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Structure- and sequence-analysis inspired engineering ofproteins for enhanced thermostabilityHein J Wijma, Robert J Floor and Dick B Janssen
Available online at www.sciencedirect.com
Protein engineering strategies for increasing stability can be
improved by replacing random mutagenesis and high-
throughput screening by approaches that include
bioinformatics and computational design. Mutations can be
focused on regions in the structure that are most flexible and
involved in the early steps of thermal unfolding. Sequence
analysis can often predict the position and nature of stabilizing
mutations, and may allow the reconstruction of thermostable
ancestral sequences. Various computational tools make it
possible to design stabilizing features, such as hydrophobic
clusters and surface charges. Different methods for designing
chimeric enzymes can also support the engineering of more
stable proteins without the need of high-throughput screening.
Addresses
Department of Biochemistry, Groningen Biomolecular Sciences and
Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG
Groningen, The Netherlands
Corresponding author: Janssen, Dick B ([email protected])
Current Opinion in Structural Biology 2013, 23:xx–yy
This review comes from a themed issue on Engineering and design
Edited by Florian Hollfelder and Stefan Lutz
0959-440X/$ – see front matter, Published by Elsevier Ltd.
http://dx.doi.org/10.1016/j.sbi.2013.04.008
IntroductionStability under practical conditions is one of the most
critical properties when biotechnological applications of
proteins are explored [1]. High thermostability is often
accompanied by good performance under unfavorable
conditions, such as the presence of cosolvents. Improved
thermal stability may also correlate with higher expres-
sion yields in heterologous hosts, improved long-term
survival under mild conditions, increased ability to
remain active in non-aqueous solvents, and a longer
half-life under harsh industrial process conditions [2].
For medicinal proteins, thermostability may correlate
with high serum survival times [3]. In protein engineer-
ing, many function-gaining mutations, such as modified
enzyme selectivity, diminish stability. As a result, ther-
mostable proteins can tolerate a larger number of
mutations than mesostable proteins [4], and give better
results when used as a starting point in protein engin-
eering [5,6]. For these reasons, and because thermostable
variants often are not available from the natural
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biodiversity, increasing thermostability is an important
goal of protein engineering.
This review focuses on recent developments in protein
engineering aimed at enhancing thermostability. A num-
ber of well-established techniques in protein thermosta-
bilization by mutagenesis have been developed,
providing important insight in the structural features that
govern thermostability. These principles and examples
have been reviewed elsewhere [4,7–12]. On the other
hand, directed evolution has emerged as a powerful
approach, especially in the absence of structural infor-
mation [8,11]. However, when high-throughput screening
is not possible, random approaches that are frequently
used in directed evolution may become inefficient. This
triggers a quest for more focused methods, for example,
by incorporating information provided by bioinformatics
and structural analysis. The first section of this review
highlights recent progress in identifying target positions
for mutagenesis and substitutions that enhance stability.
The next sections describe recent advances in protein
stabilization by employing sequence comparisons, com-
putational design, loop grafting and chimera building.
Locating critical sites for stabilityimprovementMutations that strongly enhance protein thermostability
often appear to cluster in a particular region of the protein,
whereas similar substitutions introduced elsewhere in the
protein may have a negligible effect on thermostability
[7]. The explanation for this phenomenon is that the
strongly stabilizing mutations are located at a spot where
the protein starts to unfold [7]. Mutations that improve
local structural stability also improve global stability of a
protein, especially when they are positioned in a region
that unfolds at a critical step in a kinetically controlled
unfolding pathway. However, the pathways of unfolding
are usually unknown, making a knowledge-driven
approach difficult. Yet, several methods have been
explored to predict which positions are most likely to
contribute to thermostability and should be targeted by
mutagenesis [13–17,18�].
