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BIO © [2012], Copyright CCAAS Hervé Seligmann, BIO 2012, 2, 11-26
11
http://ccaasmag.org/BIO
Positive and Negative Cognate Amino Acid Bias Affects
Compositions of Aminoacyl-tRNA Synthetases and Reflects
Functional Constraints on Protein Structure
Hervé Seligmann
1The National Collections of Natural History, The Hebrew University of Jerusalem, 91404, Israel.
[email protected]; [email protected]: esTel and Fax: +972 2 6585876. Email address
doi:10.5618/bio.2012.v2.n1.2 || Received: 30-11-2011, Accepted: 24-02-2012, Available online: 08-03-2012
Abstract
By comparing phylogenetically related tRNA syn-
thetases (enzymes that specifically aminoacylate
tRNAs), a controlled natural experiment can reveal
synthetases’ coevolution with their cognate amino
acid substrate. Analyses of metabolic cost minimi-
zation confirm the existence of cognate avoidance
in tRNA synthetase compositions. It is found that
cognate avoidance increases and decreases,
respectively, with proteomic amino acid usage in
Escherichia coli and Bacillus subtilis.
In E. coli, cognate avoidance did not decrease
with cognate abundance in the colon, but decrease
with tRNA synthetase editing sites and cognate
impact on protein structure, revealing that
hydrophobic interactions and beta-sheet formation
constrain the folding of tRNA synthetase classes I
and II, respectively. Analyses of cognate bias yield
information on how proteins function in E. coli,
because function constrains cognate bias. In B.
subtilis, cognate avoidance occurred for rare and
abundant amino acids in the soil, positive bias
existed for cognates with intermediate abundances.
Presumably, life history strategies (endosymbiont
versus free living) and environmental compositions
modulate cost minimization of amino acid usages.
Avoidance of ‘expensive’ residues in tRNA
synthetases is inversely proportional to cognate
avoidance and protein size in E. coli, but not B.
subtilis. In relation to cost minimization, E. coli’s
predictable environment perhaps enabled to reach
evolutionary (balancing) equilibrium between
various factors affecting protein synthesis costs,
where decreasing costs through one factor requires
increasing other costs. Phylogenetically controlled
comparisons can detect more statistically
significant biases than similar analyses using
randomly selected control proteins, stressing the
power of carefully designed natural experiments.
This work confirms the importance of biosynthetic
cost minimization, and challenges neutralistic
approaches of biomolecular evolution.
Keywords: aminoacylation; phylogenetic contrast;
homology; catalysis; neutral evolution; tRNA
synthetase editing; secondary structure; antiparallel
betasheet.
1. Introduction
The molecular weight of amino acids associates
negatively with their abundances in protein
compositions [1]. This observation led to the
hypothesis that amino acid compositions of proteins
are in part determined by cost minimization principles
[2]. The aim of this study is to examine whether cost
minimization of protein synthesis is integrated with
functional constraints on proteins, and thus revealing
new information on protein structure and function.
The hypothesis of cost minimization of protein
synthesis resembles the hypothesis of synonymous
codon usage optimization, which minimizes transla-
tion delays by using synonymous codons that are
matched by the anticodons of the most common tRNA
for that cognate amino acid [3-5]. Similarly to
synonymous codon optimization, the extent of cost
minimization of amino acid usage increases with
protein expression levels [6-10]. Other observations
also follow predictions from the cost minimization
hypothesis. For instance, whole organism growth and
developmental rates increase with cost minimization
at molecular levels [7, 11]. Free-living organisms
minimize more costs of amino acid usages than
parasitic ones [7, 12], while some parasitic organisms
also minimize costs of amino acid usages [13].
Reduction in protein length, as a means to decrease
their biosynthetic costs, seems to follow similar
principles, at least in comparisons between free-living
and endocellular species [14]. Further observations
that protein size is inversely proportional to its
expression level are also in line with that principle
[15-16]. Cost minimization principles also explain
differences in amino acid compositions between
proteins expressed under different endogenic [17-18]
BIO © [2012], Copyright CCAAS Hervé Seligmann, BIO 2012, 2, 11-26
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as well as environmental conditions [19]. Metabolic
costs also increase evolutionary conservation of amino
acids [7, 20]. Following the same principle at atomic
level, a cyanobacterium specifically expresses sulfur-
depleted versions of some highly expressed proteins
under sulfur-starvation [21]. This principle of
minimizing metabolic costs at the level of atomic
compositions also exists for nitrogen- and carbon-
assimilatory enzymes that are relatively depleted in
nitrogen and carbon, respectively [22-26]. This
introduces an additional prediction to the metabolic
cost avoidance hypothesis, that of substrate avoidance
in compositions of enzymes. This prediction was
confirmed specifically in the context of amino acids in
three species: Escherichia coli, its close relative which
shares its endosymbiontic habitat in the human colon,
Salmonella typhimurium and the more distantly
related Bacillus subtilis that inhabits the soil [27]. This
principle also affects an amino acid's secondary
metabolism [11]. In yeast, levels of cognate avoidance
in amino acid compositions of enzymes involved in an
amino acid's biosynthetic path can even predict a
following decrease in experimental growth after
depletion in that amino acid [11].
1.1. tRNA synthetases and cognate avoidance. In
this work, I explored the issue of cognate amino acid
bias in the realm of tRNA synthetases, a group of
enzymes that esterify amino acids to their cognate
tRNAs. The loading of each of the 20 tRNA
functional groups by its cognate amino acid is done by
a specific tRNA synthetase. This functional group of
20 enzymes has two further characteristics that
constitute an actual controlled natural experiment in
which cognate bias can be investigated. It is
subdivided into two equal sized classes of proteins,
class I and class II tRNA synthetases. Note that it is
possible that class I and II tRNA synthetases are
matched in terms of each being originally coded on
the antisense (complementary) strand of the other [28-
33]. This is a controversial hypothesis (see digression
by Williams et al. [34]) that would result from cost
minimization of genome lengths in the ancestral
genome(s) [35]. Minimizing genome lengths is also
frequently redeveloped in modern parasitic organisms
[36-39]. Independently of these, all enzymes in class I
and class II tRNA synthetases are evolved from a
common ancestor [40]. Hence comparisons between
phylogenetically matched sister tRNA synthetases
with different cognate amino acids can be used to
quantify extents of cognate amino acid avoidance in
their respective compositions.
In order to compare the relative power of
compositional tests on proteins based on evolutionary
homology to a randomly chosen control group as done
in [27], I tested cognate bias at the compositional level
of tRNA synthetases in the organisms examined by
Alves and Savageau [27]. These organisms are also
particularly adequate for a test of the „cognate amino
acid bias‟ because of the availability of the relative
abundances of amino acids in their respective natural
environments. This can enable tests on whether
cognate bias is regulated by environmental factors.
1.2. Substrate avoidance hypothesis. Increased
charging of some tRNAs, as a result of negative
feedbacks in metabolism, can be presumed to occur
specifically under conditions where the loaded tRNA
is relatively rare [41]. This means that tRNA
synthetase synthesis systematically occurs under
circumstances of lack of the reaction product it
catalyzes, i.e., the tRNA loaded with its cognate. This
rationale suggests that the composition of tRNA
synthetases includes their respective cognate amino
acid only at locations where that amino acid is
functionally important, or even unavoidable. By
comparing amino acid compositions of tRNA
synthetases that are „phylogenetic‟ sisters [40], I
expected that the cognate amino acid would be less
frequent in the composition of its esterifying tRNA
synthetase than in the composition of its phylo-
genetically closest tRNA synthetase, which esterifies a
different amino acid. Exploring differences in
cognate bias between tRNA synthetases with different
cognates but from the same tRNA synthetase class
could also reveal which properties of the cognate
make the cognate amino acid unavoidable for protein
function. For example, one could expect that amino
acids with great impact on protein structure
(quantified for example by their Chou-Fasman
conformational indices) can less be avoided (weaker
negative bias) than those with lower impact. Because
class I and II tRNA synthetases belong to different
types of proteins, it is possible that different cognate
properties associate with cognate bias in each group,
reflecting the functional importance of each property
on the different protein types. Such analyses of
differences in cognate bias would, beyond confirming
the existence of cognate bias, have the potential to
reveal functional properties of amino acids and
proteins through the study of cognate bias in particular
and cost minimization principles in general.
1.3. Why tRNA synthetases. In addition to the
adequacy of tRNA synthetases for non-manipulative
natural experiments, there are other reasons regarding
processes that influence the evolution of these
proteins. Indeed, the various tRNA synthetases did
not only evolve from a common ancestor, but
surprisingly other proteins apparently evolved from
tRNA synthetases. For example, the vertebrate
mitochondrial gamma polymerase, the sole replication
polymerase for animal mitochondrial genomes [42],
originates from horizontal transfer of prokaryotic
glycyl tRNA synthetase [43], which is also predicted
to interact with DNA templating for tRNAs [44]. The
evolutionary versatility of tRNA synthetases [45] and
their functionally crucial sites build a bridge between
the RNA and the protein „worlds‟: they have sites that
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specifically recognize RNA regions, and the
properties of these sites are determined by those of
RNA, which affect any protein that interacts with
those RNAs [to some extent, also DNA stretches (as
suggested by the homology with DNA gamma
polymerase reported above)]; they have a site that
interacts with their specific cognate amino acid,
whose properties are determined by those of that
amino acid. This could bear insights on how proteins
interact with other proteins.
