computational modeling of protein-protein interaction · 2014. 10. 8. · computational modeling of...
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Computational Modeling of
Protein-Protein Interaction
Yinghao Wu
Department of Systems and Computational Biology
Albert Einstein College of Medicine
Fall 2014
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Outline
• Binary prediction of Protein-
protein Interaction (PPI)
• Analysis of PPI networks
• Structural modeling of PPI
• Physical properties of PPI
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Outline
• Binary prediction of Protein-
protein Interaction (PPI)
• Analysis of PPI networks
• Structural modeling of PPI
• Physical properties of PPI
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Training data
(sequences of
interacting proteins)
Sequence
patterns
SVM Kernel
classifier
Predict new
interactions from
sequences
Training set for SVM kernel classifier= Positive training set (experimental interactions, some for
training, some for validation) + Negative training set (mostly random generated pairs)
Binary prediction of PPI: General
procedure
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Classification of Amino Acid(AA)
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Using Conjoint Triads for sequence pattern construction
Reduced-alphabet sequence pattern training:
1. Classify 20 AA types into 7
classes based on their
properties (hydrogen bonding,
hydrophobic, volumes of
sidechains, etc).
2. Build AA triplets using 7
classes, called “conjoint triad”
(343 unique types). Save in V
3. Calculate frequency of each
triad for each protein
sequence.
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Kernel Function
• di = (fi - min {f1, f2, . . .. . ., f343})/max{f1, f2, .
. .. . ., f343}
• DA={dA1,dA2,……,dA343}
• {DAB} = {DA} {DB}: a 686 dimensional vector
• Kernel Function:
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Kernel Function and parameter
adjustment C=128
Γ=0.25
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Network Prediction
One-core network
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Network Prediction
Multi-core network
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Network Prediction
Cross-over network
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Outline
• Binary prediction of Protein-
protein Interaction (PPI)
• Analysis of PPI networks
• Structural modeling of PPI
• Physical properties of PPI
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Protein-Protein Interaction Networks?
• Protein are nodes
• Interactions are edges
Yeast PPI network
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18
Introduction to graph theory
� Graph – mathematical object consisting of a set of:
�V = nodes (vertices, points).
�E = edges (links, arcs) between pairs of nodes.
�Denoted by G = (V, E).
�Captures pairwise relationship between objects.
�Graph size parameters: n = |V|, m = |E|.
V = { 1, 2, 3, 4, 5, 6, 7, 8 }
E = { {1,2}, {1,3}, {2,3}, {2,4}, {2,5}, {3,5}, {3,7}, {3,8}, {4,5}, {5,6} }
n = 8
m = 11
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Random network
• Connect each pair of node with prob p
• Expect value of edge is pN(N-1)/2
• Poisson distribution
– The node with high degree is rare
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Scale-free network
• Power-law degree distribution
• Hubs and nodes
• When a node add into network, it prefer to
link to hubs
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Hierarchical network
• Preserves network “modularity” via a
fractal-like generation of the network
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Hierarchical network
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• 3 types (modes) of comparative methods:
1. Network alignment
2. Network integration
3. Network querying
Types of Network Comparisons
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1. Network alignment:
• The process of comparison of two or more networks of the same type to identify regions of similarity and dissimilarity
• Commonly applied to detect subnetworks that are conserved across species and hence likely to present true functional modules
Types of Network Comparisons
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2. Network integration:
• The process of combining networks encompassing interactions of different types over the same set of elements (e.g., PPI and genetic interactions) to study their interrelations
