J. Biomedical Science and Engineering, 2009, 2, 190-199
Published Online June 2009 in SciRes. http://www.scirp.org/journal/jbise
Descriptively pr obabilisti c relations hip betwee n muta ted
primary structure of von Hippel-Lindau protein and its
clinical outcome
Shao-Min Yan1, Guang Wu2*
1National Engineering Research Center for Non-food Biorefinery, Guangxi Academy of Sciences, 98 Daling Road, Nanning, Guangxi
Province, CN-530007, China; 2Computational Mutation Project, DreamSciTech Consulting, 301, Building 12, Nanyou A-zone, Jianna n
Road, Shenzhen, Guangdong Province CN-518054, China; *Corresponding author (hongguanglishibahao@yahoo.com), Tel: +86 771
2503 930, Fax: +86 755 2664 8177.
Received 5 August 2008; revised 4 January 2009; accepted 7 January 2009.
In this study, we use the cross-impact analysis
to build a descriptively probabilistic relationship
between mutant von Hippel-Lindau protein and
its clinical outcome after quantifying mutant
von Hippel-Lindau proteins with the amino-acid
distribution probability, then we use the Bayes-
ian equation to determine the probability that
the von Hippel-Lindau disease occurs under a
mutation, and finally we attempt to distinguish
the classifications of clinical outcomes as well
as the endocrine and nonendocrine neoplasia
induced by mutations of von Hippel-Lindau
protein. The results show that a patient has 9/10
chance of being von Hippel-Lindau disease
when a new mutation occurs in von Hippel-
Lindau protein, the possible distinguishing of
classifications of clinical outcomes using mod-
eling, and the explanation of the endocrine and
nonendocrine neoplasia in modeling v iew.
Keywor ds: Amino Acid; Bayes’ Law; Cross-Impact
Analysis; Distribution Probability; Mutation; Von
Hippel-Lin d au Disease
Perhaps, the first step to study the genotype-phenotype
relationship is to determine a protein in relation to a dis-
ease, and the second step would be to build a quantitative
relationship between mutant protein and its clinical out-
come. Then we ma y be in the position to predict the clini-
cal outcome based on such a quantitative relati onship, even
to predict new functions led by new mutations.
Thus, we need the methods, which can quantify a pro-
tein sequence as a numeric sequence in order to build a
quantitative relationship. In fact, we have various ways
to quantify a protein sequence, for example, to use the
physicochemical property of amino acid to quantify a
protein sequence [1].
Since 1999, we have developed three approaches to
quantify each amino acid in a protein as well as a whole
protein (for reviews, see [2,3,4]), and our quantifications
indeed differ before and after mutation, thus it is possi-
ble to use our approaches to build a quantitative rela-
tionship between changed primary structure and changed
functio n of protein.
In 1911 and 1926, von Hippel and Lindau described
the von Hippel-Lindau disease [5,6], later on Melmon
and Rosen established the notion of the von Hippel-
Lindau disease [7], which is an autosomal dominant dis-
order characterized by cerebellar, spinal cord, and retinal
hemangioblastomas; cysts of the kidney, pancreas, liver,
and epididymis; and has an increased frequency of renal
cancer (renal cell carcinoma or hypernephroma), pan-
creatic cancer, and pheochromocytoma [8,9,10]. The von
Hippel-Lindau disease has a birth incidence of about 1 in
36000 and about 20% of cases arise as de novo muta-
tions without a family history [11,12].
The von Hippel -Li ndau disease tum or supp ressor gene
was identified in 1993 [13], of which mutations are the
major cause for developing the von Hippel-Lindau dis-
ease. Pathologically relevant is inactivation of the von
Hippel-Lindau gene and subsequent loss of the function
of the von Hippel-Lindau protein, and Elongin B, C
complex [14,15]. The dysfunction of the ubiquitination
of hypoxia-inducible factors is an important step in the
development of various tumors [15,16,17,18,19]. Also, a
recent study elucidated the role of NGF/JunB/ EglN3-
related pathways in developmental apoptosis linking to
tumourigenesis [2 0].
Clinically the von Hippel-Lindau disease is classified
into two types: type I without pheochromocytoma and
type II with pheochromocytoma [10,17]. On the other
hand, more than 300 different von Hippel-Lindau muta-
S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 190-199 191
SciRes Copyright © 2009 JBiSE
tions have been described at DNA level [21,22,23,24],
and more than 100 at protein level. It would be great
helpful if we can build a quantitative relationship be-
tween von Hippel-Lindau protein mutation and von
Hippel-Lindau disease status, that is, the relationship
between mutant protein and its clinical outcome.
In this study, we build a descriptively quantitative rela-
tionship between changed primary structure of mutated
von Hippel-Lindau protein and the classification of its
clinical outcome, distinguish the classifications of clinical
outcomes as well as the endocrine and nonendocrine neo-
plasia induced by mutations of von Hippel-Lindau protein.
2.1. Data
The human von Hippel-Lindau disease tumor suppr essor
with total 132 mutations (accession number P40337;
December 4, 2007; Entry version 91) is obtained from
UniProtKB/Swiss-Prot entry [25]. Among them, 123 are
missense point mutations , 7 deletions and 3 insertions.