An approach that employs crystallographic information to
identify target positions is the B-fit method [13], in which
residues with the highest crystallographic B-factors are
selected for mutagenesis. The rationale is that highly
flexible residues are more likely to unfold in an early stage,
and that substitutions at these positions have a higher
chance to stabilize the folded state. Several successes
ering of proteins for enhanced thermostability, Curr Opin Struct Biol (2013), http://dx.doi.org/
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Table 1
Recent examples of stabilized proteins
Targeted protein TappM increase
(8C)
Part of protein targeted
by mutagenesis (%)
Origin of stabilizing mutations Reference
Suspected critical
region targeted
Esterase +9a 1 Consensus library [14]
Epoxide hydrolase +21a 3 NDT codon library [15]
a-Amino ester hydrolase +7a 3 Consensus design [16]
Heme peroxidase +4 3 Computational design [17]
Xylanase +2 4 Computational design [20]
Xylanase +5 3 Computational design [21]
Haloalkane dehalogenase +18 4 Random and site-directed [24]
b-Glucosidase +7 3 Site-directed mutagenesis [25]
No suspected critical
region targeted
Thioredoxin +32 NA Ancestral method [28]
Triosephosphate isomerase +8 8 Ancestral method [31]
b-Amylase +9 4 Ancestral method [32]
Acylphosphosphatase +9 5 Computational design [33]
GTPase +9 4 Computational design [33]
Methionine amino peptidase +18 2d Computational design [37��]
Xylanase +8 3 Computational design [38]
Cellobiohydrolase +9a 10 Computational and consensus [42]
Methyl parathion hydrolase +12 3 Computational design [41]
Sesquiterpenoid synthase +45 2 Computational design [45�]
Baeyer–Villiger monooxygenase +16b NA Chimeric enzyme [46�]
Aldo-keto reductase – NA Chimeric enzyme [49]
Polyol dehydrogenases +12b NA Chimeric enzyme [50]
Cellulase +8.5a,c NA SCHEMA [53,54��]
Cytochrome P450 +14.5a,c NA Chimeric enzyme [56��]
Green fluorescent protein +5 NA Circularization [59]
a On the basis of 50% residual activity after heat treatment, values of which can differ extremely from the T appM measured by other techniques [18�].
b Compared to the melting temperature of the thermolabile parent of the chimera.c Compared to the melting temperature of the thermostable parent of the chimera.d Only residues near void in the hydrophobic core were targeted.
have been reported (Table 1) [14–17]. A recent investi-
gation of an esterase that was improved by the B-fit method
showed that the observed increase in temperature at which
50% of the enzyme was inactivated by heating was in fact
due to improved refolding after heat treatment [18�].Whereas the mutant esterase can refold after heating
(95 8C), it unfolds at an even lower temperature than
the wild-type esterase, suggesting that the B-fit approach
can also yield substitutions that increase reversibility of
unfolding rather than enhancing thermostability.
Besides crystallographic B-factors, which may be influ-
enced by crystal contacts and solvent conditions, various
theoretical methods can be used to predict flexible
regions that may serve as targets for stabilization. A
recent example is provided by the stabilization of xyla-
nase, for which flexible residues were located either by
MD simulations at different temperatures or by molecu-
lar mechanics based flexibility predictions [19] (http://
www.flexweb.asu). Using the molecular mechanics
based flexibility analysis, computationally designed
point mutations were introduced at 8 positions in the
sequence of the 185 aa protein, which resulted in an
improvement of the T appM by 2 8C and a 15-fold longer
half-life at 50 8C [20]. Employing the results of MD
simulations and computational design, five different
mutations were incorporated in the same xylanase, which
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gave a 4 8C higher apparent melting temperature and a
30-fold longer half-life [21].
Constraint Network Analysis (CNA) can provide insight
in the unfolding mechanisms of proteins, including the
identification of residues involved in critical unfolding
steps [22��,23]. In CNA, hydrogen bonds act as con-
straints of the structure and residues that are connected
by a continuous network of hydrogen bonds are con-
sidered to be folded. The strength of each H-bond is
calculated after which the H-bonds are gradually removed
to simulate an increasing temperature. The CNA simu-
lations indicated that the disappearance of particular H-
bonds signals the abrupt disintegration of the original
interaction cluster into much smaller clusters, which
corresponds to melting of the protein. The H-bonding
residues involved in the collapse of the cluster of citrate
synthase, isopropylmalate dehydrogenase, and thermoly-
sin-like protease agreed with the residues that are known
to be critical for thermostability improvement.
