Intramolecular Forces Density in Mesophilic and Thermophilic Proteins:
Amino Acid Clusters Based Study
Rukman Hertadi1) and Minoru Kanehisa2)
Biochemistry Division, Faculty of Mathematics and Natural Sciences,
Bandung Institute of Technology, Bandung
2)
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto
e-mail: rukman@chem.itb.ac.id
1)
Received 15 September 2007, Accepted 26 October 2007
Abstract
Thermostability of (hyper)thermophilic enzymes has been taken as an advantage in industry to enhance biochemical
reactions at elevated temperature. Factors responsible for the thermostability in this class of proteins, however, still
remain unclear despite the many works that have been done to elucidate such factors by performing various
comparative studies to homologous pairs of (hyper)thermophilic and mesophilic proteins. In the current work, we
elucidated the factors by comparing intramolecular forces density in tertiary structure of mesophilic and
(hyper)thermophilic proteins in terms of the content of various types of amino acid clusters. A graph spectral method
was employed to probe the charged, hydrophobic and aromatic clusters in each tertiary structure of all classes of
thermophilic proteins used in our study. Our results revealed that mesophilic and (hyper)thermophilic proteins contain
similar level of all types of amino clusters, thereby stabilized with similar level of high-density intramolecular forces,
but the former contain a higher number of non-cluster residues and less stabilized by electrostatic interactions, thereby
more sensitive to heat.
Keywords: Thermophilic, Graph spectra, Intramolecular force density, Amino acid cluster
Abstrak
Termostabilitas enzim-enzim (hiper)termofilik telah banyak dimanfaatkan di industri terutama untuk meningkatkan laju
reaksi biokimia pada suhu tinggi. Faktor penyebab termostabilitas enzim termostabil masih belum diketahui dengan
jelas hingga sekarang meskipun berbagai penelitian dengan melakukan studi komparatif dari pasangan homolog
protein mesofilik dan (hiper)termofilik telah banyak dilakukan. Pada studi kali ini, kami mencoba menyelidiki faktorfaktor tersebut dengan membandingkan kerapatan gaya intramolekul dalam struktur tersier dari protein mesofilik dan
(hiper)termofilik dengan cara mengevaluasi kandungan berbagai tipe klaster asam amino yang terdapat di dalam ketiga
kelas protein termofilik. Metode spektra graf digunakan untuk menentukan keberadaan klaster asam amino bermuatan,
hidrofobik, dan aromatik dalam setiap struktur tersier dari semua kelas protein termofilik. Hasil analisis ditemukan
bahwa protein mesofilik dan (hiper)termofilik pada dasarnya mengandung jumlah klaster yang sebanding, dengan kata
lain distabilkan oleh gaya intramolekular yang sebanding kekuatannya. Akan tetapi, bila ditinjau dari sisi jumlah asam
amino non-klaster, protein mesofilik rata-rata memiliki rasio yang lebih besar dibanding pada protein (hiper)termofilik.
Disamping itu protein mesofilik juga kurang distabilkan oleh interaksi elektrostatik. Kedua hal inilah menurut hasil
studi ini, yang membuat protein mesofilik menjadi lebih sensitif terhadap suhu.
Kata kunci: Termofilik, Spektra graf, Kerapatan gaya intramolekul, Klaster asam amino
associates the stability to the chemical formula of
amino acid side chains; chemical properties of
individual
amino
acids,
such
as
polarity,
hydrophobicity, pI, and pKa; and the chemical
environment, such as solvent, denaturing agent, and pH
of the solution. Intra and intermolecular forces, folding
energy, thermostability, and mechanical stability under
external stress are classified into the physical aspects of
the protein structure characterization. In this report, we
focus our characterization to one of the physical
characterization of proteins, namely thermostability.
1. Introduction
Protein stability is an aspect that commonly
included in most protein characterization. This is due to
the wide variations exhibited by a variety of proteins
classes. Besides, this property could not be predicted
from mere amino acid sequences inspection until
recently. The stability aspect of proteins may be
divided into two main categories, namely chemical and
physical/structural aspects. The chemical aspect
102
Hertadi and Kanehisa, Intramolecular Forces Density in Mesophilic 103
Understanding thermostability of proteins is critical in
designing efficient enzymes that can work at high
temperatures (Adams and Kelly, 1995).