2.2. Amino-Acid Distribution Probability
Among three approaches developed by us, the amino-acid
distribution probability is mainly related to the positions of
amino acids along the protein, which is suitable for mutation
analysis, and we have used this approach in a number of our
previous studies [2,3,4,26,27,28,29,30,31,32,33,34,35,36,37,
38,39,40,41,42,43,44]. The quantification is developed along
such a thought, for example, how do two amino acids dis-
tribute along a protein sequence? Our intuition may suggest
that there would be one amino acid in the first half of the
sequence and anothe r one in the second half. In fact, there are
only three possible distributions, 1) both amino acids are in
the first half, 2) one amino acid is in each half and 3) both
amino acids are in the second half. Thus, each distribution
has the probability of 1 /3. If we do not distinguish either the
first half or second half but are simply interested in whether
both amino acids are in both halves or in any half, there will
be the probability of 1/2 for each distribution.
If we are interested in the distribution probability of
three amino acids in a protein, we naturally imagine to
grouping the protein into three partitions, and our intuition
may suggest that each partition contains an amino acid. If
we do not distinguish the first, second and third partition,
actually there are to tally three types of dis tributions , i.e. 1)
each amino acid is in each partition, 2) two amino acids are
in a partition and an amino acid is in another partition, and
3) three amino aci ds are in a partition.
In this situation, the distribution probability can be
calculated according to the statistical mechanics, which
classifies the distribution of elementary particles in en-
ergy states according to three assumptions of whether
distinguishing each particle and energy state, i.e. Max-
well-Boltzmann, Fermi-Dirac and Bose-Einstein as-
sumptions [45]. We actually use the Maxwell-Boltzmann
assumption for computing amino-acid distribution
probability, which is equal to
 !...!!
10 n
 !...!!
[45], where r is the number of amino
acids, n is the number of partitions, rn is the number of
amino acids in the n-th partition, qn is the number of
partitions with the same number of amino acids, and ! is
the factorial function.
Thus, the distribution probabilities are different for
these three types of distributions of three amino acids,
say, 0.2222 for 1), 0.6667 for 2) and 0.1111 for 3).
Clearly the protein can only adopt one type of distribu-
tion for these three amino acids, which is the actual dis-
tribution probability.
For four amino acids, there are five distributions, 1) each
partition contains an amino acid, 2) a partition contains
two amino acids and two partitions contain an amino acid
each, 3) two partitions contain two amino acids each, 4) a
partition contains an amino acid and a partition contains
three amino acids, and 5) a partition contains four amino
acids. Their distribution probabilities are 0.0938 for 1),
0.5625 for 2), 0.1406 for 3), 0.1875 for 4), and 0.0156 for
5). Furthermore, there are seven distributions for five
amino acids, 11 distributions for six amino acids, 15 dis-
tributions for seven amino acids, and so on.
2.3. Quantification of Wild-Type von Hippel-
Lindau Protein
Table 1. Amino acids, their composition and distribution prob-
ability in wild-type human von Hippel-Lindau protein. (A,
alanine; R, arginine; N, asparagine; D, aspartic acid; C, cys-
teine; E, glutamic acid; Q, glutamine; G, glycine; H, histidine; I,
isoleucine; L, leucine; K, lysine; M, methionine; F, phenyla-
lanine; P, proline; S, serine; T, threonine; W, tryptophan; Y,
tyrosine; V, valine.)
Amino acid Number Distribution probability
A 10 0.0476
R 20 0.0067
N 9 0.1770
D 11 0.1077
C 2 0.5000
E 30 0.0001
Q 8 0.0673
G 18 0.0389
H 5 0.0640
I 6 0.1543
L 20 0.0422
K 3 0.1111
M 3 0.6667
F 5 0.2880
P 19 0.0319
S 11 0.0404
T 7 0.2142
W 3 0.6667
Y 6 0.2315
V 17 0.1280
192 S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 190-199
SciRes Copyright © 2009
With respect to the wild-type von Hippel-Lindau protein,
for example, there are eight glutamines “Q” in von Hip-
pel-Lindau protein (Table 1). We may ask how these
eight Qs distribute along the von Hippel-Lindau protein?
According to the problem of the occupancy of subpopu-
lations and partition s [45], the simple way to answer this
question is to imagine that we would divide the von Hip-
pel-Lindau protein into eight equal partitions, and each
partition has about 27 amino acids (213/8=26.625) be-
cause the von Hippel-Lindau protein is composed of 213
amino acids, then there would be 22 configurations for all
the possible distributions of eight Qs (Table 2).
Here, we calculate two distribution probabilities in Ta-
ble 2 as example according to the above equation. For
eight Qs equally distribute in each partition (the second
row in Table 2), we have q0=0, q1=8, . . . q8=0; and r1=1,
r2=1, . . . r8=1. Thus, we have the distribution probability,
!8 8
Clearly, the von Hippel-Lindau protein can adopt only
one distribution pattern, which is that two partitions
contain zero Q, five partitions contain one Q and one
partition contains three Qs (the fourth row in Tab le 2 ).
So we have q0=2, q1=5, q2=0, q3=1, q4=0, q5=0, q6=0,
q7=0, q8=0; and r1=0, r2=0, r3=1, r4=1, r5=1, r6=1, r7=1,
r8=3, that is,
!8 8
In such a manner, we can quantify each amino acid in
wild-type von Hippel-Lindau protein. Thereafter, we can
assign these probabilities to each amino acid in the von
Hippel-Lindau protein as shown in Figure 1, from which
we get the visual sense of how these distribution prob-
abilities go along the von Hippel-Lindau protein, and
more importantly we can sum up these distribution prob-
abilities together for al l 213 amino acids in t he pr ot ei n.
us a way to estimate the position of am ino acid in a protein,
because there is a standard method for the computation
using Maxwell-Bolzmann assumption, which saves us
from inventing new computational methods. Moreover, the
primary structure is the base for higher - le vel structure, t hus
any mutation in primary structure would lead to the change
in distribution probability, in higher-level structure, and
finally the biological function. This is the biological mean-
ing of use of Maxwell- Bolzmann assumption for quantify-
Actually, the Maxwell-Bolzmann assumption provides
Table 2. All possible distributions of eight glutamines in von Hippel-Lindau protein. (Bold and italic is the real distribution.)