Recently, two groups reported that the mutation of resi-
dues at a tunnel to the enzyme active site provided highly
stabilized variants [24,25]. For a haloalkane dehalogenase
it was possible to increase the T appM by 19 8C using this
method [24] yielding an enzyme variant that was also less
inhibited by DMSO, which was attributed to more difficult
ering of proteins for enhanced thermostability, Curr Opin Struct Biol (2013), http://dx.doi.org/
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Engineering of proteins for enhanced thermostability Wijma, Floor and Janssen 3
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entrance to the active site. For a b-galactosidase, mutation
of tunnel residues yielded an enzyme with a 10 8C increase
of the optimal temperature for catalytic activity. The
authors concluded that selective targeting of the active
site entrance tunnel should be a broadly applicable strategy
to stabilize enzymes [24]. A tunnel to an active site may
need to be flexible in order to allow substrate to enter the
active site, which can explain the success of this method.
The ancestral and the consensus methodsThe ancestral stabilization method allows for the stabil-
ization of proteins without the use of structural infor-
mation. The method is based on the hypothesis that far
ancestors of current organisms were thermophiles that
possessed proteins which were more thermostable than
extant homologs. Thus, mutations for improving stability
are derived from predicted sequences of common ances-
tors of a protein family. This method has been developed
from 1990 onwards [26] and was successfully used to
stabilize several proteins. Recent progress includes a
study on the stabilization of the B-subunit of the DNA
gyrase, in which the ancestral method was compared to
the consensus method [27]. The consensus method
replaces amino acids by residues that are most frequently
present in multiple sequence alignments. The con-
structed ancestral protein was 4–5 8C more stable
(T appM ) compared to the protein created on basis of the
consensus method. The ancestral method was also used
for recreating a thioredoxin that should have occurred in
organisms that existed about a billion years ago, instead of
millions of years ago as explored used in other studies
[28]. The proposed recreated precambrian thioredoxin
variants were 32 8C more stable in T appM than modern
thioredoxins and were more active under acidic con-
ditions which supposedly existed in the precambrian era.
The half-life of activity at 85 8C of a thermostable cellulase
was increased by 14-fold using the consensus method.
Interestingly, most protein sequences that were used to
create the consensus alignment originated from mesostable
organisms, but they could still contribute to the stability of a
thermostable protein [29]. Consensus mutations were also
shown to enhance the evolvability of a Kemp eliminase.
Such mutations were included alongside functional
mutations to counteracting destabilizing mutations that
were selected because they contributed to the function,
in this case the Kemp elimination [30�]. Without such
stabilizing mutations, it was found to be impossible to
evolve a computationally designed Kemp eliminase.
It is possible to increase the success rate of the ancestral
and consensus methods by including structural infor-
mation. As an example, consensus mutations combined
with structural information on B-factors and inter-sub-
unit disulphide bonds, lead to a 7 8C increase in the half-
life of activity of an a-amino ester hydrolase [16]. For the
ancestral method, a study on the stabilization of a
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triosephosphate isomerase introduced two new
parameters (residues that are correlated to other residues
and residues that were nearly invariant and therefore
might have ‘hidden correlations’ to other residues) to
exclude residues from being targeted for mutagenesis
[31]. Using this approach 90% of the predicted mutations
were found to be stabilizing, leading to a final variant that
was 8 8C more stable in T appM , whereas a variant which was
designed without use of these extra parameters was 2 8Cless stable. A study on the stabilization of a b-amylase
combined the ancestral method with structural infor-
mation and introduced a parameter to account for the
extent to which the residues surrounding the target
residue are conserved [32]. This parameter correlated
well with the stability effects of substitutions.
Computational design to stabilize enzymesWith computational protein design, the effect of
mutations is calculated based on the 3D-structure of
the protein. An appropriate energy function implemented
in the algorithm is used to discover mutations that
stabilize the folded protein structure. The use of com-
putational design can reduce the screening effort required
to find a desired stability enhancement by orders of
magnitude as compared to random directed evolution
methods. Two different approaches are currently
employed in the computational design of thermostable
enzymes. Mutations are either selected on the basis of
their predicted effect on the overall folding energy
(DGFold), or computational tools are applied to improve
particular features that stabilize proteins.