Thermostability of a protein is commonly
classified according to the organism source, where the
protein is isolated. Some microorganisms such as
archea can live in environment with temperature over
80 oC. Proteins isolated from this type of organism are
classified as hyperthermophilic proteins. Other class of
proteins known as medium thermophilic or simply
thermophilic proteins are isolated from organisms
which can live in the medium range of temperature
within 40-80 oC. The final class is typical proteins,
which are isolated from organisms that live in
temperature below 40 oC, and classified as mesophilic
proteins.
The potentially stabilizing factors were mainly
identified by comparing homologous pairs of
mesophilic and (hyper)thermophilic proteins. Jaenicke
and Böhm (1998) have summarized almost all of the
possible factors responsible for the thermostability of
proteins. They noted that thermostability occurred due
to (1) an increase in the number of hydrogen bonds, (2)
additional or improved electrostatic interactions caused
by salt bridges or networks, (3) optimized hydrophobic
interactions, (4) increased compactness or packing
densities, (5) increased polar compared with non-polar
surface areas, (6) increases in α-helical content and αhelix stability, (7) the (improved) binding of metal ions,
(8) improved fixation of the polypeptide chain termini
to the protein core, (9) replacement of residues with
energetically unfavourable conformations by glycine,
(10) truncation of solvent-exposed loops, (11) a higher
number of prolines and β-branched amino acids in
loops, (12) association to oligomers, (13) reduction of
the content of the thermally labile amino acids
asparagine, glutamine, cysteine and methionine. This
list shows that stabilizing features can occurr at all
structural levels, from the amino acid sequence to the
quaternary structure of proteins.
The comparative study to homologous pairs of
mesophilic and (hyper)thermophilic proteins explained
above still have some disadvantages. This is because
the accumulation of the many neutral mutations during
divergent evolution causes difficulty in identifying the
crucial amino acid differences by mere inspection of
structures. Furthermore, the presence or absence of few
stabilizing increments might account for significant
stability differences (Jaenicke et al., 1996). Another
difficulty arises from the temperature-dependence of
stabilizing interactions. It was suggested that
hydrophobic interactions, which are entropic at room
temperature but become enthalpic at higher
temperatures, reach their maximum stabilizing effect at
75 °C (Makhatadze and Privalov, 1995). Recent
theoretical studies suggest that the stabilizing effect of
electrostatic interactions increases with increasing
temperature (Elcock, 1998; de Bakker et al., 1999;
Xiao and Honig, 1999). Data on the temperature
dependence of other stabilizing interactions are sparse.
For these reasons, it is not yet possible to derive
rules that govern high protein thermostability. As
outlined above, the identification of stabilizing
interactions is a search for small differences against a
huge background. Consequently, only large-scale
comparisons of amino acid sequences and threedimensional structures will provide statistically
significant differences. The growing amount of whole
genome
sequences
from
mesophilic
and
(hyper)thermophilic organisms and the enormous speed
with which new X-ray structure become available, now
allow systematic comparisons between proteins from
mesophiles and (hyper)thermophiles that promise a
more general insight into the problem.
In this report, we show our effort to find the
possible factors responsible for thermophilic properties
of proteins by performing systematic comparison
between mesophilic and thermophilic proteins in terms
of the content of various types of amino acid clusters
and the interaction energy contributed by each cluster.
We will demonstrate that mesophilic and
(hyper)thermophilic proteins actually have similar level
in the thermal stabilization factors but the former are
destabilized due to the higher number in non-clustered
residues and less stabilized by electrostatic interactions.
2. Methods
A dataset containing thermophilic proteins
constructed for this work was chosen from Protein Data
Bank (PDB), by searching for the word ‘‘thermo’’.
This search yielded 710 proteins, most of them from
thermophilic organisms. At the first refinement step,
the entries with protein structures determined by
nuclear magnetic resonance (NMR) were discarded. All
the remaining entries were then examined and the
dataset size was reduced by eliminating multiple
structures of the same protein and choosing the wild
type structure with the highest resolution. Next,
individual sequences of each protein were inspected for
any modified amino acids and those having such
modified amino acids in their sequence were removed
from the entry list. The resulting proteins were
compared all against all so that none of them have more
than 20% identity with all other sequences. Finally,
non-homologue thermophilic proteins were retained if
there was at least one high-resolution crystal structure
for their corresponding mesophilic homologues.