• Can assist in uncovering protein modules supported by interactions of different types
Types of Network Comparisons
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• A grand challenge:
Types of Network Comparisons
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3. Network querying:
• A given network is searched for subnetworks that are similar to a subnetwork query of interest
• This basic database search operation is aimed at transferring biological knowledge within and across species
• Currently limited to very sparse graphs, e.g., trees
Types of Network Comparisons
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3. Network querying
Types of Network Comparisons
� Useful application for biologists: given a candidate module, align to a database of networks (“query-to-database”)
Query: Database:
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Summary
Types of Network Comparisons
Sharan and Ideker (2006) Nature Biotechnology 24(4): 427-433
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Network Alignment
• Finding structural similarities between two networks
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• Methods vary in these aspects:
A. Global vs. local
B. Pairwise vs. multiple
C. Functional vs. topological information
Network Alignment
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• Methods vary in these aspects:
A. Global vs. local
B. Pairwise vs. multiple
C. Functional vs. topological information
A.Local alignment:
� Mappings are chosen independently for each region of
similarity
� Can be ambiguous, with one node having different
pairings in different local alignments
� Example algorithms:
PathBLAST, NetworkBLAST, MaWISh, Graemlin
Network Alignment
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• Methods vary in these aspects:
A. Global vs. local
B. Pairwise vs. multiple
C. Functional vs. topological information
A.Global alignment:
� Provides a unique alignment from every node in the
smaller network to exactly one node in the larger
network
� May lead to inoptimal matchings in some local regions
� Example algorithms:
IsoRank, IsoRankN, Graemlin 2, GRAAL, H-GRAAL
Network Alignment
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• Methods vary in these aspects:
A. Global vs. local
B. Pairwise vs. multiple
C. Functional vs. topological information
B.Pairwise alignment:
� Two networks aligned
� Example algorithms:
GRAAL, H-GRAAL, PathBLAST, MaWISh, IsoRank
Multiple alignment:
� More than two networks aligned
� Computationally more difficult than pairwise alignment
� Example algorithms:
Greamlin, Extended PathBLAST, Extended IsoRank
Network Alignment
a b
c
d
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• Methods vary in these aspects:
A. Global vs. local
B. Pairwise vs. multiple
C. Functional vs. topological information
C.Functional information� Information external to network topology (e.g., protein sequence) used to
define “similarity” between nodes
� Careful: mixing different biological data types, that might agree or contradict
Topological information� Only network topology used to define node “similarity”
� Good – since it answers how much and what type of biological information
can be extracted from topology only
Network Alignment
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• In general, the network alignment problem is computationally
hard (generalizing subgraph isomorphism)
• Hence, heuristic approaches are devised
• For now, let us assume that we have a heuristic algorithm for
network alignment
• How do we measure the quality of its resulting alignments?
Network Alignment
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• Key algorithmic components of network
alignment algorithms:
– Node similarity measure
– Rapid identification of high-scoring alignments
from among the exponentially large set of possible
alignments
Network Alignment
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• How is “similarity” between nodes defined?
• Using information external to network topology, e.g., the sequence alignment score
• Homology, E-values, sequence similarity vs. sequence identity…
• Using only network topology, e.g., node degree,
• Using a combination of the two
Network Alignment
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Network Alignment
� How to identify high-scoring alignments?