Partition 1 Partition 2 Partition 3 Partition 4 Partition 5 Partition 6 Partition 7 Partition 8 Probability
1 1 1 1 1 1 1 1 0.002403
1 1 1 1 1 1 2 0.0673
1 1 1 1 1 3 0.0673
1 1 1 1 4 0.0280
1 1 1 5 5.6076e-3
1 1 6 5.6076e-4
1 7 2.6703e-5
8 4.7684e-7
1 1 1 1 2 2 0.2523
1 1 1 2 3 0.2243
1 1 2 4 0.0421
1 2 5 3.3646e-3
2 6 9.3460e-5
1 1 2 2 2 0.1682
1 2 2 3 0.0841
2 2 4
2 2 2 2 0.0105
1 1 3 3 0.0280
2 3 3 5.6076e-3
1 3 4 5.6076e-3
4 4 1.1683e-4
3 5 1.8692e-4
S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 190-199 193
SciRes Copyright © 2009 JBiSE
VHL protein position
021436485107 128 149 170 192 213
Amino-acid distribution probability
VHL protein position
Amino-acid distribution probability
Figure 1. Visualization of amino-acid distribution probability in wild-type human von Hippel-Lindau protein.
cation of protein sequence.
In this context, any clinical manifestations related to
mutation in proteins would have different distribution
probabilities determined by Maxwell-Bolzmann as-
sumption. This is the association between them.
2.4. Quantification of Mutated von
Hippel-Lindau Proteins
The calculation in the abov e subsection is referred to the
amino-acid distribution probability before mutation, say,
the amino-acid distribution probability in wild-type von
Hippel-Lindau protein. Obviously any point mutation
leads an amino acid to change to another one, which
certainly would change the distribution pattern of both
original and mutated amino acids, thus the amino-acid
distribution probability would differ for both original
and mutated amino acids between before and after muta-
For example, the missense mutations at the CpG mu-
tation hotspot at codon 167 can mutate arginine “R” to
glycine “G”, or glutamine “Q” or tryptophan “W” [13,
46] leading to type I-II, type II and type II von Hippel-
Lindau disease, respectively. In above subsection, we
have calculated the distribution probab ility of Qs (Table
2) before mutation, and now we show the calculation of
distribution probability after R167Q mutation.
After this mutation, there are nine Qs in the von Hip-
pel-Lindau mutant (Table 3), for which we hav e
!9 9
while its distribution probability before this mutation is
0.0673, so the mutation decreases the distribution prob-
ability of Q. On the other hand, there are 20 and 19 Rs
before and after this mutation. Their distribution prob-
abilities are 0.0067 and 0.0030 b efore an d after mutation,
so this mutation decreases the distribution probability of
R, too. The overall effect for this mutation is
(0.0030–0.0067)+ (0.0197–0.0673)=–0.0513, that is, the
mutation reduces the distribution probability for von
Hippel-Lindau protein.
Since von Hippel-Lindau protein functions as whole,
we can calculate the change led by the mutation in fol-
lowing way. The su m of all th e d istribution probab ility is
19.6114 in wide-type von Hippel-Lindau protein (Figure
1), while the above calculated mutation leads the sum of
mutation results in 2.23% decrease in the measure
In this way, we have the quan
all the distribution probability to be 19.1731, thus this
titative measure for the
anged primary structure o f von Hippel-Li ndau mutant s
and we also have documented clinical manifestations
induced by the mutations of von Hippel-Lindau protein,
thus we can build a quantitative relationship between
changed structure and clinical outcome.
2.5. Descriptively Probabilistic R
For building quantitative relationship between mutation
and clinical outcome, we use the descriptively probabil-
istic method, as our quantification is the amino-acid dis-
tribution probability and each individual mutation re-
lated to its clinical outcome is presented as frequency.
Therefore, we use the cross-impact analysis to couple
194 S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 190-199
SciRes Copyright © 2009 JBiSE
Table 3. Distribution pattern of glutamines before and after
mutation at position 167 in von Hippel-Lindau protein.
Befor e mutation0 0 1 11 1 1 3 -
After mutation 0 0 0 20 1 3 0 3
em [35,47,48,49,50,51,52,53], because the amino-acid
n is based on permutation, and can
nted as mean±SD for normal distribu-
obability in
s on the re-
distribution probability either increases or decreases af-
ter mutation, which is a 2-possibilty event, and the
clinical outcome either occurs or does not occur after
mutation, which is a yes-and-no event. Thereafter, we
can use the Bayesian equation to calculate the probabil-
ity of occurrence of clinical outcome under a mutation.
2.6. Classification of Clinical Outcomes
It is extremely challenging how to use a mathema
modeling to distinguish the clinical outcomes with re-
spect to mutant von Hippel-Lindau protein because of
the variety of clinical outcomes. In an effort towards
solving this problem, we employ our second quantifica-
tion, amino-acid pair pred ictability, whose relational an d
applications have been published intensively (for re-
views, see [2,3,4]).