Through this screening, 38 non-redundant families
remained. For each thermophilic protein, corresponding
mesophilic homologue with more than 20% identity
and same topology or function was selected. Among
the selected proteins, there are oligomeric proteins, and
104 JURNAL MATEMATIKA DAN SAINS, SEPTEMBER 2007, VOL. 12 NO. 3
only the first monomeric chain from their PDB files
were chosen in this study. Table 1 shows the list of
homologous pairs of thermophilic and mesophilic
proteins together with their PDB entry ID and sequence
identity. Optimum temperature was assigned to each
protein according to those of optimal growth or normal
living environment for the species involved.
2.1 Identification amino acid cluster
Amino acid clusters for all the proteins in
dataset were obtained by a graph spectral method as
described in e.g. Kannan and Vishveshwara (1999).
The methods detect a particular type of amino acid
cluster by considering all corresponding Cβ atoms of
that cluster type in the proteins as nodes of a graph and
two interacting side chains were connected in the graph
assigning an edge in weight corresponding to the
distance between the respective Cβ atoms. If any two
Cβ atoms of any two residues are within a distance of
4.5 Å, then they are said to form an interacting pair.
This connectivity information is represented in the form
of a Laplacian matrix. The Laplacian matrix is
diagonalized and clustering information is obtained
from the eigenvectors corresponding to the second
lowest eigenvalue.
There were three types of amino acid clusters
that were determined in this work by the above
procedure, namely hydrophobic, electrostatic and
aromatic clusters. In order to detect hydrophobic
clusters, only the hydrophobic residues (L, I, M, V, P,
F, C, A, Y, W) were considered in the protein. Also to
detect electrostatic and aromatic clusters, we only
considered charged residues (D, E, H, L, R) and
aromatic residues (W, T, Y), respectively.
2.2 Energy calculation
Energy calculation was performed using NAMD
v2.6 (Phillips et al., 2005) with CHARMM27 force
field. All crystal structures of the protein samples were
minimized for 500 steps using conjugate gradient
method prior to energy calculation. Cluster interaction
energy was calculated using corresponding atomic
selection methods in VMD v8.5 (Humphrey et al.,
1996).
2.3 Data Analysis
All data analysis was carried out by using one of
the efficient robust statistical methods, namely box
plots (Massart et al., 2005). In this method, numerical
data set was evaluated into five-number summaries [the
smallest observation, lower quartile (Q1), median (Q2),
upper quartile (Q3), and largest observation] and
outlier. Q1, Q2, and Q3 represent 25th, 50th, and 75th
percentiles of a sorted data set, respectively. Outliers
were defined as data whose values are less than
Q1 − 1.5(Q3−Q1) or greater than Q3 + 1.5(Q3 − Q1).
The lowest and highest observations are the lowest and
highest value of the data set, respectively, which are not
in outliers data range. The median and the inter quartile
region (IQR = Q3 − Q1) are used to construct a box
part of the box plot. It has a height equal to the IQR and
is drawn so that it starts at Q1 value and stops at Q3
value. A horizontal bar is drawn at the height of the
median. The lowest and highest observation values,
which indicate the range of the data, are represented as
vertical lines ending in a small horizontal line. The
outliers were presented in the box plot as black-solid
circles. Mean of the data set was depicted as horizontal
dashed-line within the box.
3. Result
In current analysis, we compared all calculation
results directly in order to obtain distribution profile for
each result rather than tabulating them for each
homologous pairs of mesophilic and (hyper)
thermophilic proteins as commonly performed by many
researchers (Kannan and Vishveshwara, 2000; Haney et
al., 1999). Since our purpose is to find a general reason
for thermophilic property of protein, analyzing the
distribution profile is more advantageous.
Figure 1 shows examples for the clusters
identification resulted from the graph spectral methods.
Escherichia coli elongation factor (EF1) contain three
hydrophobic clusters, ten charged clusters, and two
aromatic clusters (Figure 1A, 1B and 1C, respectively).
In this study, we set three as the minimum residue
number to be considered as a hydrophobic or charged
cluster, but a minimum two residues for that of
aromatic cluster. This setting was made due to the latter
cluster having relatively higher residue contact
compared to the other two clusters. In almost all
proteins samples used in our study, we found that
hydrophobic clusters exist in the smallest number but
highest contact density. Charged cluster, however,
typically found in a lesser contact density compared to
the hydrophobic cluster but highest in number. The
aromatic clusters are found to be medium in number,
but the least dense.