Several methods can improve stabilizing features in a
protein, such as loops, hydrophobic clusters, and surface
charges. In all these approaches, the energy function
within the software is used to verify that the designed
protein structure is lower in energy than the native
structure. One approach is the computational design
of a favorable network of positive and negative charges
on the protein surface. This way, it was possible to
increase the T appM of an acylphosphosphatase and a
GTPase by +9 8C [33]. By developing a design algorithm
that used the Rosetta energy function [34] to find the
most favorable positions to introduce mutations that
increase the net charge of a protein (supercharging
[35]), it was possible to increase the heat-survival of
an antibody [36]. The stabilized antibodies were also
monomeric in solution, unlike the native antibody which
formed aggregates. A commonly used computational
approach involves improving the hydrophobic packing
interactions between buried protein residues
[20,21,37��,38]. Software to detect hydrophobic networks
was used to analyze hydrophobic clusters in different
xylanases. Introduction of promising mutations into the
target xylanase gave three mutations which increased the
T appM by 8 8C [38]. An algorithm to introduce hydrophobic
mutations to fill protein voids was experimentally
ering of proteins for enhanced thermostability, Curr Opin Struct Biol (2013), http://dx.doi.org/
Current Opinion in Structural Biology 2013, 23:1–7
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demonstrated to give a T appM increase of 18 8C when applied
to a methionine amino peptidase [37��].
The second approach for using computational design to
enhance stability does not aim to improve one particular
type of interaction but generates all types of mutations
that should be favorable for stability according to the
implemented energy function. This work is mostly done
with energy functions that are specifically parameterized
to predict differences in DGFold due to point mutations.
Examples of suitable algorithms include FoldX [39],
PoPMuSiC [40], and PreTherMut [41]. Application of
FoldX in combination with consensus design resulted in
an improvement of the T appM of a cellobiohydrolase by 9 8C
[42]. For a feruloyl esterase, the T appM did not increase by
mutations that were designed by PoPMuSiC, but the
half-life of the enzyme at 50 8C was improved 10-fold
[43]. With PreTherMut, the T appM of a methyl parathion
hydrolase was improved by 12 8C [44]. Recently, using a
similar algorithm, the stability (T appM ) of a sesquiterpenoid
synthase was increased by a phenomenal 45 8C [45�].However, the resulting enzyme gave more side-products
and the catalytic activity was reduced.
Chimeric enzymes, truncation andcircularizationThermostability can also be enhanced by making chimeric
proteins consisting of subdomains of different proteins.
This way, favorable features from different parents can be
combined. For example, chimeric variants of Baeyer–Vil-
liger monooxygenases (BVMOs) were obtained by com-
bining subdomains from BVMOs with different stabilities.
Some chimeric enzymes were 10–15 8C more stable that
the thermolabile parent enzymes, while retaining or even
improving the substrate selectivity of a thermolabile
BVMOs [46�]. Several stabilizing chimeras have been
created between different xylanases [47] and between
xylanases and glucanases [48]. A chimera created by graft-
ing loops from an archeal enzyme onto a human aldo-keto
reductase showed that it is possible to combine favorable
features, in this case stability and activity, of archeal and
human proteins [49]. Such a stabilized chimera can even be
obtained without using a thermostable variant, as demon-
strated by combining domains of two mesostable polyol
dehydrogenases [50]. The resulting chimera was 12 8Cmore stable than its parents. Finally, a chimeric enzyme
can also be constructed by proteins with a low sequence
similarity (<30%), as was shown by replacing parts of a
(ba)8-barrel protein by parts of flavodoxin-like proteins
[51��]. The replacement was possible since the structure of
flavodoxin-like proteins resembles a part of the (ba)8-
barrel. The resulting chimera between the two proteins
adopted a fold strongly resembling the (ba)8-barrel scaf-
fold and was thermostable.