This quantificatio
calculated in the following way. For example, there
are 30 glutamic acids “E” and 20 Rs in von Hippel-
Lindau protein, the predicted frequency of amino-acid
pair ER would be 3 (30/21320/212212=2.817), while
we do find three ERs in the protein, so the amino- acid
pair ER is predictable. Still, the predicted frequency of
EE would be 4 (30/21329/212212=4.085), but actually
the EE appears nine times in reality. This is the case that
the actual frequenc y is larger than its predicte d one. In this
manner, we can quantify a protein sequence according to
the percentage of how many amino-acid pairs are predict-
able among all the amino-acid pairs in given protein as
well as its mutants. For instance, the predictable portion of
amino-acid pairs is 27.54% in wild-type von Hip-
pel-Lindau protein and 31.88% in its P25L mutant.
2.7. Statistics
The data are prese
tion or median with interquatile range for non-normal
distribution. The Kruskal-Wallis one-way ANOVA and
Chi-square are used for statistical inference, and P < 0.05
is considered significant.
After computing amino-acid distribution pr
wild-type von Hippel-Lindau protein and in its 132 mu-
tants, we have 132 changed amino-acid distribution
probabilities. Firstly, we can use the cross-impact analy-
sis to build a quantitative relationship between the in-
crease/decrease of distribution probability after muta-
tions and the clinical diag nosis, because the cross-impact
analysis is particularly suited for two relevant events
coupled together [35 ,47,48,49,50,51,52,53].
Figure 2 displays the cross-impact analysi
tionship between changed primary structure and von
Hippel-Lindau disease. At the level of amino-acid dis-
tribution probability, P(2) and
Pare the decreased
and increased probabilities ind ucy mutations, and 53
and 79 mutations result in the distribution probability
decreased and increased, respectively. At the level of
clinical diagnosis: 1)
ed b
2|1P is the impact probability
(conditional probabilitt the von Hippel-Lindau
disease is diagnosed under the condition of increased
distribution probability, and 70 mutations have such an
effect. 2)
y) tha
2|1P is the impact probability that other
disease is sed under the condition of increased
distribution probability, and 9 mutations work in such a
manner. 3) P(1|2) is the impact probability that the von
Hippel-Lindau disease is diagnosed under the condition
of decreased distribution probability, and 44 mutations
play such a role. 4)
2|1P is the impact probability
that other disease is ded under the condition of
decreased distribution probability, and 9 mutations fall
into this category. At the level of combined events, we
can see the combined results of changed structure and
von Hippel-Lindau disease.
Ta bl e 4 lists the calculate
d probabilities with respect
to Figure 2, from which several interesting points can be
drawn. 1) As
2P is larger than P(2), a mutation has a
larger chance of ireasing the distribution probability in
von Hippel-Lindau mutant. 2) As
P much lar-
ger than
2|1 is
2|1P, a mutation that ins the distribu-
tion probabas about n ine ten th chan ce o f b eing vo n
Hippel-Lindau disease. 3) As P(1|2) is much larger than
ility h
2|1P, a mutation that decreases the distribution
lity has much larger chance of being von Hippel-
Lindau disease.
able 4. Computed probabilities in reference to the cross-im-T
pact analysis in Figure 2.
2=70/79=0.8861 |1P
P(12)= |2)×P=44
P(1 (2)/53×53/132=0.3333=44/132
S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 190-199 195
SciRes Copyright © 2009
Probability increases (n = 79)
Probability decreases (n = 53)
P(2) = 1 -- P(2)
Di s t ribut ion probabil it y (eve nt 2)Clinical diagnosis (event 1)Combined event
Other d i sea se ( n = 9 )
VHL disease (n = 44)
VHL disease (n = 70)
Other diseas
P(12) = 70/132
e (n = 9)
P(12) = 9/132
P(1|2) = 1 -- P(1|2)
P(1|2) = 1 -- P(1|
P(12) = 44/132
P(12) = 9/132
Figure 2. Cross-impact relationship among von Hippel-Lindau protein mutation, changed
amino- acid distribution probability, and clinical diagnosis.
Secondly, wse the Bayes’ law
e u
1|2 P
P, whindicates the probabilities o
nces of two ev [54], to determine the p
(1), von Hippelndau disease under a mu
ause P(2) and
have already been
ross- imwhile is the p
at the distributrobability con-
ition that the voippel-Lindau disease is diagnosed.
As P(1|2)=44/0.8302 (Table 4), and = 44/
ion p
n H
f occur-
tation, be-
defined in
pact analysis,
th decreases under the
 
.08302 4015.0
(4 P
0.8635, namely, the patient has nine tenth chance of
eing von Hippel-Lindau disease when a new mutation
found in von Hippel-Lindau protein.
Among patients with von Hippel-Lindau disease,
bout 40% of mutations are genomic deletions and the
st are predominantly truncating or missense mutations,
hich do not occur within the first 53 amino acids
5,56]. In this study, we focus on the mutations of von
ippel-Lindau protein. From a probabilistic viewpo
ur results indicate the chance of being diagnos
von Hippel-Lindau disease when a new von Hippel-
Lindau mutant occurs.
The von Hippel-Lindau disease is characterized by
marked phenotypic variability [5 7,58], due to mosaicism
[59], modifier effects [60], and mainly allelic heteroge-
neity [61]. All these result in complicated clinical classi-
fications. Thus, we use the predictable portion of amino-
acid pairs to model the classifications.