3.1 Number of amino acid clusters
The number of clusters in each protein were
plotted as a function of the optimum growth
temperature of microorganism in order to obtain the
profile for the number of clusters in mesophilic and
thermophilic proteins (Figure 2A). The distribution in
the number of cluster for each type of cluster in
mesophilic, thermophilic and hyperthermophilic proteins is almost comparable. In average, there are 6
charge clusters, 2 hydrophobic clusters, and 4 aromatic
clusters in both mesophilic and (hyper)thermophilic
proteins samples used in the current study (Figure 2B).
Hertadi and Kanehisa, Intramolecular Forces Density in Mesophilic 105
3.2 Cluster density
The cluster density was then analyzed by
evaluating the mean residue number per cluster (Figure
3). Such analysis may give information on
intramolecular force density present in each protein. It
was found that the distribution of mean number of
residue per cluster for all types of cluster appeared to
be almost similar for both mesophilic and thermophilic
proteins. The mean number of residue per cluster for
charged, hydrophobic, and aromatic clusters in
mesophilic and (hyper)thermophilic proteins are 10, 60,
and 3, respectively (Figure 3B). Such similarities in the
cluster density in all proteins classes reflect the
similarity in the density of intramolecular forces.
3.3 Interaction energy
In order to further verify the similarity between
mesophilic and thermophilic in terms of intramolecular
forces, interaction energy among residues involved in
the formation of each type of cluster for all mesophilic
and (hyper)thermophilic proteins samples was
calculated. The total interaction energy in each type of
cluster was then plotted against the optimum
temperature of the corresponding protein (Figure 4A).
It was found that total interaction energy for all proteins
sample was at about in the same level for all
temperature range. Further statistical analysis
(a)
(b)
confirmed such situation (Figure 4B). The mean value
of the interaction energy in all compared cluster
between mesophilic and thermophilic proteins were
similar to each other. These results verify that
mesophilic and (hyper)thermophilic proteins can not be
distinguished in terms of amino acid clusters.
3.4 Composition of cluster and non-cluster residues
The percentage of amino acid residue belongs to a
particular cluster and also non-cluster for all mesophilic
and (hyper)thermophilic proteins samples were
compared. The amino acid percentage was initially
plotted against the optimum temperature of each
corresponding protein (Figure 5A), and then
statistically analyzed to obtain the distribution profile
(Figure 5B). From this analysis, a difference between
the percentage of residue involved in the charged
cluster formation between mesophilic and thermophilic
proteins, for which the value was relatively higher in
thermophilic proteins, were found. In addition, a
difference was also found in the percentage of noncluster residue in which mesophilic proteins contain
higher number of non-cluster residues compared to that
of thermophilic proteins. This final analysis verifies
that the only difference between mesophilic and
thermophilic proteins is in the composition of cluster
and noncluster amino acid residues.
(c)
Figure 1. Sample of amino acid cluster identified by the graph spectral method. In this sample, hydrophobic (a), charged
(b), and aromatic amino acid clusters (c) are shown for the elongation factor protein with PDB code entry 1EFU. Amino
acids involved in the cluster are represented by Cβ atoms depicted with Van der Waals representation and different color
was used for Cβ in each different cluster.
106 JURNAL MATEMATIKA DAN SAINS, SEPTEMBER 2007, VOL. 12 NO. 3
(A)
(B)
Figure 2. Distribution and statistical analysis of the number of charged, hydrophobic and aromatic cluster in mesophilic
and (hyper)thermophilic proteins. (A) Plot of number of clusters against the optimum growth temperature of
microorganism (B) Box plot of number of clusters for charged, hydrophobic, and aromatic clusters in mesophilic (Group
1, 3, 5) and thermophilic proteins (Group 2, 4, 6). The red dashed line represents mean value in each distribution. The
black solid circle represents outlier data. The gray box represents 25% and 75%precentail of the data.
Hertadi and Kanehisa, Intramolecular Forces Density in Mesophilic 107
(A)
(B)
Figure 3. Distribution and statistical analysis of the mean number of residues per cluster in charged, hydrophobic and
aromatic cluster in individual mesophilic and thermophilic proteins. (A) Plot of mean number of residues per cluster
against the optimum growth temperature of microorganism (B) Box plot of mean number of residues per cluster for
charged, hydrophobic, and aromatic clusters in mesophilic (Group 1, 3, 5) and thermophilic proteins (Group 2, 4, 6).