1.4. Comparative experiments: the power of
adequate control. In principle, the cognate amino
acid bias hypothesis was confirmed in [27], but this
was by comparing the amino acid contents of specific
proteins with a pool of proteins at large in that
organism. The results presented below on tRNA
synthetases are based on better controlled
comparisons (between pairs of phylogenetically
“sister” tRNA synthetases). These analyzes yield
results that are far more statistically significant than
those obtained in [27], thereby stressing the power of
the tRNA synthetase system for testing protein
evolution hypotheses. The study shows that careful
comparative evolutionary analysis is a powerful tool
for scientific research. In addition, the analysis can
reveal functional properties that modulate cognate
bias in relation to the constraints of each tRNA
synthetase class.
2. Materials and Methods
2.1. Overall compositional bias. I counted amino
acids in each of the tRNA synthetases of Escherichia
coli and Bacteria subtilis and calculated the
percentage that each amino acid represents in the
composition of each tRNA synthetase. Using the
phylogenetic relationships among tRNA synthetases
of class I and class II described in Figure 9 from [40],
I matched each tRNA synthetase with its
phylogenetically closest tRNA synthetase.
Considering the composition percentage of the
cognate amino acid for each tRNA synthetase, I
subtracted it from the composition percentage of that
amino acid in the phylogenetically matched tRNA
synthetase. Negative values reflect the level of
avoidance of the cognate amino acid in the tRNA
synthetase‟s primary structure. In some cases, the
phylogenetic topology indicated that a pair of closely
related tRNA synthetases form a group that is the
sister group of another tRNA synthetase. For
example, threonyl synthetase and prolyl synthetase
form a sister group for serinyl tRNA synthetase. In
such cases, I averaged the % that serine represents in
the compositions of threonyl and prolyl synthetases,
and subtracted this average from the % serine in
serinyl synthetase.
2.2. Testing the hypothesis at three different levels.
I tested the statistical significances of specific cognate
biases at three different levels using sign tests. In the
sign tests, cognate bias fits the „cognate avoidance‟
hypothesis if the index is negative. a) across tRNA
synthetases and cognate amino acids, I checked the
number of times that the cognate amino acid bias is
negative; b) within single tRNA synthetases, across
amino acids, I checked the number of times that the
subtraction of the % composition of all 19 non-
cognate amino acids in the sister tRNA synthetase
from the % in that specific tRNA synthetase was less
negative than the subtraction for the cognate amino
acid; c) across all tRNA synthetases, but for the same
amino acid, I checked the number of times that the
subtraction for a specific amino acid in the other
tRNA synthetases was less negative than for the tRNA
synthetase for which this amino acid is the cognate
amino acid. For the data used for tests (b) and (c), I
also calculated the Z standardized cognate bias by
dividing it by the standard deviation of all biases.
3. Results
3.1. General overview. Table 1 presents the „sister‟
tRNA pairs used to calculate cognate bias, and the
cognate bias calculated for each tRNA synthetase of
Escherichia coli, as well as similar reductive statistics
for Bacillus subtilis. The index is negative in the
majority of cases (75% in E. coli, 60% in B. subtilis, P
= 0.01 and P = 0.126, respectively, one sided sign
tests), indicating an overall avoidance of substrates in
the constitution of their catalysts. This result, which
follows the approach of test level (a) as indicated in
„Materials and Methods‟, was significant by sign tests
at P < 0.05 in E. coli for class I synthetases (90%, P =
0.005, one sided sign test), but not significant for class
II synthetases for B. subtilis (70%, P = 0.086, one
sided sign test). This tendency is confirmed, although
not statistically significantly, in class II synthetases of
E. coli (60%, P = 0.189, one sided sign test) and class
I synthetases of B. subtilis (55%, P = 0.25, one sided
sign test). Fisher‟s method for combining P values
uses the -2*sum of ln (P) as a chi square statistic with
2*k degrees of freedom (where k is the number of
independent tests being combined, two in this case,
for each tRNA synthetase class). Using this method,
the combined P‟s are P = 0.0075 for E. coli and P =
0.104 for B. subtilis. These results confirm in a more
rigorously controlled manner (because amino acid
compositions are compared between „matched‟
proteins, and not between specific proteins and all
proteins at large) the principle that substrates are
avoided in the composition of their catalysts.
Cognate bias in E. coli and B. subtilis are positively
correlated for class I tRNA synthetases (r = 0.63, P =
0.034, one tailed test). This result is qualitatively
similar for class II synthetases (r = 0.16, P = 0.33) and
for all synthetases (r = 0.29, P = 0.116, one tailed
test), though is not statistically significant. Hence, a
BIO © [2012], Copyright CCAAS Hervé Seligmann, BIO 2012, 2, 11-26
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wide variation exists in the extents and directions of
the amino acid cognate bias index, between bacteria,
synthetase classes, and specific synthetases within the
same species and class. These results suggest that
additional phenomena affect cognate bias.
3.2. Tests for E. coli
3.2.1. E. coli: Cognate bias tests within single tRNA
synthetases. For tests within single tRNA synthetases,
but across amino acids (rows in Table 1, test level (b)
in „Materials and Methods‟), the bias was more
frequently more negative for the cognate amino acid
than the biases calculated for non-cognate amino acids
in a non-significant majority of the tRNA synthetases
(65%). This was significant at P <0.05 according to a
sign test within single tRNA synthetases for 11 cases
(7 cases in class I: Gln, Ile and Met, P = 0.000122 for
each; Leu, P = 0.00065; Trp, P = 0.0087; Tyr and Val,
P = 0.00002; 4 cases in class II: Gly, large subunit, P
= 0.000001; His, P = 0.009; Phe, large subunit, P =
0.00002; and Ser, P = 0.00012; all one sided sign
tests). There were also two tRNA synthetases with
significant positive cognate bias, those with cognates
Phe (a different subunit of the tRNA synthetase than
in the previous group) and Arg (P = 0.004 and P =
0.0013, respectively, two-sided sign tests were used
here because the null hypothesis does not expect
positive cognate bias). Note that Phe is one of the
largest amino acids. Because at P < 0.05, when 23
tests are done, one can expect 0.05*23= 1.15 false
positive results, meaning one expects on average one
false detection of a bias. Because there are 13
significant cases in total, one can be sure that overall
the phenomenon of cognate bias exists, but one cannot
be certain that bias exists in any tRNA synthetase. An
extreme method to handle this problem is
Bonferroni‟s adjustment [46]. It divides 0.05 by the
number of tests done. P values that meet that criterion
(P = 0.05/23= 0.00214) are significant at P < 0.05
considering the number of tests done. P values in bold
remain significant according to this overconservative
criterion (in total 11 among 13 cases). According to a
slightly less conservative adjustment, called the
Benjamini-Hochberg adjustment [47] of the
Bonferroni method, all 13 cases remained significant.
The working hypothesis can also be tested by
combining the P values across the various tRNA
synthetases, using Fisher‟s method for combining P
values. This yields chi-square statistics of 129.64 and
103.38 with 10 and 13 degrees of freedom, and P =
4.6*10-18
and P = 3.5*10-11
for class I and II tRNA
synthetases, respectively. The working hypothesis is
hence confirmed by very significant results for each
tRNA synthetase class.
3.2.2. E. coli: Cognate bias tests across tRNA
synthetases. For tests keeping the amino acid
constant, but across tRNA synthetases (columns in
Table 1, test level (c) in „Materials and Methods‟),
two tests can be done. One considers only
comparisons within the class of the specific tRNA
synthetase, and the other considers all tRNA
synthetases. Keeping the class constant, the bias was
more frequently negative for the cognate of the tRNA
synthetase which specifically recognizes this amino
acid than for other tRNA synthetases. The bias is
65% (P = 0.065, one sided sign test) in all the amino
acids, 80% in amino acids with tRNA synthetases
from class I (P = 0.0275, one sided sign test), and 30%
from class II). The bias is significant at P < 0.05 for
11 amino acids, with 8 amino acids from class I tRNA
synthetases (Cys, Leu, Val, P = 0.027 for each; Gln,
Ile and Met, P = 0.0054 each; Trp and Tyr, P =
0.0005, each) and 3 amino acids from class II (Gly
and Thr, P = 0.027, each; Lys, P = 0.0005). There was
one amino acid for which the bias was significantly (P
< 0.05) less negative when it was calculated for tRNA
synthetases specific to those cognate amino acids than
for other non-specific synthetases (Pro from class II, P
= 0.011, two sided sign test because the working
hypothesis does not expect positive cognate bias). The
tests that remain significant at P < 0.05 after
Bonferroni‟s correction are indicated in bold, while
the tests remaining significant according to the
Benjamini-Hochberg adjustment of Bonferroni‟s
correction are underlined. According to the latter
Table 1 (pages 15 and 16). Contrasts in amino acid compositions between sister tRNA synthetases in E. coli.