Figure 3 illustrates the classification with respect to
the predictable portion of amino-acid pairs. Although
there are large overlaps among classifications, our quan-
tification already disting uishes them to some degr ee. For
example, in comparison with von Hippel - Lindau disease,
our quantification shows relatively lower in pheochro-
mocytoma and higher in other disorders (P=0.079,
Kruskal-Wallis one-w ay ANOVA). The lack of statistical
significance is certainly, in part, due to few cases in
some groups, however the trend is clear, which paves the
way for further classification using more sophisticated
mathematical models.
Genotype-phenotype relationships have revealed that
a certain number of missense mutations are associated
with a high risk of pheochromocyto ma but the mutatio ns
eir functions are associated with a low
ts with type II von Hippel-Lindau dis-
ease have missense mutations whereas the large dele-
ed as the that totally loss th
risk. Most patien
196 S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 190-199
SciRes Copyright © 2009 JBiSE
pairs induced by mutations of von Hip-
eo), von Hippel-Lindau disease and other
with an interquatile range (P = 0.079,
Figure 3. Predictable portion of amino-a
pel-Lindau protein in pheochromocytoma
disorders. The data are presented as med
Kruskal-Wallis one-way ANOVA).
161116 21 27 32 3742 47 52
Figure 4. Distribution of changed amino-acid d
crine neoplasia induced by mutations of von
istribution probability in endocrine and no
Hippel-Lindau protein (P = 0.094, Chi-square).
Changed amino-acid distribution probability (%)
Endocrine neoplasia
Endocrine neoplasia
Mutants in von Hippel-Linda u protein
1713 19 25 32 38 44 50 56 62
Changed amino-acid distribution probability(%)
Nonendocrine neoplasia
Nonendocrine neopla sia
Mutants in von Hippel-Lindau protein
S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 190-199 197
SciRes Copyright © 2009 JBiSE
ype I
ribution probability (upper panel),
h might provide the much clearer pattern,
l viewpoint, one could consider t
is different. Without suc
t a theoretical study finds
ical assays. Our ap-
tions and truncating mutations predominate in t
families [11,19,62,63]. Many missense mutations caus-
ing a type I phenotype are involved in the core hydro-
phobic residues and were predicted to disrupt protein
structure, whereas type II phenotype missense mutations
nvolved in substitutiare ions at a surface amino acid that
does not cause a total loss of function [64,65].
Figure 4 displays the distribution of changed amino-
acid distribution probability in endocrine neoplasia
(pheochromocytoma, type II von Hippel-Lindau disease)
and nonendocrine neoplasia (type I von Hippel-Lindau
disease). As can be seen, the mutations that led to the
endocrine neoplasia have the trend to increase the
amino-acid dist
whereas the mutations that led to nonendocrine neopla-
sia have the effect to either increase or decrease the
amino-acid distribution probability (lower panel). The
difference between two panels is mainly considered
from view of symmetry. As the x-axis is related to the
number of von Hippel-Lindau mutations, this figure
would be different when more mutations would be found
in future, whic
although we did not find the statistical difference be-
tween two panels (P=0.094, Chi-square) now.
From a theoreticao No.
calculate the distribution probability of all 19 potential
types of mutations at each position of von Hippel-Lindau
protein, and then find the link between mutations and
clinical outcomes. However, the amount of computation
is huge because it would be equal to 2.36910272 muta-
tions (19213), which is not only beyond the capacity of
any computers, but also beyond the capacity for com-
parison. Actually, we really know that each position does
not have 19 types of potential mutations, because this
mutation process is gov erned by the tran slation probab il-
ity between RNA codon and mutated amino acids [66,
67,68]. On the other hand, our study is focused on the
documented data rather than the simulated data.
In this study, we use a single valu e, the sum of all dis-
tribution probability to represent the normal von Hippel-
Lindau protein and its mutated proteins, respectively,
because there is no other way to use a single value dy-
namically to represent a protein, namely, the value is
different when a proteinh a
measure, we cannot model a protein dynamically with its
mutations. To the best of knowledge, currently it is only
the accession number that can represent a protein
uniquely, however it has nothing to do with the protein
itself, i.e. composition, length, function, etc.
In general, one would hope to verify this type of study
against the real-life cases, which is possible in future
although it would deal with a large-scale collaboration
because this type of diseases is not frequently seen in
clinical settings, for example, the von Hippel-Lindau
disease has a birth incid ence of abou t 1 in 36 000 [11,12].
It will take years to verify wha
with fast-speed computational technique. Even, we can-
not verify all the theoretical studies, for example, we
cannot create another earth without global warming.
The implications of this study include two aspects. 1)
relationship between changed primary structTheure and
changed function is very meaningful, because it provides
the dynamic rather than static relationship between mu-
tant protein and its function. This can furthermore pro-
vide us the basis for building a dynamic model to predict
the new function in mutant proteins. Nevertheless, we
need to quantify the proteins in order to build a dynamic
model and this study is doing in su ch a way. 2) Fro m the
clinical viewpoint, the classification of von Hippel-
Lindau disease as well as many mutation related diseases
needs a considerable amount of clin
proach can provide a probabilistic estimate for disease
classification after determining which amino acid has
mutated, because the primary structure of protein is the
base for its high-level structure and function.
This study was partly supported by Guangxi Science Foundation (No.
