The close relationship between the biosynthetic families of amino acids and the organisation of the genetic code.

Abstract

By generating random codes and applying Fisher's exact test, we confirm that the biosynthetic families of amino acids are intimately involved in the organisation of the genetic code. This observation corroborates the coevolution theory of genetic code origin. As the amino acids belonging to the single biosynthetic families have codons that are contiguous in the genetic code, they must have entered the code itself by means of a clustering mechanism, which must clearly have been compatible with the mechanism on which this theory is based because this too envisages the clustering of biosynthetically correlated amino acids within the code.

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The close relationship between the biosynthetic families of amino acids and the
organisation of the genetic code
Massimo Di Giulioa,⁎, Umberto Amatob
aLaboratory for Molecular Evolution, Institute of Genetics and Biophysics ‘Adriano Buzzati Traverso’, CNR, Via P. Castellino, 111, 80131 Naples, Napoli, Italy
bIstituto per le Applicazioni del Calcolo ‘Mauro Picone’, CNR, Sede di Napoli, Via Pietro Castellino 111, I-80131 Naples, Napoli, Italy
a b s t r a c ta r t i c l ei n f o
Article history:
Received 20 October 2008
Received in revised form 18 December 2008
Accepted 23 December 2008
Available online 14 January 2009
Keywords:
Biosynthetic relationships between amino
acids
Precursor–product amino acid pairs
Ancestral stages of the code
Generation of random codes
By generating random codes and applying Fisher's exact test, we confirm that the biosynthetic families of
amino acids are intimately involved in the organisation of the genetic code. This observation corroborates the
coevolution theory of genetic code origin. As the amino acids belonging to the single biosynthetic families
have codons that are contiguous in the genetic code, they must have entered the code itself by means of a
clustering mechanism, which must clearly have been compatible with the mechanism on which this theory is
based because this too envisages the clustering of biosynthetically correlated amino acids within the code.
© 2009 Elsevier B.V. All rights reserved.
1. Introduction
Immediately after the genetic code was deciphered, numerous
authors maintained that its organisation seemed to reflect the
biosynthetic relationships between amino acids (Nirenberg et al.,
1963; Pelc, 1965). This point of view reached its climax with the
formulation of the coevolution theory of genetic code origin in 1975,
which suggests that the genetic code is an imprint of the biosynthetic
relationships between amino acids (Wong, 1975). That the biosyn-
thetic relationships between amino acids might have been able to
condition the origin of the genetic code was convincingly maintained
by Taylor and Coates (1989) and other authors (Dillon, 1973; Miseta,
1989; Di Giulio, 1996; Di Giulio and Medugno, 2000) and, more
recently, by Davis (2007, 2008). Di Giulio (2001) replied to the
criticisms that were voiced against the ‘biosynthetic origin’ of the
genetic code. Indeed, some authors have cast doubt on the
authenticity of the correlation between the biosynthetic relationships
between amino acids and the organisation of the genetic code
(Amirnovin, 1997; Ronneberg et al., 2000) and, in particular, on
whether or not the relationships between amino acid pairs in a
precursor–product relationship are significantly reflected in the
genetic code (Amirnovin, 1997; Ronneberg et al., 2000).
More recently, Rob Knight has expressed doubts on the signifi-
cance of this relationship between the biosynthetic pathways
connecting the amino acids and the organisation of the genetic code
(Di Giulio, 2008). Given the importance that this relationship has in
the process of falsification/corroboration of the theories proposed to
explain genetic code origin, we have decided to check, once again, the
truth of the relationship: biosynthetic pathways of amino acids–
genetic code organisation.
2. Materials and methods
Let the genetic code and the set of amino acids Ai, i=1,…, 20 be
assigned so that each amino acid occupies a specific position within
the code. Let T be the set of pairs of these positions within the genetic
code so that exchanges between the corresponding amino acids are
possible; and let L be the set of the relative number of possible
exchanges, Lj, on the basis of the genetic code structure (only single
base changes are considered). Finally, let F be the set of amino acid
pairs in a biosynthetic relationship, obtained considering all the
possible pairs of amino acids within the biosynthetic families (Table
1). The sum of the Lj's for a particular code constitutes the Codon
Correlation Score (CCS). Let P=(P1,…, P20) be any one permutation of
the genetic code so that the positions of the synonymous codon blocks
are occupied by the amino acids A(Pi), i=1,…, 20. We assume that
histidine always occupies the same position in the genetic code
because it is metabolically isolated (Taylor and Coates,1989). The CCS
relative to a permutated genetic code is calculated as the sum of
possible exchanges Lj for all the pairs of amino acids occupying
positions in the code so that changes are possible (i.e. belonging to T)
and in a biosynthetic relationship (i.e. belonging to F). In addition to
the CCS value, the number of possible biosynthetic relationships with
the permutation P (CCS1) is also considered.