108 JURNAL MATEMATIKA DAN SAINS, SEPTEMBER 2007, VOL. 12 NO. 3
(A)
(B)
Figure 4. Distribution and statistical analysis of the interaction energy in charged, hydrophobic and aromatic clusters in
individual mesophilic and thermophilic proteins. (A) Plot of interaction energy against the optimum growth temperature
of microorganism where the protein is isolated. (B) Box plot of interaction energy for charged, hydrophobic, and
aromatic clusters in mesophilic (Group 1, 3, 5) and thermophilic proteins (Group 2, 4, 6).
Hertadi and Kanehisa, Intramolecular Forces Density in Mesophilic 109
(A)
(B)
Figure 5. Comparison of the percent amino acid residues involved in the hydrophobic, charged and aromatic clusters
and also the remaining non-cluster residues. (A) Plot of the percentage composition in each protein against the
temperature growth of microorganism (B) Box plot of percentage composition of individual residues group. for charged,
hydrophobic, aromatic and non clusters in mesophilic (Group 1, 3, 5, 7) and thermophilic proteins (Group 2, 4, 6, 8).
110 JURNAL MATEMATIKA DAN SAINS, SEPTEMBER 2007, VOL. 12 NO. 3
Table 1. List of homologous pairs of thermophilic and mesophilic proteins used in the study
(Hyper) Thermophile
No.
Protein name
Mesophile
Source
PDB
Optimum
temp. (oC)
Source
PDB
Optimum temp.
(oC)
1.
Carboxyl peptidase
Thermococsus litoralis
1A2Z
88
Bacillus amyloliquefaciens
1AUG
30
2.
Lactate dehydrogenase
Thermotoga maritima
1A5Z
80
Porcine muscle
9LDT
37
3.
Methionyl-tRNA synthetase
Thermus thermophilus
1A8H
75
Escherichia coli
1QQT
37
4.
Cytrate synthase
Pyrococcus Furiosus
1AI9
100
Candida albicans
1A59
20
5.
Malate dehydrogenase
Thermus flavus
1BMD
70
Aquaspirillum arcticum
1B8P
4
6.
β-mannanase
Thermomonospora fusca
1BQC
50
Bacillus agaradherans
1A3H
30
7.
Pyrimidine nuclease
Bacillus stearothermophilus
1BRW
55
Escherichia coli
1OTP
37
8.
Glutamate dehydrogenase
Thermococcus litoralis
1BVU
88
Clostridium symbiosum
1HRD
37
9.
Xylose isomerase
Thermus thermophilus
1BXB
75
Streptomyces rubiginosus
1XIF
28
10.
Adenylosuccinate lyase
Thermotoga maritima
1C3U
80
Anas platyrhynchos
1AUW
25
11.
Superoxide dismutase
Aquifex pyrophilus
1COJ
85
Homo sapiens
1VAR
37
12.
Dyhidrofolate reductase
Thermotoga maritima
1CZ3
80
Candida albicans
1AI9
37
13.
TATA binding protein
Pyrococcus woesei
1D3U
100
Saccharomyces cerevisiae
1TBP
25
14.
3-isopropylmalate dehydrogenase
Thermus thermophilus
1DR0
75
Salmonella typhimurium
1CNZ
37
15.
Sporulation protein A
Bacillus stearothermophilus
1DZ3
55
Bacillus subtilis
1NAT
30
16.
Elongation factor TU
Thermus aquaticus
1EFT
70
Escherichia coli
1EFC
37
17.
Ribonucleoprotein
Thermus aquaticus
1FFH
70
Escherichia coli
1FTS
37
18.
Glutamyl-tRNA synthetase
Thermus thermophilus
1GLN
75
Escherichia coli
1EUQ
37
19.
Tyrosyl-tRNA synthetase
Thermus thermophilus
1H3E
75
Staphylococcus aureus
1JII
37
20.
Histidyl-tRNA synthetase
Thermus thermophilus
1H4V
75
Staphylococcus aureus
1QE0
37
21.
Thermolysin
Bacillus thermoproteolyticus
1HYT
55
Pseudomonas aeruginosa
1EZM
37
22.
Carboxypeptidase T
Thermoactinomyces vulgaris
1OBR
55
Bovine Pancreas
2CTC
37
23.
3-isopropylmalate dehydrogenase
Thermus thermophilus
1OSJ
75
Thiobacillus ferrooxidans
1A05
30
24.
Response regulator
Bacillus stearothermophilus
1QMP
55
Escherichia coli
1B00
37
25.
Carbonic anhydrase
Methanosarcina thermophila
1QQ0
50
Escherichia coli
1LXA
37
26.