0537012-G and 0991080), and Guangxi Academy of Sciences (project
[1] K. C. Chou, (2004) Structure bioinformatics and its im-
pact to biomedical science, Curr. Med. Chem, 11,
[2] G. Wu and S. Yan, (2002) Randomness in the primary
structure of protein: Methods and implications, Mol. Biol.
Today, 3, 55-69.
[3] G. Wu and S. Yan, (2006) Mutation trend of hemaggluti-
nin of influenza A virus: A review from computational
mutation viewpoint, Acta Pharmacol. Sin., 27, 513-526.
[4] G. Wu and S. Yan, (2008) Lecture notes on computational
mutation, Nova Science Publishers, New York, 2008.
[5] Von Hippel, (1911) Die anatomische Grund lage der von
mir beschriebenen ‘sehr seltenen Erkrankung der Netz-
haut’, Graefes. Arch. Ophthalmol., 79, 350-377.
[6] A. Lindau, (1926) Studien uber kleinhirncysten, bau,
pathogenese und bezoejimgem zur angiomatosis retinae,
Acta Pathol. Microbiol. Scand., Suppl 1, 1-128.
[7] K. L. Melmon and S. W. Rosen, (1964) Lindau’s disease,
Am. J. Med., 36, 595-617.
[8] V. V. Michels, (1988) Investigative studies in von Hip-
pel-Lindau disease, Neurofibromatosis, 1, 159-163.
[9] H. P. Neumann, (1987) Basic criteria for clinical diagno-
sis and genetic counselling in von Hippel-Lindau syn-
drome, Vasa, 16, 220-226.
[10] R. R. Lonser, G. M. Glenn, M. Walther, E. Y. Chew, S. K.
Libutti, W. M. Linehan, and E. H. Oldfield, (2003) von
Hippel-Lindau disease, Lancet, 361, 2059-2067.
[11] E. R. Maher, A. R. Webster, F. M. Richards, J. S. Green,
P. A. Crossey, S. J. Payne, and A. T. Moore, (1996) Phe-
notypic expression in von Hippel-Lindau disease: Corre-
198 S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 190-199
SciRes Copyright © 2009 JBiSE
Mol. Genet., 4,
ity by the von
Kaelin, (2002) Molecular basis of the VHL
er, and oxygen sensing, J. Am. Soc. Nephrol.,
gical basis, clinical criteria, genetic testing,
. Tory, I. Kuzmin, T. Stackhouse, F. Latif, W.
lations with phenotype, Hum. Mutat.,
mas, (1998) Germ-
von Hip-
von Hippel-Lindau disease tumor
suppressor gene, Hum. Mutat., 12, 417-423.
5] A. Bairoch and R. Apweiler, (2000) The SWISS-PROT
protein sequence data bank and its supplement TrEMBL
cid pairs in human haemoglobin
alysis of presence
Analysis of distributions of
lations with germline VHL gene mutations, J. Med.
Genet., 33, 328-332.
[12] F. M. Richards, S. J. Payne, B. Zbar, N. A. Affara, M. A.
Ferguson-Smith, and E. R. Maher, (1995) Molecular
analysis of de novo germline mutations in the von Hip-
pel-Lindau disease gene, Hum.
[13] F. Latif, K. Tory, J. Gnarra, M. Yao, F. M. Duh, M. L.
Orcutt, et al., (1993) Identification of the von Hip-
pel-Lindau disease tumor suppressor gene, Science, 260,
[14] P. O. Schnell, M. L. Ignacak, A. L. Bauer, J. B. Striet, W.
R. Paulding, and M. F. Czyzyk-Krzeska, (2003) Regula-
tion of tyrosine hydroxylase promoter activ [28]
Hippel-Lindau tumor suppressor protein and hy-
poxia-inducible transcription factors, J. Neurochem., 85,
[15] W. G. Jr.
hereditary cancer syndrome, Nat. Rev. Cancer, 2,
[16] W. G. Jr. Kaelin, (2003) The von Hippel-Lindau gene,
kidney canc
14, 2703 -2711.
[17] T. Shuin, I. Yamasaki, K. Tamura, H. Okuda, M. Furihata,
and S. Ashida, (2006) Von Hippel-Lindau disease: Mo-
lecular patholo
and a
clinical features of tumors and treatment, Jpn. J. Clin.
Oncol., 36, 337-343.
[18] M. Ohh, (2006) Ubiquitin pathway in VHL cancer syn-
drome, Neoplasia, 8, 623-629.
[19] F. Chen, T. Kishida, M. Yao, T. Hustad, D. Glavac, M.
Dean, J. R. Gnarra, M. L. Orcutt, F. M. Duh, G. Glenn, J.
Green, Y. E. Hsia, J. Lamiell, H. Li, M. H. Wei, L.
Schmidt, K
M. Linehan, M. Lerman, and B. Zbar, (1995) Germline
mutations in the von Hippel-Lindau disease tumor sup-
pressor gene: Corre
5, 66-75.
[20] S. Lee, E. Nakamura, H. Yang, W. Wei, M. S. Linggi, M.
P. Sajan, R. V. Farese, R. S. Freeman, B. D. Carter, W. G.
Jr. Kaelin, and S. Schlisio, (2005) Neuronal apoptosis
linked to EglN3 prolyl hydroxylase and familial phaeo-
chromocytoma genes: developmental culling and cancer.
Cancer Cell, 8, 1-13.
