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九大数理集中講義Comparison, Analysis, and Control
of Biological Networks (3)Domain-Based Mathematical Models for Protein Evolution
Tatsuya Akutsu
Bioinformatics Center
Institute for Chemical Research
Kyoto University
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Contents A simple evolutionary model of protein
domains A domain-based model of protein-protein
interaction networks An evolutionary model of multi domain
proteins
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Motivation of Our Studies Explaining observed distributions on
proteins and PPI networks PPI networks are scale-free [Jeong et al., 2001]
#(proteins having k domain families) follows exponential distribution [Koonin et al., 2002]
#(proteins having k domains) follows power-law [Koonin et al.,
2002]
#(domains appearing in k proteins) follows power-law [Wuchty, 2001]
Providing simple evolutionary models In real proteins, what evolve are not networks
but genes/proteins
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An Evolutionary Model of Protein Domains
J.C. Nacher, M. Hayashida and T. Akutsu: Physica A, 367, 538-552, 2006
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Protein Domain
Domain: Well-defined region within a protein that either performs a specific function or constitutes a stable unit
Protein consisting of3 domains
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Evolutionary Model of Protein Domains
N proteins, each one consists of only one domain
(domains are different from each other)
We repeat T times the following steps:
a) With probability (1-a) we create a new protein with new domain(MUTATION)
b) Otherwise, we randomly select one protein and make a copy of it(PROTEIN DUPLICATION)
We assume that each protein consists of only one domain
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Model(continued)
i : i-th kind of domain : number of proteins consisting of i-th domain : time when i-th domain was first created
t
ka
dt
dk ii a
ii t
tck
ik
1-aa
Duplication of Protein
T timesa ~ 1.0
Mutation
Q(k): number of domains each of which appears in k proteins
)]/1(1[)( akkQ
it
As in Barabasi &Albert 1999
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Protein duplication
mutation
Prob.= aProb.= 1- a
Model of Protein Evolution
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Exaplanation of Q(k)
1 2 3 4 5
Types of domains
6
Types of proteins
1,2,2,2,3,1 654321 kkkkkk
0)5()4(,)3(,)2(,)1( 61
63
62 QQQQQ
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Our Model vs. Preferential Attachment Similarity
#(proteins with the i-th domain) ⇔ degree of the i-th node Duplication of protein with the i-th domain ⇔ Attachment of an edge to
the i-th node Mutation (creation of a protein with a new domain) ⇔ Addition of a new
vertex Difference:
new node
new edge
3)]/1(1[ vs. kk a
1-a aDuplication
a ~ 1.0
Mutation
PD(1)=3PD(2)=1PD(3)=1
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A Domain-Based Model of Protein-Protein
Interaction Networks
J.C. Nacher, M. Hayashida and T. Akutsu: BioSystems, 95, 155-159, 2009
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A Domain-Based Model of Protein-Protein Interactions [Sprinzak & Margalit 2001, Deng et al. 2002] Proteins interact ⇔ There exist interacting domain pair(s)
A B
C D
X
Y Z
Domain-DomainInteraction
Protein-ProteinInteraction
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Combination of Domain Evolution Model and Domain-based Protein-Protein Interaction Model
Evolutional model of protein domains
Random interaction of domains
Domain-based protein-protein interactions Proteins interact ⇔ There exist interacting domain pair(s)
)]/1(1[)( aD kkP
) with interacts Pr( ji DD
Scale-free property of PPI (protein-protein interaction network) )]/1(1[)( a
PPI kkP
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Mathematical Analysis
domainA
domainB
nA=x=3
nB=y=2
3 proteinswith
degree 2
However, if the number of domain-domain interactions is large,the distribution approaches to the normal distribution because of the central limit theorem
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An Evolutionary Model of Multi Domain Proteins
J.C. Nacher, M. Hayashida and T. Akutsu: BioSystems, 101:127-135, 2010.
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Domain Fusion and Internal Duplication (1) 1. Internal Duplication
Duplication of one or more domains inside one protein 2. Domain Fusion
Two proteins are merged
Domain Fusion
MutationProtein
DuplicationInternal Domain
duplication
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Modeling of Duplication, Mutation and Fusion (1) Ni
(t) : #proteins having i domains at time t
pm : prob. mutation (creation of new protein) occurs
pd : prob. duplication occurs
pf : prob. fusion occurs
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Modeling of Duplication, Mutation and Fusion (2)
By letting ni(t) =Ni
(t) /t and ni = ni
(t) for t→∞
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Modeling of Duplication, Mutation and Fusion (3)Using generation function, we have exact solution
Using Stirling’s approximation
It shows nk follows almost exponential distribution
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Modeling of Internal Duplication
By letting ni(t) =Ni
(t) /t and ni = ni
(t) for t→∞
nk follows
power-law
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Combination of Mutation, Fusion, Internal/External Duplications
Difficult to solve
⇒ Computer simulation
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Summary A simple (simplest? ) model of protein domain
evolution, which explains power-raw distribution A domain-based model of protein-protein
interaction network ⇒ Explains power-law property of PPI
⇒ Good agreement between simulation and real data
⇒ Simpler than existing models (e.g., duplication-divergence)
An evolutionary model of multi-domain proteins
⇒ #(proteins having k domain families) follows exponential
⇒ #(proteins having k domains) follows power-law
⇒ Good agreement between simulation and real data
⇒ Importance of role of internal duplications