Rows are subtractions (contrasts) of frequencies (multiplied by 10000) of amino acids in the composition of
evolutionary sister tRNA synthetase from frequencies in compositions of specified tRNA synthetases. Contrasts
for cognate amino acids of the specified tRNA synthetase are in bold. Z indicates the normalized cognate bias
(in bold) divided by the standard deviation of the contrasts in that tRNA synthetase across all amino acids
(column Zb, corresponds to test level (b) in Materials and Methods and in Results), and for that amino acid
across tRNA synthetases (row Zc, corresponds to test level (c) in Materials and Methods and in Results). Sign
tests (see text) use numbers of contrasts that are more negative than the cognate bias. These numbers are
indicated by ± (column (±b) and row (±
c) after those corresponding for Z; for test level c (last rows), the first
number is for cases within the same tRNA synthetase class, and the second number for all tRNA synthetases).
These statistics are also indicated for B. subtilis (Zbb, Zb
c and ±b
b, ±b
c, respectively), N indicates tRNA
synthetase size in E. coli, followed by its size in B. subtilis. Bold numbers indicate statistical significance of
cognate bias at P < 0.05 according to a sign test. Note that in B. subtilis, data are missing for glutamynil tRNA
synthetase which is absent in that genome (complete genome sequence, NC_000964).
BIO © [2012], Copyright CCAAS Hervé Seligmann, BIO 2012, 2, 11-26
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AA composition Arg Cys Gln Glu Ile Leu Met Trp Tyr Val
Synthetase NCBI entry N
I Arg - Cys AAC74946 577-556 96 39 -121 -94 5 -242 74 65 -52 -82
Cys – Arg AAC73628 461-466 -96 -39 121 94 -5 242 -74 -65 52 82
Gln – Glu AAC73774 554 43 32 -111 2 80 -85 -38 -25 15 -5
Glu – Gln AAC75457 471-483 -43 -32 111 -2 -80 85 38 25 -15 5
Ile - Val AAC73137 941-921 -153 65 -49 -225 -174 -12 -81 -8 77 145
Leu – Met AAC73743 860-804 -11 10 -65 1 -125 -86 85 82 64 217
Met - Leu AAC75175 677-664 11 -10 65 -1 125 86 -85 -82 -64 -217
Trp – Tyr AAC76409 334-330 -23 -45 -68 -102 37 -110 134 -82 159 206
Tyr - Trp AAC74709 424-419 23 45 68 102 -37 110 -134 82 -159 -206
Val – Ile AAC77215 951-880 153 -65 49 225 174 12 81 8 -77 -145
II Ala -His AAA03208 876-878 -104 -49 -83 -71 126 -288 28 -50 -137 -14
Asn - Asp AAC74026 466-430 18 1 -43 116 -127 -211 -141 82 106 331
Asp - Asn AAC74936 590-430 -18 -1 43 -116 127 211 141 -82 -106 -331
Gly - Phe AAC76584 303-295 -206 134 263 58 -93 78 -42 137 380 -18
AAC76583 689-679 16 -120 61 -19 -165 46 56 0.1 13 -254
His - Ala AAC75567 424-424 104 49 83 71 -126 288 -28 50 137 14
Lys – Asn, Asp AAC77090 505-499 -1533 -105 -716 -1647 -1042 -1769 -642 -149 -587 -1398
AAC75928 505 -1473 -105 -657 -1647 -1042 -1828 -642 -149 -568 -1398
Phe – Gly AAC74783 795-804 -16 120 -61 19 165 -46 -56 -0.1 -13 254
AAC74784 327 206 -134 -263 -58 93 -78 42 -137 -380 18
Pro – Thr AAC73305 562-564 753 20 356 911 614 951 356 40 277 555
Ser – Pro, Thr AAC73979 430-426 121 6 -24 36 -101 221 -5 -60 -68 -58
Thr - Pro AAC74789 642-643 -753 -20 -356 -911 -614 -951 -356 -40 -277 -555
Z
Zb
-1.11
-0.24
0.87
0.90
-1.28
0.44
-0.02 -1.50
0.13
-15.41
-1.39
-0.90
0.14
-1.29
-0.81
-1.66
-0.57
-0.86
-0.41
±
±b
8, 15
3, 7
2, 4
6, 14
1, 3
6, 11
3, 5
1, 3
4, 8
2, 5
1, 3
1, 4
5, 10
0, 3
2, 4
0, 3
1, 5
2, 6
2, 4
do no nob eb oT
BIO © [2012], Copyright CCAAS Hervé Seligmann, BIO 2012, 2, 11-26
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AA
composition
Ala Asn Asp Gly His Lys Phe Pro Ser Thr
Synthetase Zb Zb
b ±
b | ±b
b
I Arg - Cys 81 -61 -200 4 -104 73 -83 4 -135 112 -0.90, -0.16 19| 11
Cys – Arg -81 61 200 -4 104 -73 83 -4 135 -112 0.37, 0.40 10| 11
Gln – Glu -167 63 -54 1 -115 68 115 81 102 -0.1 -1.92, 0.24 3 | 12
Glu – Gln 167 -63 54 -1 115 -68 -115 -81 -102 0.1 -0.04 9 | -
Ile - Val 158 -197 103 103 87 91 -39 5 163 -58 -1.46, 0.12 3 | 9
Leu – Met 47 25 10 100 -50 37 -233 4 -327 214 -0.86, -0.84 4 | 2
Met - Leu -47 -25 -10 -100 50 -37 233 -4 327 -214 -0.85, 0.12 3 | 11
Trp – Tyr 49 -83 63 -203 98 58 -230 143 74 -76 -0.70, -1.10 6 | 3
Tyr - Trp -49 83 -63 203 -98 -58 230 -143 -74 76 -1.37, -0.82 2 | 5
Val – Ile -158 197 -103 -103 -87 -91 39 -5 -163 58 -1.22, -1.07 2 | 4
II Ala -His 72 -24 166 -54 62 159 128 -79 193 19 0.74, 0.11 14| 11
Asn - Asp -86 39 -114 -42 19 -135 109 -50 176 -49 -1.15, -0.24 13| 6
Asp - Asn 86 -39 114 42 -19 135 -109 50 -176 49 -0.40, 1.61 9 | 18
Gly - Phe -196 -96 -53 23 -171 27 -183 41 -68 -13 0.25, -0.05 12| 4
353 -42 86 -228 -69 155 196 -30 -98 41 -1.36 1 | -
His - Ala -72 24 -166 54 -62 -159 -128 79 -193 -19 -0.65, -2.21 6 | 1
Lys – Asn, Asp -1735 -806 -1435 -1361 -380 -871 -1160 -809 -764 -1092 -2.20, -0.58 10| 4
-1715 -825 -1475 -1401 -400 -950 -1120 -809 -744 -1053 -2.42 9 | -
Phe – Gly -353 42 -86 228 69 -155 -196 30 98 -41 -1.17, -1.05 2 | 2
196 96 53 -23 171 -27 183 -41 68 13 2.02 18| -
Pro – Thr 792 416 753 693 238 475 574 376 317 535 2.01, -0.47 8 | 2
Ser – Pro, Thr 41 32 37 -2 -27 -72 -94 14 -82 85 -1.37, -0.42 3 | 6
Thr - Pro -792 -416 -753 -693 -238 -475 -574 -376 -317 -535 -2.86, 0.07 9 | 9
Zc
Zbc
0.10
0.02
0.11
-0.64
0.18
2.03
-0.38
-0.05
-0.33
-1.12
-2.34
-0.47
-0.39
-1.43
1.10
-1.18
-0.25
-0.58
-1.16
0.11
±c
±bc
5, 12
6, 11
6, 11
2, 5
7, 16
9, 18
2, 3
4, 7
4, 8
1, 1
0, 0
2, 4
6, 11
0, 1
9, 19
0, 2
5, 9
4, 8
2, 3
4, 10
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method, 3 among the 5 tests (significant at P = 0.027)
remain significant, but because the decision of which
three among the five is arbitrary, none can be
specifically assigned as significant. Nevertheless,
according to the latter approach, 10 among 20 tests are
significant at P < 0.05 after adjusting P values.
Analyses can be repeated by considering all tRNA
synthetases across classes. Following this approach,
there were 12 cognates with P < 0.05 (8 in class I: Cys
and Met, P = 0.003, each; Gln, Trp and Tyr, P =
0.0006, each; Glu and Leu, P = 0.01, each; and Val, P
= 0.029; and 3 in class II: Gly and Thr, P = 0.0006,
each; and Lys, P = 0.0000005). There were 3 cognates
for which there was significant (P <0.05) positive
cognate bias, one from class I (Arg, P = 0.021) and
two from class II (Asp and Pro, P = 0.006 and P =
0.00002, respectively). These results also confirm the
working hypothesis at the level of test 3. Combining
the P values according to Fisher‟s method, this yields
P = 5.6*10-14
and P = 3.2*10-7
for class I and II,
respectively.
Results on positive cognate biases, together with
parallel ones from the previous section, suggest that
positive cognate bias exists in some few cases
(nevertheless 10%). Consequently, cognate bias is
bimodal, with a majority of cases in the realm of the
negative bias expected by the working hypothesis, but
positive bias apparently occurs too. The working
hypothesis expects negative bias as a result of the
principle of minimizing costs of protein synthesis by
avoiding the cognate/substrate of the protein in the
protein‟s own composition. This hypothesis is
incompatible with positive bias. It accommodates
cases where no systematic tendency for positive bias
exist (hence weak positive bias), but not cases where
the positive cognate bias is strong and statistically
detectable. This implies that a factor other than cost
minimization of cognate usage operates here, at least
in some cases, so as to create these rarer positive
biases.