[21] Clinical Research Group for VHL in Japan, (1995)
Germline mutations in the von Hippel-Lindau disease
(VHL) gene in Japanese VHL, Hum. Mol. Genet., 4,
[22] H. P. Neumann, B. Bender, I. Zauner, D. P. Berger, C.
Eng, H. Brauch, and B. Zbar, (1996) Monogenetic hy-
pertension and pheochromocytoma, Am. J. Kidney Dis.,
28, 329-333.
[23] S. Olschwang, S. Richard, C. Boisson, S. Giraud, P.
Laurent- Puig, F. Resche, and G. Tho
line mutation profile of the VHL gene in
pel-Lindau disease and in sporadic hemangioblastoma,
Hum. Mutat., 12, 424-430.
[24] C. Stolle, G. Glenn, B. Zbar, J. S. Humphrey, P. Choyke,
M. Walther, S. Pack, K. Hurley, C. Andrey, R. Klausner,
and W. M. Linehan, (1998) Improved detection of germ-
line mutations in the
in 2000, Nucleic Acids Res., 28, 45-48.
[26] N. Gao, S. Yan, and G. Wu, (2006) Pattern of positions
sensitive to mutations in human haemoglobin -chain,
Protein Pept. Lett., 13, 101-107.
[27] G. Wu and S. Yan, (2000) Prediction of distributions of
amino acids and amino a
-chain and its seven variants causing-thalassemia from
their occurrences according to the random mechanism,
Comp. Haematol. Int, 10, 80-84.
G. Wu and S. Yan, (2001) Analysis of distributions of
amino acids, amino acid pairs and triplets in human
insulin precursor and four variants from their occur-
rences according to the random mechanism, J. Bio-
chem. Mol. Biol. Biophys., 5, 293-300.
[29] G. Wu and S. Yan, (2001) Analysis of distributions of
amino acids and amino acid pairs in human tumor necro-
sis factor precursor and its eight variants according to
random mechanism, J. Mol. Model, 7, 318-323.
[30] G. Wu and S. Yan, (2002) Random an
bsence of two-and three-amino-acid sequences and
distributions of amino acids, two- and three-amino-acid
sequences in bovine p53 protein, Mol. Biol. Today, 3,
[31] G. Wu and S. Yan, (2002)
amino acids in the primary structure of apoptosis regula-
tor Bcl-2 family according to the random mechanism, J.
Biochem. Mol. Biol. Biophys, 6, 407-414.
[32] G. Wu and S. Yan, (2002) Analysis of distributions of
amino acids in the primary structure of tumor suppressor
p53 family according to the random mechanism, J. Mol.
Model, 8, 191
[33] G. Wu and S. Yan, (2004) Determination of sensitive
positions to mutations in human p53 protein, Biochem.
Biophys. Res. Commun., 321, 313-319.
[34] G. Wu and S. Yan, (2005) Searching of main cause lead-
ing to severe influenza A virus mutations and conse-
quently to influenza pandemics/epidemics, Am. J. Infect.
Dis., 1, 116-123.
[35] G. Wu and S. Yan, (2005) Prediction of mutation trend in
hemagglutinins and neuraminidases from influenza A vi-
ruses by means of cross-impact analysis, Biochem. Bio-
phys. Res. Commun., 326, 475-482.
G. Wu and S. Yan, (2006) Timing
[36] of mutation in hemag-
glutinins from influenza A virus by means of amino-acid
distribution rank and fast Fourier transform, Protein Pept.
Lett., 13, 143-148.
[37] G. Wu and S. Yan, (2006) Prediction of possible muta-
tions in H5N1 hemagglutinins of influenza A virus by
means of logistic regression, Comp. Clin. Pathol., 15,
[38] G. Wu and S. Yan, (2006) Prediction of mutations in
H5N1 hemagglutinins from influenza A virus, Protein
Pept. Lett., 13, 971-976.
[39] G. Wu and S. Yan, (2007) Improvement of model for
prediction of hemagglutinin mutations in H5N1 influenza
S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 190-199 199
SciRes Copyright © 2009
guishing of arginine, leucine and ser-
ine, Protein Pept. Lett., 14, 191-196.
0] G. Wu and S. Yan, (2007) Improvement of prediction of
mutation positions in H5N1 hemagglutinins of influenza
A virus using neural network with distinguishing of ar-
ginine, leucine and serine, Protein Pept. Lett., 14,
1] G. Wu and S. Yan, (2007) Prediction of mutations engi-
neered by randomness in H5N1 neuraminidases from in-
fluenza A virus, Amino Acids, 34, 81-90.
2] G. Wu and S. Yan, (2007) Prediction of mutations in H1
neuraminidases from North America influenza A virus
engineered by internal randomness, Mol. Divers., 11,
[43] G. Wu and S. Yan, (2008) Prediction of mutations initi-
ated by internal power in H3N2 hemagglutinins of influ-
enza A virus from North America, Int. J. Pept. Res. Ther.,
14, 41-51.
[44] G. Wu and S. Yan, (2008) Prediction of mutation in
H3N2 hemagglutinins of influenza A virus from North
America based on different datasets, Protein Pept. Lett.,
15, 144-152.
[45] W. Feller, (1968) An introduction to probability theory
and its applications, 3rd ed, Wiley, New York, 1, 34-40.
[46] B. Zbar, T. Kishida, F. Chen, L. Schmidt, E. R. Maher, F.