A sophisticated method for creating such chimeras is the
SCHEMA approach [52], which uses an algorithm to
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calculate the most favorable crossover positions between
subdomains. The calculation selects positions that mini-
mize the number of interactions that are broken when
combining subdomains. The method was applied to the
stabilization of a cellulase by recombination of an enzyme
from three different parents [53,54��]. By analyzing and
modeling the thermostabilities of 54 characterized mem-
bers of a family of fungal cellobiohydrolases, one of the
subdomains appeared critical for stability and its replace-
ment lead to an 8.5 8C increase in apparent melting
temperature (T appM ). In another study, the SCHEMA
method was combined with an active learning algorithm,
which analyzed data from a training set of chimeras to
predict a large collection of stabilizing chimeras [55]. This
method revealed a negative correlation between the iso-
electric point of a chimeric enzyme and its long-term
stability, which was used to construct enzymes with
enhanced long-term stability. Recently, a chimeric
P450 enzyme was created which was 14.5 8C more stable
compared to its parent [56��]. This thermostable enzyme
was created by modeling the protein fitness landscape and
constructing the most stabilizing variants. Bayesian stat-
istics were used to predict, to a reasonable extent, the
mean and variance fitness of any sequence.
Other possible stabilization methods are truncation and
cyclization. It is well known that the removal of thermo-
labile parts or even whole domains from a protein can
significantly improve its stability [57]. Recent achieve-
ments include the stabilization of an endo-b-1,4-gluca-
nase by removing a cellulose binding domain, which in
this case is not needed for biotechnological use of the
enzyme. This truncation resulted in a threefold increase
in half-life of the protein at 65 8C, while the catalytic
activity increased. Circularization of the proteins
sequence can also enhance stability. The N-terminus
and C-terminus are often the most flexible part of a
protein and easily become the targets of proteolytic
enzymes. Therefore, connecting the C-terminus and
N-terminus can stabilize a protein. Such a circulized
protein can be created by introducing the protein of
interest between parts of a naturally split intein from
the dnaE gene [58]. This method was used to circulize a
GFP mutant, resulting in a 5 8C more stable protein [59].
This circulized protein was also highly resistant to
proteolysis.
Conclusions and outlookEven though directed evolution is a highly successful
method for improving protein stability, there is a demand
for additional powerful techniques, especially in case of
proteins that cannot be expressed and assayed in high-
throughput format [60]. Sequence-based bioinformatics
approaches and computational methods exploiting
protein structures are especially successful. When inte-
grated into directed evolution protocols, these newer
method can boost the efficiency and reliability of directed
ering of proteins for enhanced thermostability, Curr Opin Struct Biol (2013), http://dx.doi.org/
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Figure 1
14
12
8
10
6
4
2
00 10 20 30 40
Obtained increase in TMapp (ºC)
Num
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of p
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2007
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Current Opinion in Structural Biology
TappM improvements obtained by protein engineering of enzymes from
2007 to 2012. Primary publications were searched on Web of Science
using different key word combinations to find protein engineering studies
aimed at increasing enzyme thermostability. If a publication was a
follow-up study in which an earlier stabilized enzyme was further
improved, the additional stabilization is plotted.
evolution protocols. For example, the B-fit approach and
the consensus method assist the design of small sets of
variants (or smart libraries) from which improved
enzymes may be obtained by screening only small num-
bers. Furthermore, when three-dimensional structures
are available, energy calculations may identify substi-
tutions that improve hydrophobic cores, or introduce
surface charges that prevent aggregation. This again leads
to smaller numbers of mutants to be tested experimen-
tally, and a high abundance of desired variants among
those sets.
An important remaining challenge is to obtain large
improvements of thermostability. Currently, the increase
in thermostability by mutagenesis typically amounts to 2–15 8C in T app
M (Figure 1). For many applications this is
acceptable because even a modest increase often corre-
sponds to a >10-fold longer life-time (e.g. [20,21,61]).
However, the differences in T appM between natural
enzymes that display hyperthermostability and their
mesostable homologs are much larger, indicating that
there is still a lot of room for improving engineering
techniques.
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