� Idea: seeded alignment
�Inspired by seeded sequence alignment (BLAST)
�Identify regions of network in which “the best”
alignments likely to be found
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• How to identify high-scoring alignments?• Greedy seed and extend approaches
• Use the most “similar” nodes across the two
networks as “anchors” or “seed nodes” • “Extend around” the seed nodes in a greedy fashion
Network Alignment
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• How to identify high-scoring alignments?• Greedy seed and extend approaches
• Use the most “similar” nodes across the two
networks as “anchors” or “seed nodes” • “Extend around” the seed nodes in a greedy fashion
Network Alignment
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• How to identify high-scoring alignments?• Greedy seed and extend approaches
• Use the most “similar” nodes across the two
networks as “anchors” or “seed nodes” • “Extend around” the seed nodes in a greedy fashion
Network Alignment
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Network Alignment
• How to identify high-scoring alignments?• Greedy seed and extend approaches
• Use the most “similar” nodes across the two
networks as “anchors” or “seed nodes”• “Extend around” the seed nodes in a greedy fashion
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Network Alignment
• How to identify high-scoring alignments?• Greedy seed and extend approaches
• Use the most “similar” nodes across the two
networks as “anchors” or “seed nodes”• “Extend around” the seed nodes in a greedy fashion
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Network Alignment
• How to identify high-scoring alignments?• Greedy seed and extend approaches
• Use the most “similar” nodes across the two
networks as “anchors” or “seed nodes”• “Extend around” the seed nodes in a greedy fashion
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Take home message
• Binary prediction of Protein-protein
Interaction (PPI)
• Analysis of PPI networks• Different topologies of network
• Different type of network comparison
• Basic ideas of network alignment
• Structural modeling of PPI
• Physical properties of PPI
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Outline
• Binary prediction of Protein-
protein Interaction (PPI)
• Analysis of PPI networks
• Structural modeling of PPI
• Physical properties of PPI
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Outline
• Binary prediction of Protein-
protein Interaction (PPI)
• Analysis of PPI networks
• Structural modeling of PPI
• Protein-protein docking
• Template-based modeling
• Physical properties of PPI
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Outline
• Binary prediction of Protein-
protein Interaction (PPI)
• Analysis of PPI networks
• Structural modeling of PPI
• Protein-protein docking
• Template-based modeling
• Physical properties of PPI
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Two bases are compatible if their signatures
match
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Outline
• Binary prediction of Protein-
protein Interaction (PPI)
• Analysis of PPI networks
• Structural modeling of PPI
• Protein-protein docking
• Template-based modeling
• Physical properties of PPI
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Template-based modeling: general
methodology
• Dimeric threading
• Monomer threading and oligomer
mapping
• Template-based docking
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Outline
• Binary prediction of Protein-
protein Interaction (PPI)
• Analysis of PPI networks
• Structural modeling of PPI
• Physical properties of PPI
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Outline
• Binary prediction of Protein-
protein Interaction (PPI)
• Analysis of PPI networks
• Structural modeling of PPI
• Physical properties of PPI
• Kinetic rates
• Binding affinity
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Outline
• Binary prediction of Protein-
protein Interaction (PPI)
• Analysis of PPI networks
• Structural modeling of PPI
• Physical properties of PPI
• Kinetic rates
• Binding affinity
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Brownian Dynamics (BD)
• The dynamic contributions of the solvent are incorporated as a dissipative random force (Einstein’s derivation on 1905). Therefore, water molecules are not treated explicitly.
• Since BD algorithm is derived under the conditions that solvent damping is large and the inertial memory is lost in a very short time, longer time-steps can be used.
• BD method is suitable for long time simulation.
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Algorithm of BD
The Langevin equation can be expressed as
Here, ri and mi represent the position and mass of atom i, respectively. ζi is a frictional coefficient and is determined by the Stokes’ law, that is, ζi = 6πai
Stokesη in which aiStokes is a
Stokes radius of atom i and η is the viscosity of water. Fi is the systematic force on atom i. Ri is a random force on atom i having a zero mean <Ri(t)> = 0 and a variance <Ri(t)Rj(t)> = 6ζikTδijδ(t); this derives from the effects of solvent.
For the overdamped limit, we set the left of eq.1 to zero,
The integrated equation of eq. 8 is called Brownian dynamics;
where Δt is a time step and ωi is a random noise vector obtained from Gaussian distribution.
iii
ii
itt
m RFrr
++−=d
d
d
d2
2
ζ
iii
it
RFr
+=d
dζ
i
ii
i
iit
Tkt
tttt ω
Frr ∆+∆+=∆+
ζζB
2)()()(
(1)
(3)
(2)
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Brownian dynamic simulation of protein association
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Outline
• Binary prediction of Protein-
protein Interaction (PPI)
• Analysis of PPI networks
• Structural modeling of PPI
• Physical properties of PPI
• Kinetic rates
• Binding affinity
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Computational simulation of binding affinity:
thermodynamic cycles
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Summary
• Binary prediction of Protein-
protein Interaction (PPI)
• Analysis of PPI networks
• Structural modeling of PPI
• Physical properties of PPI