3.2.3. E. coli: Effects of intrinsic usage and
environmental abundance. The bias against the
cognate amino acid decreases in E. coli as a function
of the usage frequency of that amino acid in the
species‟ proteome (table 1 in [27]). I found this
tendency, yet it was not statistically significant (r =
0.31 and P = 0.093 for all synthetases; r = 0.41 and P
=0.118 for class I; r = 0.20 and P = 0.29 for class II,
all one tailed tests). There was also no statistically
significant trend between cognate amino acid bias and
the abundance of the amino acid in the colon, the
habitat of E. coli (classes pooled, r = -0.36, P = 0.147;
class I, r = -0.23; class II, r = 0.23, P = 0.51; two
tailed tests). This suggests that substrate avoidance in
E. coli affects mainly the composition of
rare/expensive amino acids, but not of highly
expressed synthetases. If one remains within the issue
of cost minimization of protein synthesis metabolism,
these data suggest that E. coli protein compositions
are relatively independent of the composition of its
host environment, but are more affected by the
composition of its own intrinsic cellular environment,
because it seems to minimize costs according to its
own usage frequency of an amino acid. This result
could be interpreted as resulting from its
endosymbiontic life habit.
3.3. Tests for B. subtilis
3.3.1. B. subtilis: Cognate bias tests within single
tRNA synthetases. In Bacteria subtilis, for tests
within single tRNA synthetases (rows in Table 1, test
level (b) as indicated in „Materials and Methods‟), but
across amino acids, in 68% of the tRNA synthetases
(P = 0.042, one sided sign test), bias was more
frequently negative for the cognate amino acid than
the biases calculated for the 19 non-cognate amino
acids (56% for class I (P = 0.25), 80% for class II (P =
0.027), one sided sign tests). The negative bias is
significant at P 0.05 (one sided sign test) in 58% (11
among 19 cases) of all tRNA synthetases, 40% for
class I (Leu, P = 0.00018; Trp, P = 0.0011; Tyr, P =
0.016 and Val, P = 0.0048, one sided sign tests), and
70% for class II (Asn and Ser, P = 0.042 in each; Gly
and Lys, P = 0.0048 in each; His, P = 0.00002; Phe
and Pro, P = 0.00018 in each). There was one class II
tRNA synthetase for which cognate bias was
significantly less negative than for the other non-
cognate amino acids (aspartyl synthetases, P =
0.00004). Hence these results independently confirm
the existence of positive cognate bias, in addition of
negative cognate bias. Combining P values using
Fisher‟s method yields chi-square = 44.7, and chi-
square = 66.9 for class I and II, respectively, which
yields P = 6.98*10-7
and P = 1.76*10-10
, respectively.
This P value is greater by 11 orders of magnitude than
that for class I tRNA synthetases in E. coli, and
greater by one magnitude than for class II tRNA
synthetases in E. coli. Overall, the phenomenon of
negative cognate bias exists and is clearly detectable
in both species at the level of single tRNA
synthetases, but it is even more obvious in E. coli than
in B. subtilis.
3.3.2. B. subtilis: Cognate bias tests across tRNA
synthetases. For tests keeping the amino acid
constant, but across tRNA synthetases, test level (c) as
indicated in „Materials and Methods‟, tests can be
done considering only cognates from tRNA
synthetases of the same class, and considering all
cognates. Considering only those from tRNA
synthetases within a given class, bias was more
frequently more negative for the tRNA synthetase for
which this amino acid is its cognate amino acid than
for the other non-specific tRNA synthetases from the
same class in 74% of the cases (P = 0.016, one sided
sign test),66% for tRNA synthetases from class I and
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80% from class II (P = 0.127 and P = 0.027, respectively, one
sided sign tests). This was significant at P < 0.05 for 9 (47%)
of the cases, 4 (44%) from class I (Leu and Tyr, P = 0.0098, in
each; Trp and Val, P = 0.045, in each) and 5 (50%) from class
II (Asn and Lys, P = 0.027 in each; His, Phe and Pro, P =
0.0054 in each).
These analyses were repeated pooling all tRNA synthetases,
independently of their class. According to this approach, there
was negative bias for cognates from 13 (68%), and this bias
was significant for 9 (47%) cases, 4 from class I ( Leu, P =
0.0011; Trp and Val, P = 0.0048, in each; and Tyr, P =
0.0159) and 5 from class II (Asn, P = 0.0159; His and Phe, P =
0.000019 in each; Lys, P = 0.0048; and Pro, P = 0.00018).
There was statistically significant positive cognate bias for one
cognate, Asp (P = 0.000038). Combining P values to test for
the working hypothesis considering all tRNA synthetases
together yields chi square statistics 53.95 and 90.28 with P =
1.87*10-5
and 6.62*10-11
for class I and II respectively. This is
9 orders of magnitude less than for class I in E. coli, and 4
orders of magnitude more than for class II in E. coli.
3.3.3. B. subtilis: Effects of environmental abundance and
intrinsic usage. In B. subtilis, avoidance of cognate amino
acids decreases (albeit not statistically significantly) with
usage frequencies of those amino acids in the proteome (r = -
0.01, P = 0.96, 2 tailed test, for all synthetases; class I
synthetases, r = -0.17, P = 0.67 and r = 0.51, P = 0.13 for class
II synthetases). Although these results are not per se
statistically significant, the tendencies are systematically
opposite of those from E. coli, where negative cognate bias
seems to decrease with usage. Figure 1 plots cognate amino
acid bias as a function of the abundance of the amino acid in
the soil, the environment of B. subtilis. It shows that cognate
amino acid bias, in terms of avoidance, occurs for synthetases
Phe
Thr
Ser
Gly
Ala
Asp
Asn
Leu
Arg
HisPro
TrpLys,Val,Tyr
Met
Cys, Glu
Ile
-0.03
0
0.03
0 1 2 3 4
Abundance category in Soil
Co
gn
ate
av
oid
an
ce
Figure 1. Cognate bias in B. subtilis tRNA synthetases as a
function of amino acid abundance class in the soil.
that esterify amino acids that are rare in the
soil, and those that are abundant in the soil.
Positive cognate bias exists for amino acids
with intermediate abundances. These results
contrast with those in E. coli, where substrate
avoidance seems unrelated to environmental
concentrations, and decreases, rather than
increases, with amino acid usage.
4. Discussion
4.1. General overview. The results described
here confirm the main result in [27] that
cognate amino acid avoidance exists. Results
also confirm that cost minimization exists.
The cost minimization might be even slightly
stronger in the bacterium with symbiontic
lifestyle than in the free living B. subtilis, as
suggested by lower numbers of specific tRNA
synthetases with significant negative bias
detected through test levels b and c in B.
subtilis (tests b and c, 11 and 9, respectively)
than in E. coli (11 and 12). Regarding the
hypothesis that symbionts are less likely to
minimize costs, it is strengthened by the fact
that positive cognate bias is more common in
E. coli than in B. subtilis: tests b and c
detected 5 statistically significant positive
biases in total in E. coli, but only two in B.
subtilis. This observation could be interpreted
as a confirmation of the effects of life style on
cost minimization by cognate bias.
4.2. Statistical considerations. Results
discovered by using a controlled (natural)
experimental setup here are very significant
for specific tRNA synthetases and amino
acids, in spite of the use of the conservative
(and robust) sign test. Results stress the
increased power of this system at uncovering
patterns, and at detecting more subtle and
finely tuned phenomena than the approach
used in [27].
Although analyses presented in this work
are of confirmative nature of principles
already described earlier, I used statistical
adjustment methods to control the multiplicity
of tests only as a precaution, but not to
confirm the cases with significant value at P <
0.05 without adjustments [46]. Considering
that such methods are overconservative and
can artificially inflate negative results [47],
the methods of correcting test multiplicity can
be valuable if the main aim is to be certain
that a phenomenon exists. Therefore they are
important for the fewer cases where positive
cognate bias was detected. However previous
studies already showed that the phenomenon
of negative cognate bias occurs, hence
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asserting the existence of negative bias is not the
primary aim here.
The results suggest that the comparative system
formed by the two classes of phylogenetically related
tRNA synthetases could prove more useful when
trying to uncover the functional aspects of amino acid
conservation and replacements at active sites.
However, one has to bear in mind that the increased
statistical power comes at the costs of the generality of
the results: observations within tRNA synthetases may
be affected by the specifics of tRNA synthetase
biology (including differences between classes I and
II). This situation is not exceptional for this specific
system. The more precise approach used here is
considered to complete, rather than replace, the more
general results based on less „carefully‟ designed
comparisons [27]. The fact that both analyses confirm
the same principles increases the confidence that the
hypothesized principle is indeed valid at biological
level.
4.3. E. coli versus B. subtilis
It makes sense that the absolute rarity in the medium
leads to cost minimization for an amino acid.
