J. Biomedical Science and Engineering, 2010, 3, 262-267
doi:10.4236/jbise.2010.33035 Published Online March 2010 (http://www.SciRP.org/journal/jbise/
JBiSE
).
Published Online March 2010 in SciRes. http://www.scirp.org/journal/jbise
Mutation pattern in human adrenoleukodystrophy protein in
terms of amino-acid pair predictability
Shao-Min Yan1, Guang Wu2*
1National Engineering Research Center for Non-food Biorefinery, Guangxi Academy of Sciences, Nanning, China;
2Computational Mutation Project, DreamSciTech Consulting, Shenzhen, China; *Corresponding author.
Received 28 December 2009; revised 5 January 2010; accepted 8 January 2010.
ABSTRACT
The mutation pattern in protein is a very important
feature and is studied through various approaches
including the study on mutation pattern in domains
where amino acids are converted into numbers from
letters. In this study, we converted the amino acids in
human adrenoleukodystrophy protein with its 128
missense mutations into random domain using the
amino-acid pair predictability, and then we studied
their mutation patterns. The results show 1) the mu-
tations are more likely to target the amino-acid pairs
whose actual frequency is larger than their predicted
one, 2) the mutations are more likely to form the
amino-acid pairs whose actual frequency is smaller
than their predicted frequency, 3) mutations are
more likely to occur at unpredictable amino-acid
pairs, and 4) mutations have the trend to narrow the
difference between predicted and actual frequencies
of amino-acid pairs.
Keywords: Adrenoleukodystrophy;
Amino-Acid Pair Predictability; Mutation Pattern
1. INTRODUCTION
The public-accessible protein databases provide us not
only the possibility of tracking the history of protein
family as well as other important topics, but also the
possibility of analyzing the protein evolution from vari-
ous angles. Of many facts that influence the protein
evolution is the mutation, which has been the objective
of many studies.
One way to study the mutation is to find out its pattern,
for example, the “hotspot” sites in a protein have been
defined as to be sensitive to endogenous and exogenous
mutagens [1,2,3]. This approach and others such as
multi-sequence comparison and alignment work in the
domain of amino-acid sequence, that is, we directly
analyze the mutation patterns in terms of the letters,
which represent amino acids in a protein.
Another approach, which is extremely powerful and
widely used in other research fields, is to analyze the
issue of interest in numeric domains, which also paves
the ways to use more sophisticated mathematical tools to
analyze the mutation patterns. For example, we can use
the physicochemical property of amino acids to repre-
sent a protein sequence, and analyze the protein se-
quence in physicochemical domain as a numeric se-
quence [4].
However, we should not stop here because the phys-
icochemical property and other parameters borrowed
from physics and chemistry were developed for the pur-
pose of other types of studies, but may not for the pur-
pose of studying mutations. Our group has developed
three approaches since 1999 to study protein mutation in
a random domain [5,6,7,8] not only because pure chance
is now considered to lie at the very heart of nature [9]
but also because our approaches are more sensitive to
the protein length, amino-acid composition and position,
neighboring amino acids etc. In particular, our ap-
proaches are sensitive to mutations because they can
give different values before and after mutation [5,6,7,8].
In this study, we will analyze the mutation pattern in
the adrenoleukodystrophy protein (ALDP), which is a
transporter in the peroxisome membrane and belongs to
the ATP-binding cassette transporter superfamily [10,11,
12,13]. This protein is involved in the transport of coen-
zyme A esters of very-long-chain fatty acids from the
cytoplasm into the peroxisomal lumen [14,15]. Muta-
tions in the gene ABCD1 mapping to Xq28 can result in
the defects of adrenoleukodystrophy protein that are the
cause of a severe X-linked disease, called X-linked
adrenoleukodystrophy [16,17]. This disease is the most
common peroxisomal disorder [18] with a minimal inci-
dence of 1:21000 males [19]. It is characterized by the
characteristic accumulation of saturated very long-chain
fatty acids and disorder of peroxisomal beta-oxidation
[20,21,22,23,24]. The clinical outcomes of adrenoleu-

S. M. Yan et al. / J. Biomedical Science and Engineering 3 (2010) 262-267
263
JBiSE
kodystrophy vary strikingly and unpredictably [13,14,15,
16,17,18,19,20,21,22,23,24,25,26]. The phenotypes in-
clude the rapidly progressive childhood cerebral form
(CCALD), the milder adult form, adrenomyeloneuropa-
thy (AMN), and variants without neurologic involve-
ment. There is no apparent correlation between genotype
and phenotype [24,25,26,27].