Gene 435 (2009) 9–12
Abbreviations: CCS, Codon Correlation Score.
⁎ Corresponding author. Fax: +39 081 6132706.
E-mail address: digiulio@igb.cnr.it (M. Di Giulio).
0378-1119/$ – see front matter © 2009 Elsevier B.V. All rights reserved.
doi:10.1016/j.gene.2008.12.018
Contents lists available at ScienceDirect
Gene
journal homepage: www.elsevier.com/locate/gene

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Generating a high number of permutations within the genetic
code, we obtain an accurate estimate of the probability density of CCS,
CCS1and their joint under the hypothesis that the code evolved at
random.
In the same way, we have calculated the probability density of CCS,
CCS1and their joint, considering the set F′ of amino acid pairs for
which there is a precursor–product relationship between amino acids
(that is to say, the sum of the possible changes Ljfor all the amino acid
pairs belonging to F′ and occupying positions belonging to T).
The simulation was performed by generating 600 million random
permutations of the genetic code. The simulation software was
written in Matlab environment and is available on the web page
http://www.na.iac.cnr.it/programmi/randomgeco/randomgeco.htm.
3. Results and discussion
3.1. Estimate of the probability with which the biosynthetic families of
amino acids are distributed in the genetic code, by means of simulation
We generated random codes while maintaining invariant the
allocations of synonymous codon blocks of amino acids, as in the
genetic code, and permuting the 20 amino acids (amino acid
permutation codes) (Di Giulio, 1989a, b). For each of these codes, we
calculated the Codon Correlation Score (CCS) (Amirnovin, 1997; Di
Giulio and Medugno, 2000), i.e. for each pair ij of amino acids we
calculated the number of times that the amino acid i transforms into
the amino acid j on the basis of the genetic code structure and only
considering single base changes (Di Giulio, 1989a; Di Giulio and
Medugno, 2000; Archetti, 2004). The sum of all these numbers for all
the considered pairs of amino acids in a biosynthetic relationship is
the CCS. For instance, for the five biosynthetic families of amino acids
defined on the basis of an amino acid precursor or a non amino acid
precursor (Table 1) and for the genetic code, we obtain a CCS value of
55 units (Table 1).
It clearly emerges that if the biosynthetic relationships between
amino acids had a true significance for the origin of the genetic code,
then all the possible combinations of amino acid pairs within a
biosynthetic family should be statistically significant in that these
combinations of amino acid pairs would be made between amino
acids that, by definition, should possess codons that are contiguous in
the genetic code (Di Giulio and Medugno, 2000).
Therefore, if the biosynthetic relationships between amino acids
wereimportantin establishinggeneticcodeorigin,thenthis CCSvalue
of 55 units associated to the genetic code should be obtained with a
low probability from the set of amino acid permutation codes. As
shown in Table 2, this probability of obtaining a CCS value that is
greater than or equal to 55 is 0.0050 and, therefore, statistically
significant.
The CCS defines only one aspect of a code's capability to reflect the
biosynthetic relationships between amino acids; a more important
aspect is given by the number of amino acid pairs in a biosynthetic
relationship that intervene to define a given CCS (Di Giulio and
Medugno, 2000). This number of amino acid pairs in a biosynthetic
relationship, for the five amino acid biosynthetic families defined by
the genetic code, is 23 (Table 1). We can therefore ask ourselves with
what probability this number of amino acid pairs in a biosynthetic
relationship is obtained from the set of amino acid permutation codes.
As shown in Table 3, this probability is only 6.8×10−5.
The CCS and the number of amino acids in a biosynthetic
relationship, defined by the genetic code considering the five
biosynthetic families of amino acids, need to be joined in order to
calculate the real probability of extracting the genetic code from the
set of amino acid permutation codes (Di Giulio and Medugno, 2000).