M. Richards, P. A. Crossey, A. R. Webster, N. A. Affara,
M. A. Ferguson-Smith, et al., (1996) Germline mutations
in the Von Hippel-Lindau disease (VHL) gene in families
from North America, Europe, and Japan, Hum. Mutat., 8,
[47] T. G. Gordon and H. Hayward, (1968) Initial experiments
with the cross-impact matrix method of forecasting, Fu-
tures, 1, 100-116.
[48] T. G. Gordon, (1969) Cross-impact matrices - an illustra-
tion of their use for policy analysis, Futures, 2, 527-531.
[49] S. Enzer, (1970) Delphi and cross-impact techniques: an
effective combination for systematic futures analysis,
Futures, 3, 48-61.
[50] S. Enzer, (1970) Cross-impact techniques in technology
assessment, Futures, 4, 30-51.
[51] A. P. Sage, (1977) Methodology for large-scale systems,
McGraw-Hill, New York, 165-203.
[52] G. Wu, (2000) Application of cross-impact analysis to the
relationship between aldehyde dehydrogenase 2 and
flushing, Alcohol Alcohol., 35, 55-59.
[53] G. Wu and S. Yan, (2008) Building quantitative relation-
ship between changed sequence and changed oxygen af-
finity in human hemoglobin-chain, Protein Pept. Lett., 15,
[54] Wikipedia, (2008) Bayes’ theorem,
http://en.wikipedia.org/wiki/ Bayes’_theorem.
[55] S. O. Ang, H. Chen, K. Hirota, V. R. Gordeuk, J. Jelinek,
Y. Guan, E. Liu, A. I. Sergueeva, G. Y. Miasnikova, D.
Mole, P. H. Maxwell, D. W. Stockton, G. L. Semenza,
and J. T. Prchal., (2002) Disruption of oxygen homeosta-
sis underlies congenital Chuvash polycythemia, Nature
Genet., 32, 614-621.
[56] Y. Pastore, K. Jedlickova, Y. Guan, E. Liu, J. Fahner, H.
Hasle, J. F. Prchal, and J. T. Prchal., (2003) Mutations of
von Hippel- Lindau tumor-suppressor gene and congeni-
tal polycythe mia, Am. J. Hum. Gene t. , 73, 412 -419.
[57] E. R. Maher, (2004) Von Hippel-Lindau disease, Curr.
Mol. Med., 4, 833-842.
[58] E. R. Woodward and E. R. Maher, (2006) Von Hip-
pel-Lindau disease and endocrine tumour susceptibility,
End. Relat. Cancer, 13, 415-425.
[59] M. T. Sgambati, C. Stolle, P. L. Choyke, M. M. Walther,
B. Zbar, W. M. Linehan, and G. M. Glenn, (2000) Mo-
saicism in von Hippel-Lindau disease: lessons from kin-
dreds with germline mutations identified in offspring
with mosaic parents, Am. J. Hum. Genet., 66, 84-91.
[60] A. R. Webster, F. M. Richards, F. E. MacRonald, A. T.
Moore, and E. R. Maher, (1998) An analysis of pheno-
typic variation in the familial cancer syndrome von
Hippel-Lindau disease: evidence for modifier effects,
Am. J. Hum. Genet., 63, 1025-1035.
[61] P. A. Crossey, C. Eng, M. Ginalska-Malinowska, T. W. J.
Lennard, J. R. Sampson, B. A. J. Ponder, and E. R.
Maher, (1995) Molecular genetic diagnosis of von Hip-
pel-Lindau disease in familial phaeochromocytoma, J.
Med. Genet., 32, 885-886.
[62] P. A. Crossey, F. M. Richards, K. Foster, J. S. Green, A.
Prowse, F. Latif, M. I. Lerman, B. Zbar, N. A. Affara, M.
A. Ferguson-Smith, and R. Maher, (1994) Buys CHCM,
identification of intragenic mutations in the von Hip-
pel-Lindau disease tumour suppressor gene and correla-
tion with disease phenotype, Hum. Mol. Genet., 3,
[63] E. R. Maher, A. R. Webster, F. M. Richards, J. S. Green,
P. A. Crossey, S. J. Payne, and A. T. Moore, (2000) Phe-
notypic expression in von Hippel-Lindau disease: corre-
lations with germline VHL gene mutations, J. Med.
Genet., 37, 62-63.
[64] C. E. Stebbins, W. G. Jr. Kaelin, and N. P. Pavletich,
(1999) Structure of the VHL-ElonginC-ElonginB com-
plex: Implications for VHL tumor suppressor function,
Science, 284, 455-461.
[65] S. J. Marx and W. F. Simonds, (2005) Hereditary hor-
mone excess: Genes, molecular pathways, and syn-
dromes, End. Rev., 26, 615-661.
[66] G. Wu and S. Yan, (2005) Determination of mutation
trend in proteins by means of translation probability be-
tween RNA codes and mutated amino acids, Biochem.
Biophys. Res. Commun., 337, 692-700.
[67] G. Wu and S. Yan, (2006) Determination of mutation
trend in hemagglutinins by means of translation prob-
ability between RNA codons and mutated amino acids,
Protein Pept. Lett., 13, 601-609.
[68] G. Wu and S. Yan, (2007) Translation probability be-
tween RNA codons and translated amino acids, and its
applications to protein mutations, in: Leading-Edge
Messenger RNA Research Communications, ed. Os-
trovskiy M. H. Nova Science Publishers, New York,
Chapter 3, 47-65.
viruses with distin