However, it is less straightforward to understand why
cognate avoidance occurs for amino acids that are
frequent in the medium. Note that proteome-wide
amino acid usage in B. subtilis correlates positively
with their abundances in the soil (r = 0.42, P = 0.031,
one sided test). However, while cognate avoidance
exists in E. coli and B. subtilis at similar levels
(though slightly weaker in B. subtilis), patterns clearly
contrast between them. It is not clear whether the
differences in associations between cognate avoidance
and environmental versus proteomic abundances are
related to the endosymbiontic versus „free living‟
lifestyles of these two bacteria. It makes sense that the
endosymbiont is less dependent on variation in amino
acid environmental abundances because these are all
kept high by the host (or with predictable daily
fluctuations), and hence cognate avoidance will
mainly reflect the own metabolic needs of E. coli. In
contrast, results in B. subtilis indicate two factors
accounting for the differences: one related to
environmental amino acid abundances (its
environmental abundances seem to affect proteomic
amino acid abundances, as suggested by the positive
correlation between proteome-wide amino acid
abundances and those in the soil), and the other
unknown factor (for amino acids which are abundant
in the soil) that might be due to intrinsic metabolic
needs (perhaps usage frequency, as in E. coli). These
results would be in line with general principles
deduced from observations that usually, parasites and
symbionts minimize metabolic costs less than
comparable free living organisms [7-8, 48]. There is
here an apparent conundrum: parasites minimize less
costs at the level of protein synthesis, including by
having larger proteins and hence longer protein coding
genes. Since they minimize costs of DNA replication
by reducing genome sizes, they are not (or less)
minimizing costs according to proteins, but are more
than free-living bacteria according to genome size.
4.4. Negative cognate bias and cost minimization
strategy by avoiding large residues
The data analyzed here enable to develop the theory
about cost minimization of protein metabolism
beyond confirmation of previous results [27]. Cost
minimization can be related to various properties of
proteins, and be estimated using different methods
[49]. I previously used the (negative) correlation
between amino acid molecular weight and its
proteomic usage to estimate an overall extent of cost
minimization of protein metabolism based on the
usage of all amino acids [7]. In that approach, the
more negative the correlation coefficient, the rarer
large amino acids are in the composition of proteins.
The analyses presented above are related specifically
to avoiding usage of cognate amino acids. This is a
different type of cost avoidance. Figure 2 shows that
in E. coli, extents of negative cognate avoidance are
negatively associated (r = -0.41, P = 0.049, two tailed
test) with extents of cost minimization in relation to
amino acid size. This is estimated by the negative
correlations between amino acid usages and their
molecular weights. Each datapoint in Figure 2 is for
a given tRNA synthetase (note that for Gly, Lys and
Phe, the tRNA synthetases consist of two subunits).
This correlation is also qualitatively confirmed for B.
subtilis (r = -0.07) but is clearly much weaker and not
statistically significant in this species. This result for
E. coli suggests a balancing situation between
minimizing metabolic energetic costs (at the level of
favoring usage of small amino acids) and biosynthetic
time (negative cognate bias mainly avoids long
durations until a tRNA loaded with the cognate is
encountered during protein synthesis). This inverse
proportionality between the two phenomena suggests
that overall cost minimization reached an equilibrium
where increasing cost minimization according to one
factor (time) will decrease cost minimization
according to the other factor (energy). This implies the
possibility that protein function can be affected by
replacing one amino acid by another one due to cost
considerations. Hence decreases in protein
functionality due to avoiding cognates can be
compensated by using more expensive non-cognates.
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Figure 2. Cognate bias (Z standardized) in E. coli tRNA
synthetases as a function of cost minimization of amino acid
abundances in these proteins, estimated by the Pearson
correlation coefficient r between amino acid abundances and
the molecular weight of the amino acids for each protein.
For both x and y axes, the more negative values indicate
greater cost minimization. Note that cognate bias can differ
widely for different subunits of the same tRNA synthetase,
such as for those acylating Phe, as well as be very similar, as
found for those acylating Lys.
The fact that this phenomenon is not observed in B.
subtilis suggests that in this species the system did not reach
equilibrium in this respect. This makes sense because an
evolutionary equilibrium requires stable and especially
predictable environmental conditions. The colon might be
more stable than the soil.
4.5. Negative cognate bias and protein size. The
interesting result about a compensating equilibrium between
amino acid energetic cost minimization and cognate usage in
tRNA synthetases in the previous section can be
independently confirmed by looking at a further factor
affecting costs of protein synthesis, its length. As expected,
tRNA synthetases are larger in the endosymbiont E. coli
than in the free living B. subtilis (in 63 percent of the cases,
which is however not statistically significant in this small
sample according to a sign test). However, if one considers
that three proteins forming tRNA synthetases are missing in
B. subtilis (for example, the smaller subunits for tRNA
synthetase Lys and Phe), it is clear that the freeliving
bacterium reduces protein size as compared to E. coli. When
one compares cost minimization between these two species
for homologous tRNA synthetases, by calculating the
difference between correlation coefficients r between amino
acid usages and their molecular weights, one finds that the
correlations are more negative (hence more cost
minimization) in E. coli than in B. subtilis in 16 out of 20
cases (P = 0.012 according to a two sided sign test). This
situation is opposite to the one found for protein
length as cost criterion, according to which E.
coli seems to minimize costs less than B.
subtilis. This suggests compensatory effects
between different types of cost minimization, as
indicated in Figure 2. Indeed, the increase in
tRNA synthetase length observed in E. coli is
inversely proportional to the increase in cost
minimization in that species, as compared to B.
subtilis, and as estimated by the correlation
between amino acid usage and their molecular
weights (r = -0.39, P = 0.044, one tailed test,
data not shown; a one tailed test is used here
because a negative correlation is expected,
considering the result in Figure 2 that suggests
that compensation is expected). Besides
confirming the principle of reaching
equilibrium in a stable/predictable rather than
an unpredictable environment, and that of
optimization according to several criteria at
once, these results suggest that a
unidimensional approach to proteins is
inadequate, that these are complex constructs
reacting to different pressures, and that their
structure is optimized in relation to several
factors, even if those reflect (more or less) the
same simplistic economic principle.
4.6. Cognate bias and functional properties:
secondary structure formation. A wide
variation exists among amino acids in terms of
cognate bias, from negative to positive bias, and
this varies also between E. coli and B. subtilis.
One could assume that this extent depends on
how much avoidable the cognate is in the
structure of the protein. For example,
membrane proteins are very hydrophobic and
with low polarity, hence these could probably
be limited in the extents they could avoid using
hydrophobic, non-polar amino acids in their
structure, even if these were not optimal from
the point of view of metabolic/energetic
considerations. The same principle functions
also for tRNA synthetases, which are not
membrane bound but have other function-
associated constraints. Class I tRNA
synthetases have the Rossmann fold, both
classes are made up of anti-parallel beta strands
[50]. Amino acid polarity is also an important
property in protein folding, low polarity
favoring hydrophobic interactions, while polar
regions with opposite charges tend to attract
each other and those with similar charges repel
each other, both principles that contribute to
protein folding. Hence one expects that cognate
bias associates with the betasheet-formation
index, which estimates the tendency of an
amino acid to be more (or less) frequent than
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expected in protein regions forming
betasheet secondary structures. Indeed,
cognate bias in E. coli correlates negatively
with the beta index (class I, r = - 0.79, P =
0.0033; class II, r = -0.45, P = 0.096; all, r =
-0.69, P = 0.00038, one tailed tests). This
phenomenon was much weaker for B.
subtilis (class I, r = - 0.20, P = 0.30; class II,
r = -0.51, P = 0.066; all, r = -0.14, P = 0.28,
one tailed tests). Because the structure of
both classes of tRNA synthetases is based
on antiparallel betasheets, it is not surprising
to see that correlations of cognate bias with
antiparallel betasheet indices (E. coli: class
I, r = -0.75, P = 0.0062; class II, r = -0.57, P
= 0.043; all, r = -0.73, P = 0.00013; B.
subtilis: class I, r = -0.72, P = 0.014; class II,
r = -0.46, P = 0.09; all, r = -0.38, P = 0.054,
see Figure 3) are generally stronger than
those indicated above, for indices calculated
pooling parallel and antiparallel betasheets.
It is unlikely that this difference between the
two species indicates that secondary
structure formation during protein folding is
not related to the beta index in B. subtilis,
but rather that the latter optimizes cognate
bias according to additional criteria. Similar
analyses for another important amino acid
property, polarity, suggest for E. coli (but
not B. subtilis) differences between class I
and II tRNA synthetases in relation to
cognate bias optimization: in class I, cognate
bias decreases with cognate polarity (r = -
0.66, P = 0.038, two tailed test), while the
tendency was opposite for class II (r = 0.31,
P = 0.38, two tailed test). Figure 4 shows
that this difference between classes is
mainly due to the tRNA synthetases with
cognates Phe and Pro. Other class II tRNA
synthetases overall fit into the trend
observed for class I tRNA synthetases. This
trend suggests that polar cognates can more
be avoided in the structure of its tRNA
synthetase than non-polar ones. This
suggests that polar interactions are not
preponderant in tRNA synthetase folding,
besides the exceptions for the tRNA
synthetases of Phe and Pro indicated above.
For B. subtilis, this tendency for greater
negative cognate bias in polar cognates
exists but is weaker (class I, r = -0.44, P =
0.118; class II, r = -0.40, P = 0.126, one
tailed tests).
Further analyses reveal again differences
between class I and II tRNA synthetases in
E. coli, in relation to cognate bias. Betasheet
formation indices above unity indicate that a
specific amino acid is more frequently part
of betasheets than of other structural domains. Amino acids that
are avoided in beta sheets get values below unity. Such residues
are also important for betasheet formation, because this involves
(relatively short) regions that should not form a beta sheet.