This is because, considering only (i) the CCS, we only count codes that
have a CCS value greater than or equal to 55 units, but it can happen
that they have less than 23 amino acid pairs in a biosynthetic
relationship; and (ii) the number of amino acids in a biosynthetic
relationship, we can count codes having a CCS value of less than
55 units. Therefore, the joint of these two variables should provide the
real estimate of randomly extracting the genetic code from the set of
amino acid permutation codes. This probability is seen to be 5.8×10−5.
3.2. Estimate of the probability with which the biosynthetic families of
amino acids are distributed in the genetic code, using Fisher's exact test
Fisher's exact test seems to be suitable for estimating the statistical
significance of the distribution of the biosynthetic families of amino
acids within the genetic code (Di Giulio, 2008). As suggested by Rob
Knight (Di Giulio, 2008), Fisher's exact test can estimate this
probability because if we consider the Asp family, for instance, we
observe from the genetic code that: (i) 5 amino acids (Ile, Met, Thr, Asn
and Lys) of the Asp biosynthetic family are codified by ANN type
codons (=a), while (ii) only 2 amino acids (Ser and Arg) not belonging
to this family are codified by codons of the same ANN type (=b).
Furthermore, (i) only 1 amino acid (Asp) belonging tothe Asp family is
codified by non-ANN codons (=c) whereas (ii) 12 amino acids (Phe,
Tyr, Cys, Trp, Leu, Pro, His, Gln, Val, Ala, Gly and Glu) are codified by
non-ANN codons (=d). If we use these parameter values (a=5, b=2,
c=1, d=12), Fisher's exact test yields a probability of 0.0072 that the
distribution of the amino acids in the Asp biosynthetic family has been
generated in the genetic code only by chance. Hence, a highly
significant probability. Table 4 reports the estimate of the probabilities
for the 5 biosynthetic families (Table 1). Moreover, the calculation of
the quantity −2lnP (Table 4) makes it possible to estimate the
probability of the aggregate (Fisher, 1950). Indeed, by summing all
the −2lnP quantities provides a value χ2=34.81 (df=10) and a
P=1.3×10−4. It therefore emerges that the codons attributed by the
genetic code to the amino acids in the 5 biosynthetic families are not
random but reflect these families in that their current distribution
within the genetic code would be obtained by chance alone with a
very low probability.
3.3. The statistical non-significance in the genetic code of a particular set
of amino acid pairs in a biosynthetic precursor–product relationship and
its interpretation
These observations clearly point out that the biosynthetic relation-
ships between amino acids were important in genetic code origin.
However, the coevolution theory of genetic code origin (Wong, 1975)
is based on the notion of amino acids in a precursor–product
relationship, and this has been criticised (Amirnovin,1997; Ronneberg
et al., 2000). We have already analysed this controversial point
elsewhere (Di Giulio,1999, 2001; Di Giulio and Medugno, 2000). Here
Table 1
All the combinations of amino acid pairs relative to the five biosynthetic amino acid
families (Di Giulio and Medugno, 2000)
Serine family:
Ser-Gly=2, Ser-Cys=4, Ser-Trp=1, Gly-Cys=2, Gly-Trp=1, Cys-Trp=2
Phosphoenolpyruvate family:
Phe-Tyr=2
Pyruvate family:
Ala-Val=4, Ala-Leu=0, Val-Leu=6
Aspartate family:
Asp-Asn=2, Asp-Thr=0, Asp-Ile=0, Asp-Met=0, Asp-Lys=0, Asn-Thr=2, Asn-Ile=2,
Asn-Met=0, Asn-Lys=4, Thr-Ile=3, Thr-Met=1, Thr-Lys=2, Ile-Met=3, Ile-Lys=1,
Met-Lys=1
Glutamate family:
Glu-Gln=2, Glu-Arg=0, Glu-Pro=0, Gln-Arg=2, Gln-Pro=2, Arg-Pro=4
His does not fall into this classification because it is metabolically isolated (Taylor and
Coates, 1989). The numbers indicate the times that the pairs' amino acids are
interchangeable on the basis of the genetic code structure.