Besides the importance mentioned above, there are
currently 132 mutations documented in human adreno-
leukodystrophy protein, which is statistically sufficient
for pattern analysis.
2. MATERIALS AND METHODS
2.1. Data
The amino-acid sequence of human adrenoleukodystro-
phy protein and its 132 mutations were obtained from
the UniProtKB/Swiss-Prot (accession number P33897).
Among the mutations, 128 are missense point mutations
and the rest are small deletions or insertions [28].
2.2. Conversion of Adrenoleukodystrophy
Protein into Random Domain
We use the amino-acid pair predictability as a measure-
ment for the randomness of adjacent amino-acid pairs in
a protein, and this simple measure can serve as an indi-
cator to the complicity of protein construction, which
leads to many our studies [29,30,31,32,33,34,35,36,37,
38,39].
The human adrenoleukodystrophy protein consists of
745 amino acids. The first and second amino acids are
an adjacent amino-acid pair, the second and third as an-
other amino-acid pair, the third and fourth, until the 744th
and 745th, thus there are 744 adjacent amino-acid pairs.
Then, we can use the permutation to define if an
amino-acid pair is predictable. For example, there are 80
alanines (A) and 57 valines (V) in human adrenoleu-
kodystrophy protein: if the permutation works, then the
amino-acid pair AV would appear 6 times (80/745 
57/744  744 = 6.12); actually we do find six AV pairs in
this protein, so the appearance of AV is predictable, be-
cause the actual frequency is equal to its predicted one.
On the other hand, there are 59 arginines (R) in hu-
man adrenoleukodystrophy protein, if the permutation
works, the predicted frequency of AR appearance would
be 6 (80/745  59/744  744 = 6.34); however, the pair
AR appears 9 times in realty, so the appearance of AR is
unpredictable, because the actual frequency is larger
than its predicted one.
Also, we can find the case, where the actual frequency
is smaller than its predicted one. For example, there are
94 leucines (L) and 55 glutamic acids (G) in human
adrenoleukodystrophy protein so that the predicted fre-
quency of pair LG is 7 (94/745  55/744  744 = 6.94),
however, its actual frequency is only 2.
2.3. Mutations in Terms of Predictable and
Unpredictable Amino-Acid Pairs
A point missense mutation would lead to the change in
two amino-acid pairs if the mutation would not occur at
terminals. For instance, a mutation at position 484 sub-
stitutes proline (P) to arginine (R), which impairs the
protein dimerization [40]. This mutation leads amino-
acid pairs TP and PS to change to TR and RS, because
threonine (T) is located at position 483 and serine (S) is
located at position 485. Nevertheless, this mutation
would be reflected in terms of predicted frequency, ac-
tual frequency and their difference (see Table 1).
This mutation changed the difference between pre-
dicted frequency (PF) and actual frequency (AF) of af-
fected amino-acid pairs, (PFAF). Before mutation,
(PF  AF) = (1  1) + (2  4) = 2 for TP and PS, and
(PF  AF) = (2  0)+( 4  4) = 2 for TR and RS. After
mutation, (PF  AF) = (1  0) + (2  3) = 0 for TP and
PS, and (PF  AF) = (2  1) + (4  5) = 0 for TR and
RS. Needless to say, there would construct a certain mu-
tation pattern if we analyze sufficient mutations.
2.4. Statistics
The data were presented as median with an interquartile.
The Mann-Whitney U-test and Chi-square test were used
for comparisons, and P < 0.05 is considered statistically
significant.
3. RESULTS
As there are 20 kinds of amino acids, they can theoreti-
cally construct 400 types of amino-acid pairs, which serve
us as a reference for comparison with human adrenoleu-
kodystrophy protein. Of 400 types of amino-acid pairs,
118 are absent including 44 predictable and 74 unpredict-
able. Consequently 744 amino-acid pairs in human adre-
noleukodystrophy protein include only 282 types of
amino-acid pairs (400 – 118 = 282), which means these
282 types would host all mutations occurred in the protein.