Hence one could assume that also the absolute value of the
subtraction of the betasheet index from unity, as an estimate of
structural specificity of an amino acid in relation to betasheet
Figure 3. Cognate bias in tRNA synthetases as a function of the
betasheet formation index of the amino acids for E. coli (filled
symbols) and B. subtilis (circles). Negative cognate bias is
stronger for amino acids with low tendencies to form beta
sheets.
Figure 4. Cognate bias in E. coli tRNA synthetases as a
function of amino acid polarity.
formation, might associate with cognate bias: those with high
specificity could not be avoided, whether this specificity
indicates specificity promoting, or preventing betasheet
formation. This is the case for class II tRNA synthetases in E.
coli (r = 0.66, P = 0.038, one tailed test) but the exact opposite
is observed for class I (r =-0.79, P = 0.0065, two tailed
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test; Figure 5). Tendencies were similar but weaker for B.
subtilis (class I, r =-0.24, P = 0.27; class II, r = 0.27, P = 0.23,
one tailed tests). The results indicate that cognate avoidance is
prevented for class II tRNA synthetases if that amino acid has
a high impact on betasheet formation, whether by preventing
or promoting it. Interpreting the result for class I is less
straightforward, as the more an amino acid impacts secondary
structure formation, the more it is avoided. However, results
from Figure 4 indicated that nonpolar (hydrophobic)
interactions, a physicochemical property, rather than a
structural one (beta index), is optimized in relation to cognate
bias in class I tRNA synthetases of E. coli. This seems to be
the explanation for the negative correlation with cognate bias
observed in class I tRNA synthetases: the structural impact on
betasheet formation used in Figure 5 increases with amino
acid polarity for class I tRNA synthetases (class I, r = 0.78, P
= 0.0077; class II, r = 0.27, P = 0.45, two tailed tests). These
analyses focused on cognate bias confirm the antiparallel
betasheet structure of tRNA synthetases, but further analyses
show that additional properties are important for protein
structure, and that there are differences between the tRNA
synthetase classes. The differences in this respect between the
two bacteria suggest that much can be learned from such
analyses. It is probable that comparing larger numbers of
species, as similar data on tRNA synthetase sequences are
available from genomic sequences of numerous species, could
enable to study the details of interactions between principles
of protein folding and the species‟ life histories, based on
relatively simple analyses of cognate bias.
Figure 5. Cognate bias in E. coli tRNA synthetases as a
function of amino acid structural impact on betasheet
formation. The x axis is the absolute value of the subtraction
of the betasheet index from unity. Hence the more an amino
acid affects betasheet formation, whether by promoting or
preventing it, the larger the value on the x axis. In class II
tRNA synthetases, cognate avoidance decreases with
structural impact of the cognate on betasheet
formation, the opposite occurs for class I
tRNA synthetases.
4.7.Cognate bias and functional properties:
tRNA editing. Many tRNA synthetases
possess catalytic sites that are not involved in
aminoacylation of the tRNA, but in its pre-
and post-transfer editing, thus preventing
misacylations of tRNAs by non-cognate
amino acids [51]. The existence of specific
sites with editing activities has been indicated
for several tRNA synthetases (class I: Ile [52],
Leu [53], Met [54], Val [55]; class II: Ala
[56], Asn [57], Phe [58], Pro [59], Thr [60])
but evidence indicates the lack of specific
editing sites for some class I tRNA
synthetases (Cys [61], Gln [62] and Tyr [63])
and apparently inefficient pre-transfer editing
for the class II tRNA synthetase
aminoacylating Lys [64]. The nature of cost
minimization of amino acid composition is
that an „expensive‟ amino acid is used only
where it is unavoidable. Obviously, catalytic
and other functionally important sites (such as
editing sites) are regions where cost
minimization (by cognate avoidance) is
unlikely because of their functional
importance. Functional sites are formed by
relatively few residues, but tRNA synthetases
with editing sites should on average possess
more functionally important regions than
those lacking one. Hence one can expect a
slightly less negative cognate bias for tRNA
synthetases that possess editing sites. No such
effect could be detected for cognate biases of
E. coli and B. subtilis. However, after
controlling for the effect of cognate polarity,
using the regression in Figure 4 to calculate
residual cognate bias, a positive association
between cognate bias and editing sites can be
detected, considering that the confirmed lack
of editing site is „-1‟ for the tRNA synthetases
of Cys, Gln and Tyr, „1‟ for those with
confirmed editing sites, and „0‟ for the others
(r = 0.44, P = 0.027; Spearman rank
correlation rs = 0.52, P = 0.0112, one tailed
tests, see Figure 6). Hence after controlling
for the effects of a functional constraint on
cognate bias that relates to the overall
structure of the protein, the effect of the
number of specific functional sites on cognate
bias becomes detectable. It is notable that no
such effect is observed if a similar residual
analysis is done, but using the regression lines
in Figure 5, where cognate bias is shown
constrained by the formation of betasheets.
This could mean that the function of editing
sites is unrelated to betasheet formation, but
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that polarity affects interactions with loaded tRNAs. This
interesting hypothesis seems plausible, but should not be
overstressed at this stage. It is nevertheless a potential
example of how the analysis of cognate bias, in relation to
other properties, can reveal how proteins function.
Figure 6. Residual cognate bias in E. coli tRNA synthetases
as a function of the presence of editing sites in the tRNA
synthetase. The y axis is the residual cognate bias calculated
from the regression line in Figure 4. The status of editing sites
on the x axis is: -1, tRNA synthetases for which evidence
exists for a lack of editing site; 1, tRNA synthetases with
confirmed editing site(s); 0, tRNA synthetases with no
reported information.
4.8. Cost minimization hypotheses. I have already expressed
my disappointment that so much biology can be understood
via such a simple economy-minded principle as amino acid
cost minimization [7]. Similar principles also predict high
densities of off frame codons in protein coding genes,
reducing costs of synthesis of dysfunctional proteins after
ribosomal slippages [65-67]. It also seems that the adaptation
for translational activity of antisense tRNAs, tRNAs templated
by DNA complementing regular, sense tRNA genes, is also
governed by cost minimization principles [68-70], of reducing
genome size. Some results even confirm that selection favors
the compensatory combination of codon-anticodon
mismatches with tRNA misacylations by non-cognates, which
result in correct amino acid insertion because the mismatched
codon codes for the misacylated amino acid [71-73]. The latter
effects predicted by cost minimization principles (in these
cases in relation to inaccuracies in protein synthesis) could
have been expected to be weak, these nevertheless seem to
have substantial effects on genomes [73]. This means that cost
minimization principles are a powerful approach for the
analysis of biochemical systems. These results present serious
challenges to those promoting neutralistic views of evolution
[74].
5. Conclusions
The principle of cost minimization of protein
synthesis predicts that cognates are avoided in
tRNA synthetases, as compared to closely
related tRNA synthetases with a different
cognate. This negative cognate bias is more
easily detected in a phylogenetically matched
setup than when comparing cognate abund-
ances in specific proteins with abundances in
randomly selected proteins. The bias is also
found less negative in the free living B.
subtilis than in the endosymbiont E. coli.
However, cost minimization is a multidimen-
sional phenomenon, and has to be observed
simultaneously at different levels. They are,
for instances, avoidance of costly residues
(independently of cognate status), protein
size, and negative cognate bias. In E. coli,
these various components of protein meta-
bolic costs seem to be at evolutionary
equilibrium, where one component is at the
expense of the other. This is probably due to
constraints related to the protein‟s function.
Analyses show that cognate avoidance is
prevented for amino acids that promote
betasheets, the major secondary structure of
tRNA synthetases. They also reveal that non-
polar interactions are important for class I
tRNA synthetases, but not class II. Cognate
avoidance is found to be greater in tRNA
synthetases lacking editing sites, which is
probably due to the fact that these proteins
possess more functionally critical sites/
regions that limit cost minimization. These
phenomena are not detected in B. subtilis.
This is possibly because its environment is
less stable, and proteins do not evolve toward
a stable steady state structure that integrates
functional and cost minimization constraints.
Acknowledgments
I thank Neeraja M. Krishnan for comments on
a previous version of this manuscript.
References
[1] Barrai, I.; Volinia, S.; Scapoli, C. The
usage of oligopeptides in proteins
correlates negatively with molecular
weight. Int. J. Peptide Prot. Res. 1995, 45,
326-331. http://dx.doi.org/10.1111/j.1399-3011.1995.tb01045.x
[2] Dufton, M.J. Genetic code synonym
quotas and amino acid complexity: Cutting
the cost of proteins? J. Theor. Biol. 1997,
187, 165-173. http://dx.doi.org/10.1006/jtbi.1997.0443
BIO © [2012], Copyright CCAAS Hervé Seligmann, BIO 2012, 2, 11-26
11
http://ccaasmag.org/BIO
[3] Ikemura, T. Correlation between the abundance of
Escherichia coli transfer RNAs and the occurrence
of the respective codons in its protein genes. J. Mol.
Biol. 1981, 146, 1-21. http://dx.doi.org/10.1016/0022-2836(81)90363-6
[4] Bennetzen, J.L.; Hall, B.D. Codon selection in
Saccharomyces cerevisiae. J. Biol. Chem. 1982,
257, 3026-3031.
[5] Gouy, M.; Gautier, C. Codon-usage in bacteria:
correlation with gene expressivity. Nucl. Acids Res.