10
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we aim only to discuss a particular set of amino acid pairs in a
precursor–product relationship, for which the coevolution theory
does not appear to be statistically significant. This set (Table 5) is
obtained from the biosynthetic relationships, as shown in Fig. 1 of
Taylor and Coates (1989), considering only the amino acids that are in
a certain and undoubted precursor–product amino acid relationship.
For this set (Table 5), which has a value CCS=14, we obtain P=0.34
that this or greater values are obtained by chance from the set of
amino acid permutation codes. Likewise, for the variable number of
amino acid pairs in a precursor–product relationship, we observe in
the genetic code that, out of 11 expected pairs, only 6 can be seen to be
in a precursor–product relationship (Table 5), which obtains a P=0.23.
Finally, the combination of CCS with the number of pairs in a
precursor–product relationship provides P=0.20. All these probabil-
ities are non-significant and, strictly speaking, would falsify the
coevolution theory because these 6 pairs in a precursor–product
Table 2
CCS value and relative probability obtained by generating 600 million random codes
CCSProbability
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
1
1.000000e+000
1.000000e+000
9.999999e−001
9.999995e−001
9.999981e−001
9.999938e−001
9.999814e−001
9.999522e−001
9.998814e−001
9.997390e−001
9.994425e−001
9.989188e−001
9.979562e−001
9.964247e−001
9.938586e−001
9.901341e−001
9.843973e−001
9.767309e−001
9.657325e−001
9.520826e−001
9.336629e−001
9.122552e−001
8.848599e−001
8.548269e−001
8.180483e−001
7.797487e−001
7.347016e−001
6.898858e−001
6.389989e−001
5.904153e−001
5.369594e−001
4.877780e−001
4.352306e−001
3.884948e−001
3.398588e−001
2.979857e−001
2.554859e−001
2.200197e−001
1.848242e−001
1.563660e−001
1.286806e−001
1.069762e−001
8.623422e−002
7.048268e−002
5.565436e−002
4.474116e−002
3.460283e−002
2.737667e−002
2.072807e−002
1.614853e−002
1.196053e−002
9.171537e−003
6.640698e−003
5.015845e−003
3.543572e−003
2.635513e−003
1.813780e−003
1.330300e−003
8.924083e−004
6.463233e−004
4.224900e−004
3.024033e−004
1.931633e−004
1.373200e−004
8.547833e−005
6.035500e−005
3.674333e−005
2.601000e−005
1.556000e−005
1.086500e−005
6.386667e−006
4.441667e−006
2.505000e−006
1.711667e−006
8.933333e−007
Table 2 (continued)
CCSProbability
6.133333e−007
3.066667e−007
2.433333e−007
1.150000e−007
8.666667e−008
3.833333e−008
2.666667e−008
1.500000e−008
1.500000e−008
3.333333e−009
3.333333e−009
0
76
77
78
79
80
81
82
83
84
85
86
87
In order to read the probability value greater than or equal to a specific CCS value, it is
necessary to take the probability value relative to CCS−1. Data in bold indicate the
probability of obtaining a CCS value greater than or equal to 55.
Table 3
Values of the number of amino acid pairs in a biosynthetic relationship per code and
relative probability, obtained by generating 600 million random codes
Biosynthetic pair numberProbability
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
1
1.000000e+000
9.999994e−001
9.999902e−001
9.998884e−001
9.991374e−001
9.953792e−001
9.820917e−001
9.473310e−001
8.772985e−001
7.651148e−001
6.182170e−001
4.575374e−001
3.079486e−001
1.875907e−001
1.030945e−001
5.095104e−002
2.259470e−002
8.968927e−003
3.175425e−003
1.002875e−003
2.797617e−004
6.790500e−005
1.422500e−005
2.360000e−006
3.033333e−007
4.500000e−008
3.333333e−009
0
In ordertoreadthe probabilityvalue greater than orequal toa specific numberof amino
acid pairs in a biosynthetic relationship per code (n), it is necessary to take the
probability value relative to n−1. The probability that the biosynthetic pair number 23
is obtained is rendered in bold.
11
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relationship (Table 5) would be easily obtained in the genetic code by
chance alone. Clearly, these probabilities partly justify the criticisms of
Amirnovin (1997) and Ronnenberg et al. (2000).
