Mutation pattern in human adrenoleukodystrophy protein in terms of amino-acid pair predictability

doi:10.4236/jbise.2010.33035

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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/

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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.

Email: [email protected]

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

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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

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Number of mutation

Substituted

Substituting

 (PF - AF)

-8 -6 -4 -20246

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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