1982, 10, 7055-7074. http://dx.doi.org/10.1093/nar/10.22.7055
[6] Akashi, H.; Gojobori, T. Metabolic efficiency and
amino acid composition in the proteomes of
Escherichia coli and Bacillus subtilis. PNAS 2002,
99, 3695-3700. http://dx.doi.org/10.1073/pnas.062526999
[7] Seligmann, H. Cost minimization of amino acid
usage. J. Mol. Evol. 2003, 56, 151-161.
http://dx.doi.org/10.1007/s00239-002-2388-z
[8] Das, S.; Ghosh, S.; Pan, A.; Dutta, C.
Compositional variation in bacterial genes and
proteins with potential expression level. FEBS
Letters 2005, 579, 5205-5210.
http://dx.doi.org/10.1016/j.febslet.2005.08.042
[9] Swire, J. Selection on synthesis cost affects
interprotein amino acid usage in all three domains
of life. J. Mol. Evol. 2007, 64, 558-571.
http://dx.doi.org/10.1007/s00239-006-0206-8
[10] Raiford, D.W.; Heizer, E.M.; Miller, R.V.;
Akashi, H.; Raymer, M.L.; Krane, D.E. Do amino
acid biosynthetic costs constrain protein evolution
in Saccharomyces cerevisiae? J. Mol. Evol. 2008,
67, 621-630. http://dx.doi.org/10.1007/s00239-008-9162-9
[11] Perlstein, E.O.; de Bivort, B.L.; Kunes, S.;
Schreiber, S.L. Evolutionary conserved
optimization of amino acid biosynthesis. J. Mol.
Evol. 2007, 65, 186-196. http://dx.doi.org/10.1007/s00239-
007-0013-x
[12] Das, S.; Pan, A.; Paul, S; Dutta, C. Comparative
analyses of codon and amino acid usage in
symbiotic island and core genome in nitrogen-
fixing symbiotic bacterium Bradyrhizobium
japonicum. J. Biomol. Struct. & Dynamics 2005,
23, 221-232.
[13] Chanda, I.; Pan ,A.; Saha, S.K.; Dutta, C.
Comparative codon and amino acid composition
analysis in Tritryps-conspicuous features of
Leishmania major. FEBS Letters 2007, 581, 5751-
5758. http://dx.doi.org/10.1016/j.febslet.2007.11.041
[14] Brocchieri, L.; Karlin, S. Protein length in
eukaryotic and prokaryotic proteomes. Nuc. Acids
Res. 2005, 33, 3390-3400.
http://dx.doi.org/10.1093/nar/gki615
[15] Warringer, J.; Blomberg, A. Evolutionary
constraints on yeast protein size. BMC Evol. Biol.
2006, 6, 61. http://dx.doi.org/10.1186/1471-2148-6-61
[16] Gong, X.; Fan, S.; Bilderbeck, A.; Li, M.; Pang,
H.; Tao, S. Comparative analysis of essential genes
and nonessential genes in Escherichia coli K12.
Mol. Genet. Genomics 2008, 279, 87-94.
http://dx.doi.org/10.1007/s00438-007-0298-x
[17] de Bivort, B.L. ; Perlstein, E.O. ; Kunes, S. ;
Schreiber, S.L.Amino acid metabolic origin as an
evolutionary influence on protein sequence in
yeast. J. Mol. Evol. 2009, 68, 490-497.
http://dx.doi.org/10.1007/s00239-009-9218-5
[18] Sajitz-Hermstein, M.; Nikoloski, Z. A novel
approach for determining environment-specific
protein costs: the case of Arabidopsis thaliana.
Bioinformatics 2010, 26, i582-i588.
http://dx.doi.org/10.1093/bioinformatics/btq390
[19] Smitch, D.R.; Chapman, M.R. Economical
evolution: Microbes reduce the synthetic cost of
extracellular proteins. mBio 2011, 1, e00131-10.
[20] Heizer, E.M.; Raymer, M.L.; Krane, D.E. Amino
acid biosynthetic cost and protein conservation. J.
Mol. Evol. 2011, 72, 466-473.
http://dx.doi.org/10.1007/s00239-011-9445-4
[21] Mazel, D.; Marlière, P. Adaptive eradication of
methionine and cysteine from cyanobacterial light-
harvesting proteins. Nature 1989, 341, 245-248.
http://dx.doi.org/10.1038/341245a0
[22] Baudouin-Cornu, P.; Surdin-Kerjan, Y.; Marlière,
P.; Thomas, D. Molecular evolution of protein
atomic composition. Science 2001, 293, 297-300.
http://dx.doi.org/10.1126/science.1061052
[23] Barton, M.D.; Papp, B.; Delneri, D.; Oliver, S.;
Rattray, M.; Bergman, C. Systems biology of
energetic and atomic costs in the yeast
transcriptome, proteome, and metabolome. Nature
Preceedings 2008,
http://hdl.handle.net/10101/npre.2008.1841.1
[24] Jankovic, B.; Seoighe, C.; Alqurashi, M.;
Gehring, C. Is there evidence of optimisation for
carbon efficiency in plant proteomes? Plant Biol.
2011, 13, 831-834. http://dx.doi.org/10.1111/j.1438-
8677.2011.00494.x
[25] Grzymski, J.J.; Dussaq, M.A.The significance of
nitrogen cost minimization in proteomes of marine
microorganisms. ISME J., 2011.
http://dx.doi.org/10.1038/ismej.2011.72
[26] Bragg, J.G.; Wagner, A. Protein material costs:
single atoms can make an evolutionary difference.
Trends Genetics 2009, 25, 5-8.
http://dx.doi.org/10.1016/j.tig.2008.10.007
[27] Alves, R.; Savageau, M.A. Evidence of selection
for low cognate amino acid bias in amino acid
biosynthetic enzymes. Mol. Microbiol. 2005, 56,
1017-1034. http://dx.doi.org/10.1111/j.1365-2958.2005.04566.x
BIO © [2012], Copyright CCAAS Hervé Seligmann, BIO 2012, 2, 11-26
11
http://ccaasmag.org/BIO
[28] LéJohn, H.B.; Cameron, L.E.; Rennie, S.L.
Molecular characterization of an nad-specific
glutamate-dehydrogenase gene inducible by L-
glutamine-antisense gene pair arrangement with L-
glutamine-inducible heat-shock 70-like protein
gene. J. Biol. Chem. 1994, 269, 4523-4531.
[29] Carter, C.W., Duax, W.L. Did tRNA synthetase
classes arise on opposite strands of the same gene?
Mol. Cell 2002, 10, 705-708.
http://dx.doi.org/10.1016/S1097-2765(02)00688-3
[30] Rodin, A.S.; Rodin, S.N. Translation of both
complementary strands might govern early
evolution of the genetic code. In Silico Biol. 2007,
7, 309-318.
[31] Rodin, S.N.; Rodin, A.S. On the origin of the
genetic code: signatures of its primordial
complementarity in tRNAs and aminoacyl-tRNA
synthetases. Heredity 2008, 100, 341-355.
http://dx.doi.org/10.1038/sj.hdy.6801086
[32] Rodin, A.S.; Rodin, S.N.; Carter, C.W. On
primordial sense-antisense coding. J. Mol. Evol.
2009, 69, 555-567.http://dx.doi.org/10.1007/s00239-009-9288-4
[33] Rodin, A.S.; Szathmary, E.; Rodin, S.N. One
ancestor for two codes viewed from the perspective
of two complementary modes of tRNA
aminoacylation. Biol. Direct 2009, 4, 4.
http://dx.doi.org/10.1186/1745-6150-4-4
[34] Williams, T.A.; Wolfe, K.H.; Fares, M.A. No
Rosetta Stone for a sense–antisense origin of
aminoacyl tRNA synthetase classes. Mol. Biol.
Evol. 2009, 26, 445-450.
http://dx.doi.org/10.1093/molbev/msn267
[35] Trifonov, E.N. Tracing life back to elements.
Physics Life Rev. 2008, 5, 121-132.
http://dx.doi.org/10.1016/j.plrev.2008.03.001
[36] Delmotte, F. ; Rispe, C. ; Schaber, J. ; Silva, F.J. ;
Moya, A. Tempo and mode of early gene loss in
endosymbiotic bacteria from insects. BMC Evol.
Biol. 2006, 6, 56. http://dx.doi.org/10.1186/1471-2148-6-56
[37] Marais, G.A.B. ; Calteau, A. ; Tenaillon,
O.Mutation rate and genome reduction in
endosymbiotic and free-living bacteria. Genetica
2008, 134, 205-210. http://dx.doi.org/10.1007/s10709-007-
9226-6
[38] McCutcheon, J.P.; McDonald, B.R.; Moran,
N.A. Origin of an alternative genetic code in the
extremely small and gc-rich genome of a bacterial
symbiont. PLOS Genetics 2009, 5, e1000565.
http://dx.doi.org/10.1371/journal.pgen.1000565
[39] McCutcheon, J.P. The bacterial essence of tiny
symbiont genomes. Curr. Op. Microbiol. 2010, 13,
73-78. http://dx.doi.org/10.1016/j.mib.2009.12.002
[40] Nagel, G.M.; Doolittle, R.F. Phylogenetic
analysis of the aminoacyl-tRNA synthetases. J.