The coevolution theory responds to the non-significance in the
genetic code of these pairs of amino acids in a precursor–product
relationship (Table 5) by suggesting that the pairs not observed in the
genetic code refer only to Asp and Glu because the latter were codified
late on in the genetic code, respectively by the codons AAY and CAR
which todaycodify for Asn and Gln.This postulate is such as to remove
the majority of the non-contiguities between the amino acids in a
precursor–product relationship (Wong, 1975). That this postulate is
most likely correct is suggested by the existence of the pathways Asp-
tRNAAsn→AsntRNAAsn(Curnow et al., 1996) and Glu-tRNAGln→Gln-
tRNAGln(Schon et al., 1988), which are molecular fossils of the codon
concession mechanism fromthe precursor tothe product amino acids,
as predicted by the coevolution theory (Wong,1975,1976, 2005, 2007;
Wachtershauser,1988; Danchin,1989; de Duve,1991; Di Giulio, 2002,
2008). Therefore, the fact thatthis setof precursor–product pairs gives
a non-significant probability is explained by the very behaviour of Asp
and Glu, which are also involved in the transformation taking place on
tRNAs, for the very reason that the codons AAY and CAR might have
codified for Asp and Glu in the late phase of genetic code evolution.
This would not only justify the postulate of the coevolution theory but
would also appear to be a necessity (Di Giulio, 2008). In short, the
existence of these biosynthetic pathways on the tRNAs involving Asp
and Glu and the absence of the precursor–product amino acid pairs
again involving just these two amino acids is not random. In
conclusion the coevolution theory can fully justify the statistical
non-significance of this particular set of amino acid pairs in a
precursor–product relationship, by exploiting the very existence of
biosynthetic pathways on tRNAs, which are the most characteristic
and corroborative observation regarding this theory (Wong, 1975,
1976; Wachtershauser,1988; Danchin,1989; de Duve,1991; Morowitz,
1992; Edwards, 1996; Di Giulio, 2002, 2008).
4. Conclusions
The results presented here unequivocally indicate that there exists
a closerelationshipbetweenthebiosyntheticpathwaysof aminoacids
and the organisation of the genetic code. This observation confirms
what is referred in the literature (Nirenberg et al., 1963; Pelc, 1965;
Dillon,1973; Wong 1975; Jurka and Smith,1987; Di Giulio,1996,1999,
2001; Di Giulio and Medugno, 2000; Davis, 2007, 2008). This is strong
evidence in favour of the coevolution theory of genetic code origin
because if, as pointed out here, the biosynthetic families of amino
acids are reflected in the genetic code, i.e. the amino acids of the single
biosynthetic families have codons that are contiguous in the code,
then it might have taken place only if the amino acids of the
biosynthetic families entered the code through a clustering mechan-
ism. This mechanism must clearly be compatible with the one
suggested by the coevolution theory because it too envisages
clustering of biosynthetically correlated amino acids in the code.
Therefore, although this theory is not significant in a particular set of
amino acids in a precursor–product relationship (Table 5), never-
theless it is the general relationship between the biosynthetic families
of amino acids and the genetic code which clearly designates that the
interpretation given by the coevolution theory to the absence of
several precursor–product amino acids pairs in the code is correct and
that, therefore, this theory is considerably corroborated by this.
Finally, it is obvious that all these observations are even more
compatible with the extended coevolution theory (Di Giulio, 2008).
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Table 4
This shows the results of the application of Fisher's exact test to the five biosynthetic
families of amino acids
abcdP
−2lnP
8.48
5.08
4.51
9.86
6.88
Serine family
Phosphoenolpyruvate family
Pyruvate family
Aspartate family
Glutamate family
4
2
3
5
3
4
4
7
2
2
0
0
0
1
1
12
14
10
12
14
0.0144
0.079
0.105
0.0072
0.032
See text for further information.
Table 5
A particular set of amino acid pairs which are in a certainprecursor–product amino acid
relationship
Ser-Gly=2, Ser-Cys=4, Ser-Trp=1, Asp-Asn=2, Asp-Thr=0, Asp-Met=0, Asp-Lys= 0, Thr-
Ile=3, Glu-Gln=2, Glu-Arg=0, Glu-Pro=0
The numbers indicate the times that the two amino acids interchange on the basis of
the genetic code. See text for further information.
12
M. Di Giulio, U. Amato / Gene 435 (2009) 9–12