
proteome-wide comparisons a priority, not only to provide a more
complete account of past evolutionary events but also to develop
predictive models of protein evolution in a warming world. As
sequenced genomes appear in greater numbers, the ability to
deduce sequence differences among orthologous proteins of
differently thermally adapted organisms will concomitantly grow.
If researchers can conduct proteome-wide analyses of evolution of
thermal sensitivities using algorithms employing sequence data,
perhaps in conjunction with models of three-dimensional structures
of the relevant proteins, we will gain the ability to quickly predict
how proteomes must change in response to the rising environmental
temperatures predicted by models of global change.
Acknowledgements
We acknowledge the contributions of other members of ‘Team Dehydrogenase’ –
Elizabeth Dahlhoff, John Graves, George Greaney, Peter Hochachka, Linda
Holland, Glenn Johns, Margaret McFall-Ngai, Joseph Siebenaller and Paul Yancey
– in developing the perspective on protein adaptation presented in this review. We
also acknowledge the intellectual guidance of Bruce Sidell and the opportunity he
provided to study Antarctic notothenioids at the Palmer Research Station.
Competing interests
The authors declare no competing or financial interests.
Author contributions
All authors contributed to the conceptual organization of the paper; P.A.F. drafted the
manuscript and G.N.S., Y.D. and X.M. helped develop the final draft; P.A.F.
performed the bioinformatics analyses.
Funding
Portions of this work were supported by grants from the National Science Foundation
(IOS-0718734 to G.N.S. and MCB02-35686 to P.A.F.) and the Partnership for the
Interdisciplinary Study of the Coastal Oceans (PISCO) sponsored by the David and
Lucile Packard Foundation and the Gordon and Betty Moore Foundation.
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