Mol. Evol. 1995, 40, 487-498.
http://dx.doi.org/10.1007/BF00166617
[41] Pelchat, M.; Lapointe, J. Aminoacyl-tRNA
synthetase genes of Bacillus subtilis: organization
and regulation. Biochem. & Cell Biol.-Biochimie et
Biol. Cell. 1999, 77, 343-347. http://dx.doi.org/10.1139/bcb-
77-4-343
[42] Kaguni, L.S. DNA polymerase gamma, the
mitochondrial replicase. Annu. Rev. Biochem. 2004,
73, 293–320.
http://dx.doi.org/10.1146/annurev.biochem.72.121801.161455
[43] Wolf, Y.I.; Koonin, E.V. Origin of an animal
mitochondrial DNA polymerase subunit via
lineage-specific acquisition of a glycyl-tRNA
synthetase from bacteria of the Thermus-
Deinococcus group. Trends in Genetics 2001, 17,
431-433. http://dx.doi.org/10.1016/S0168-9525(01)02370-8
[44] Seligmann, H. Mitochondrial tRNAs as light
strand replication origins: Similarity between
anticodon loops and the loop of the light strand
replication origin predicts initiation of DNA
replication. Biosystems 2010a, 99, 85-93.
http://dx.doi.org/10.1016/j.biosystems.2009.09.003
[45] Schimmel, P., De Pouplana, L.R.Footprints of
aminoacyl-tRNA synthetases are everywhere.
Trends Biochem. Sci. 2000, 25, 207-209.
http://dx.doi.org/10.1016/S0968-0004(00)01553-X
[46] Perneger, T.V. What is wrong with Bonferroni
adjustments. Brit. Med. J. 1998, 136, 1236-1238.
http://dx.doi.org/10.1136/bmj.316.7139.1236
[47] Benjamini, Y.; Hochberg, Y. Controlling the
false discovery rate: a practical and powerful
approach to multiple testing. J. Roy. Stat. Soc. B
1995, 57, 289-300.
[48] Chanda, I.; Pan, A.; Dutta, C. Proteome
composition in Plasmodium falciparum: Higher
usage of GC-rich nonsynonymous codons in highly
expressed genes. J.Mol. Evol. 2005, 61, 513-523.
http://dx.doi.org/10.1007/s00239-005-0023-5c
[49] Barton, M.D.; Delneri, D.; Oliver, S.G. ; Rattray,
M.; Bergman, C.M. Evolutionary systems biology
of amino acid biosynthetic cost in yeast. Plos One
2010, 5, e11935. http://dx.doi.org/10.1371/journal.pone.0011935
[50] O‟Donoghue, P.; Luthey-Schulten, Z. On the
evolution of structure in aminoacyl-tRNA
synthetases. Microbiol. Mol. Biol. Rev. 2003, 67,
550-573. http://dx.doi.org/10.1128/MMBR.67.4.550-573.2003
[51] Martinis, S.A.; Boniecki, M.T. The balance
between pre- and post-transfer editing in tRNA
synthetases. FEBS Letters 2010, 584, 455-459.
http://dx.doi.org/10.1016/j.febslet.2009.11.071
BIO © [2012], Copyright CCAAS Hervé Seligmann, BIO 2012, 2, 11-26
11
http://ccaasmag.org/BIO
[52] Baldwin, A.N.; Berg, P. Transfer ribonucleic
acid-induced hydrolysis of valyladenylate bound to
isoleucyl ribonucleic acid synthetase. J. Biol.
Chem. 1966, 241, 839-845.
[53] Englisch, S.; Englisch, U.; von der Haar, F.;
Cramer, F. The proofreading of hydroxyl analogues
of leucine and isoleucine by leucyl-tRNA
synthetases from E. coli and yeast. Nuc. Acids Res.
1986, 14, 7529-7538. http://dx.doi.org/10.1093/nar/14.19.7529
[54] Fersht, A.E.; Dingwall, C. An editing mechanism
for the methionyl-tRNA synthetase in the selection
of amino acids in protein synthesis. Biochemistry
1976, 18, 1250-1256.
[55] Fersht, A.E.; Kaethner, M.M. Enzyme
hyperspecificity. Rejection of threonine by the
valyl-tRNA synthetase by misacylation and
hydrolytic editing. Biochemistry 1976, 15, 3342-
3346. tp://dx.doi.org/10.1021/bi00660a026
[56] Tsui, W.-C.; Fersht, A.R. Probing the principles
of amino acid selection using the alanyl-tRNA
synthetase from Escherichia coli. Nuc. Acids Res.
1981, 9, 4627-4637. http://dx.doi.org/10.1093/nar/9.18.4627
[57] Danel, F.; Caspers, P.; Nuoffer, C.; Hartlein, M.;
Kron, M.A.; Page, M. G.P. Asparaginyl-tRNA
syntetase pre-transfer editing assay. Curr. Drug
Discov. Technol. 2011, 8, 66-75.
http://dx.doi.org/10.2174/157016311794519947
[58] Roy, H.; Ling, J.; Irnov, M.; Ibba, M. Post-
transfer editing in vitro and in vivo by the beta
subunit of phenylalanyl-tRNA synthase. EMBO J.
2003, 23, 4639-4648.
http://dx.doi.org/10.1038/sj.emboj.7600474
[59] Beuning, P.J.; Musier-Forsyth, K. Hydrolytic
editing by a class II aminoacyl-tRNA synthetase.
PNAS 2000, 97, 8916-8920.
http://dx.doi.org/10.1073/pnas.97.16.8916
[60] Dock-Bregeon, A.-C.; Sankaranarayanan, R.;
Romby, P.; Caillet, J.; Springer, M.; Rees, B.;
Francklyn, C.S.; Ehresmann, C.; Moras, D.
Transfer RNA-mediated editng in threonyl-tRNA
synthase. Cell 2000, 103, 877-884.
http://dx.doi.org/10.1016/S0092-8674(00)00191-4
[61] Fersht, A.E.; Dingwall, C. Cysteinyl-tRNA
synthetase from Escherichia coli does not need an
editing mechanism to reject serine and alanine.
High bindning energy of small groups in specific
molecular interactions. Biochemistry 1979, 18,
1245-1249. http://dx.doi.org/10.1021/bi00574a020
[62] Gruic-Sovulj, I.; Uter, N.; Bullock, T. ; Perona,
J.J. tRNA-dependent aminoacyl-adenylate
hydrolysis by a nonediting class I aminoacyl-tRNA
synthetase. J. Biol. Chem. 2005, 280, 23978-23986.
http://dx.doi.org/10.1074/jbc.M414260200
[63] Fersht, A.E.; Shindler, J.S.; Tsui, W.-C. Probing
the limits of protein-amino acid side chain
recognition with the aminoacul-tRNA synthetases.
Discrimination against phenylalanine by tyrosyl-
synthetases. Biochemistry 1980, 19, 5520-5524.
http://dx.doi.org/10.1021/bi00565a009
[64] Jakubowski, H. Misacylation of tRNAlys with
noncognate amino acids by lysyl-tRNA synthetase.
Biochemistry 1999, 38, 8088-8093.
http://dx.doi.org/10.1021/bi990629i
[65] Seligmann H.; Pollock, D.D. The ambush
hypothesis: hidden stop codons prevent off frame
gene reading. DNA & Cell Biol. 2004, 23, 701-705.
http://dx.doi.org/10.1089/dna.2004.23.701
[66] Seligmann, H. Cost minimization of ribosomal
frameshifts. J. Theor. Biol. 2007, 249, 162-167.
http://dx.doi.org/10.1016/j.jtbi.2007.07.007
[67] Seligmann, H. The ambush hypothesis at the
whole-organism level: Off frame, 'hidden' stops in
vertebrate mitochondrial genes increase
developmental stability. Comput. Biol. & Chem.
2010, 34, 80-85.
http://dx.doi.org/10.1016/j.compbiolchem.2010.03.001
[68] Seligmann, H. Avoidance of antisense,
antiterminator tRNA anticodons in vertebrate
mitochondria. Biosystems 2010, 101, 42-50.
http://dx.doi.org/10.1016/j.biosystems.2010.04.004
[69] Seligmann, H. Undetected antisense tRNAs in
mitochondrial genomes? Biol. Direct 2010, 16, 39.
http://dx.doi.org/10.1186/1745-6150-5-39
[70] Seligmann, H. Pathogenic mutations in antisense
mitochondrial tRNAs. J. Theor. Biol. 2010, 269,
287-296. http://dx.doi.org/10.1016/j.jtbi.2010.11.007
[71] Seligmann, H. Do anticodons of mysacylated
tRNAs preferentially mismatch codons coding for
the misloaded amino acid? BMC Molecular Biology
2010, 11, 41. http://dx.doi.org/10.1186/1471-2199-11-41
[72] Seligmann, H. Error compensation of tRNA
misacylation by codon-anticodon mismatch
prevents translational amino acid misinsertion.
Comp. Biol. Chem. 2011, 35, 81-95.
http://dx.doi.org/10.1016/j.compbiolchem.2011.03.001
[73] Seligmann, H. Coding constraints modulate
chemically spontaneous mutational replication
gradients in mitochondrial genomes. Cur.
Genomics, in press.
[74] Seligmann, H. Positive correlations between
molecular and morphological rates of evolution. J.
Theor. Biol. 2010, 264, 799-807.
http://dx.doi.org/10.1016/j.jtbi.2010.03.019