Of those 282 types, 98 are predictable and 184 are unpre-
dictable; while of 744 amino-acid pairs 175 and 569 pairs
are predictable and unpredictable because some types of
amino-acid pairs appear more than once.
The first mutation pattern is the amino-acid pairs tar-
Table 1. Predicted frequency, actual frequency and their dif-
ference of amino-acid pairs affected by T485S mutant of hu-
man adrenoleukodystrophy protein.( PF: Predicted frequency, AF:
actual frequency; T: threonine, P: praline, R: arginine, S: serine.).
Amino-aci
d pair Before mutation After mutation
PF AF PF–AF PF AF PF–AF
TP 1 1 0 1 0 1
PS 2 4 –2 2 3 –1
TR 2 0 2 2 1 1
RS 4 4 0 4 5 –1

264 S. M. Yan et al. / J. Biomedical Science and Engineering 3 (2010) 262-267
geted by mutations, or we might call them as hotspots.
Table 2 details the substituted amino-acid pairs in terms
of the relationship between actual and predicted fre-
quencies. Here, the mutations are more likely to target
the amino-acid pairs whose actual frequency is larger
than their predicted one, for example, 47 mutations tar-
geted these amino-acid pairs; by contrast, the mutations
are less likely to target the amino-acid pairs whose ac-
tual frequency is smaller than their predicted one, for
example, only 5 mutations targeted these amino-acid
pairs. The Chi-square test indicates remarkable statistical
difference before and after mutation (P=<0.001).
The second mutation pattern is the amino-acid pairs
formed after mutations. Table 3 details the substituting
amino-acid pairs in terms of the relationship between
actual and predicted frequencies. The Chi-square test
indicates remarkable statistical difference before and
after mutation (P=<0.001). Table 3 has the same format
as those in Table 2, thus we can easily find out the sec-
ond mutation pattern by comparing two tables. For ex-
ample, the data in the fourth column in both tables are
almost in totally opposite orders, that is, mutations are
more likely to target the amino-acid pairs whose actual
frequency is larger than their predicted frequency, while
mutations are more likely to form the amino-acid pairs
whose actual frequency is smaller than their predicted
frequency. Again, the data in the seventh column in both
tables appear somewhat similar.
Meanwhile, Tables 2 and 3 also reveal the third muta-
tion pattern that is mutations are more likely to occur at
unpredictable amino-acid pairs (lines 4–8 in both tables).
The above three mutation patterns are mainly related
to the relationship in amino-acid pair between actual and
predicted frequencies. In fact, the difference between
predicted and actual frequencies can also provide us with
further mutation patterns. Figure 1 shows the difference
between predicted and actual frequencies related to
amino-acid pairs identical to substituted and substituting
amino-acid pairs before and after mutation, and their
statistical results are shown in Figure 2, which high-
lights the fourth mutation pattern.
Before mutation, the median of difference between
predicted and actual frequencies is –2 in substituted
amino-acid pairs, suggesting that the mutations occur in
the amino-acid pairs, which appear more than their pre-
dicted frequency. Meanwhile, the corresponding value is
1 in substituting amino-acid pairs, indicating that the
mutations lead to the appearance of the amino-acid pairs
that appear less than their predicted frequency.
After mutation, the median of difference between pre-
dicted and actual frequencies is 0 in substituted amino-
acid pairs, suggesting that these amino-acid pairs are
Table 2. Amino-acid pairs identical to substituted amino-acid pairs before and after mutations. (AF: actual frequency; PF:
predicted frequency. There is a remarkable statistical difference before and after mutation (Chi-square = 47.841 with 5 de-
grees of freedom, P = <0.001).
Amino-acid pairs Before Mutation After Mutation
Pair I Pair II Number % Total % Number % Total %
Predictable AF=PF AF=PF 21 16.41 16.41 14 10.94 10.94
Unpredictable AF>PF AF>PF 47 36.72 83.59 14 10.94 89.06
AF>PF AF=PF 29 22.66 20 15.63
AF>PF AF<PF 20 15.63 30 23.44
AF<PF AF=PF 6 4.69 27 21.09
AF<PF AF<PF 5 3.91 23 17.97
Table 3. Amino-acid pairs identical to substituting amino-acid pairs before and after mutations (AF: actual frequency; PF:
predicted frequency. There is a remarkable statistical difference before and after mutation (Chi-square = 54.114 with 5 de-
grees of freedom, P = <0.001).