β-glycosidase
Thermosphaera aggregans
1QVB
85
Bacillus polymyxa
1BGA
30
27.
Superoxide dismutase
Sulfolobus solfataricus
1WB8
87
Porphyromonas gingivalis
1QNN
37
28.
Elongation factor TS
Thermus thermophilus
1TFE
75
Escherichia coli
1EFU
37
29.
Thermitase
Thermoactinomyces vulgaris
1THM
55
Bacillus lentus
1C9J
26
30.
Endocellulase
Thermomonospora fusca
1TML
50
Humicola insolens
1BVW
40
31.
Elongation factor TU
Thermus aquaticus
1TUI
70
Escherichia coli
2BVN
37
32.
Methionine aminopeptidase
Hyperthermophile pyrococcus
1XGS
100
Escherichia coli
1MAT
37
33.
Alcohol dehydrogenase
Thermoanaerobium brockii
1YKF
57
Clostridium beijerinckii
1KEV
37
34.
Triosephosphate isomerase
Pyrococcus woesei
1HG3
100
Escherichia coli
1TRE
37
35.
Glyceraldehyde-3-phosphate
dehydrogenase
Bacillus stearothermophilus
2GD1
55
Escherichia coli
1GAD
37
36.
D-amino acid aminotransferase
Thermophilic bacillus
3DAA
60
Escherichia coli
1A3G
37
37.
Phosphofructokinase
Bacillus stearothermophilus
4PFK
55
Escherichia coli
1PFK
37
38.
Thermolysin
Bacillus stearothermophilus
8TLN
55
Staphylococcus aureus
1BQB
37
Hertadi and Kanehisa, Intramolecular Forces Density in Mesophilic 111
4. Discussion
Comparing two or more series of results is one
of the most often performed data analysis tasks in
comparative studies. Classical statistical methods are
the t-test for comparing means and the F-test for
comparing variances of two series of data, and analysis
of variance (ANOVA) for more than two series. These
methods are vulnerable to the presence of outliers and
are based on assumptions such as normal distributions
and (depending on the test) equal variance (Massart et
al., 2005). The juxtaposition of box plots is an excellent
way to investigate if there are differences between the
data sets and can be applied without any statistical
assumptions. In our amino acid cluster analysis, we
have shown the effectiveness of this type of analysis in
discriminating factors responsible for thermostability of
thermophilic proteins in sparse data set resulted from
various analysis above (Figure 2 to 5).
It has been shown that the intramolecular
interaction energy contributed by various types of
amino acid clusters in mesophilic proteins was about
similar level to that of thermophilic proteins and even
to that of hyperthermophilic ones. This fact suggests
that the stabilization energy contributed by cluster
formations is about similar level in both mesophilic and
(hyper)thermophilic proteins. Our result is highly
correlated with the previous study by Karshikoff group
who found similar packing in mesophilic and
thermophilic proteins (Karshikoff and Landstein,
1998). However, if we assume that non-cluster residues
in proteins as a destabilization factor due to relatively
lower number of intramolecular forces in stabilizing
their positions, our result showed that mesophilic
proteins contain relatively higher number of non-cluster
residues compare to those of (hyper)thermophilic
proteins. In other word, mesophilic proteins contain
more destabilization factors thereby more fluctuated
and less stable at high temperature.
We also found that the number of charged
residues involved in the charged cluster formation were
slightly higher in thermophilic proteins compared to
those of mesophilic ones. Electrostatic interaction is a
long-range interaction, hence the small difference in the
number of charged residues between thermophilic and
mesophilic proteins might lead to a significant thermal
stabilization effect. Contribution of electrostatic
interaction has been reported in Alsop et al. (2003).
Our results support their suggestion that electrostatic
interactions optimized thermal stability of thermophilic
proteins.
4. Conclusion
Level of contribution of amino acid clusters to
the thermal stability of mesophilic proteins is similar to
that of (hyper)thermophilic proteins, meaning, high-
density intramolecular forces also induced comparable
stabilization in all class of proteins. However, two
apparent distinct results of the analyses could lead to
the difference in the thermostability between
mesophilic and (hyper)thermophilic proteins. First, the
percentage of non-cluster amino acid is on average
higher in mesophilic proteins compared to that of
(hyper)thermophilic proteins. Second, structures of
thermophilic proteins are more optimized by
electrostatic interactions compared to that of
mesophilic proteins.
Acknowledgment
The author (RH) wishes to thank the Hitachi
Scholarship Foundation for providing financial support
for this work.
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