Amino-acid pairs Before Mutation After Mutation
Pair I Pair II Number % Total % Number % Total %
Predictable AF=PF AF=PF 11 8.59 8.59 9 7.03 7.03
Unpredictable AF>PF AF>PF 6 4.69 91.41 39 30.47 92.97
AF>PF AF=PF 24 18.75 40 31.25
AF>PF AF<PF 29 22.66 25 19.53
AF<PF AF=PF 34 26.56 8 6.25
AF<PF AF<PF 24 18.75 7 5.47

S. M. Yan et al. / J. Biomedical Science and Engineering 3 (2010) 262-267
265
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Number of mutation
0
5
10
15
20
25
30
35
Substituted
Substituting
 (PF - AF)
-8 -6 -4 -20246
0
5
10
15
20
25
30 After mutation
Before mutation
Figure 1. Difference between predicted frequency (PF) and actual
frequency (AF) related to amino-acid pairs identical to substituted
and substituting amino-acid pairs before and after mutation.
(PF-AF)
-5 -4 -3 -2 -10123
Group
Substituted pairs before mutation
Substituting pairs before mutation
Substituting pairs after mutation
Substituted pairs after mutation
Figure 2. Sum of difference between predicted and actual fre-
quencies [(PF  AF)] of substituted and substituting amino-
acid pairs before and after mutation in human adrenoleukodystro-
phy protein. The data are presented by median with an interquatile
interval. There is a statistically significant difference between cor-
responding groups (P < 0.001, Mann-Whitney U-test).
more randomly constructed in the mutant adrenoleu-
kodystrophy proteins, as their predicted and actual fre-
quencies are about the same. However, the correspond-
ing value is –1 in substituting amino-acid pairs, indicat-
ing that the mutations are favor the amino-acid pairs that
already exist in the protein. Striking statistical difference
is found between the corresponding groups (P < 0.001).
4. DISCUSSION
In this study, we analyze the mutation patterns in nu-
merical domain rather than word descriptions because it
is far much easy to find repeatable patterns in numerical
domain. Actually there are many ways to analyze the
mutation patterns in numerical domain, for example, we
can use the physicochemical property to replace amino
acids in a protein, and then we can analyze the mutation
patterns in physicochemical property domain, which
would be an interesting topic for pursuit.
The numerical domain in our approach is random
whose rationale has been given in the Introduction, its
biological implications would include the followings: 1)
Nature follows parsimony, which suggests to construct a
protein with minimal time and energy, thus the predict-
able amino-acid pairs in our approach confirm the nature
parsimony, while the unpredictable amino-acid pairs
suggest that nature deliberately spends more time and
energy to construct them, which could be functional sites.
2) The difference between predicted and actual frequen-
cies in amino-acid pairs can be regarded as a force driv-
ing mutation. Although there are uncountable factors
driving mutations, their effect in fact is the difference
between predicted and actual frequencies. 3) The basic
effect of mutation in our sense is to narrow the differ-
ence between predicted and actual frequencies in tar-
geted amino-acid pairs: most mutations do have such
effects. However, the new formed amino-acid pairs
could create new unpredictable amino-acid pairs, which
once again have the difference between predicted and
actual frequencies leading to new mutation so the evolu-
tion can continue.
As the difference between predicted and actual fre-
quencies is a measure of random construction of amino-
acid pairs in a protein, thus the smaller the difference is,
the more random the construction of amino-acid pairs is.
In particular, a) the larger the positive difference is, the
more randomly unpredictable amino-acid pairs are ab-
sent; and b) the larger the negative difference is, the more
randomly unpredictable amino-acid pairs are present.
Therefore, this study highlights the mutation patterns
in terms of amino-acid pair predictability in human
adrenoleukodystrophy protein. In future, we hope to
incorporate these mutation patterns in random domain
into the changes in the secondary structure contents and
consequently affect biological functions of the protein
[41,42].
5. ACKNOWLEDGEMENTS
This study was partly supported by National Basic Research Program
of China (2009CB724703), Guangxi Science Foundation (0991080)

266 S. M. Yan et al. / J. Biomedical Science and Engineering 3 (2010) 262-267
and Guangxi Academy of Sciences (09YJ17SW07).
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