Discovering functional interaction patterns in Protein-Protein Interactions Networks

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Discovering functional interaction patterns in
Protein-Protein Interactions Networks

• 
  Authors
• 
  Mehmet E
  Turnalp
• 
  Tolga Can
• 
  Presented By
• 
  Sandeep
  Kumar

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Background

• Availability of genome scale protein network
• Understanding topological organization
• Identification of conserved subnetworks across
  different species
• Discover modules of interaction
• Predict functions of uncharacterized proteins
• Improve the accuracy of currently available
  networks

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Aim of study

• Using available functional annotations of
  proteins in PPI network and look for
  overrepresented patterns of interactions in the
  network
• Present new frequent pattern identification
  technique PPISpan

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Yeast as a model

• Why yeast genomics? A model eukaryote organism
• Well known PPI network

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PPI Network

• Protein protein interaction shown by edge between
  them indicating physical association in the form
  of modification, transport or complex formation
• Interesting conserved interaction patterns among
  species
• Patterns correspond to specific biological process

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Frequent sub-graphs
A graph (sub graph) is frequent if is support
(occurrence frequency) in a given dataset is no
less than minimum support threshold
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Example Frequent Subgraphs
GRAPH DATASET
(A)
(B)
(C)
FREQUENT PATTERNS (MIN SUPPORT IS 2)
(1)
(2)
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The Algorithm - PPISpan

• Based on gSpan
• Modified to adapt for PPI network
• Candidate generation
• Frequency counting

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Algorithm PPISpan (G, L, minSup)

• Set the vertex labels in G with GO terms from the
  desired GO level L
• S lt- all frequent 1-edge graphs in G in frequency
  based lexicographical order
• for each edge e in S (in ascending order
  frequency) do
• SubGraphs (e, minSup, e)
• Remove e from G

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Algorithm Subpgraphs (s, minSup, ext)

• If (feasible (s, ext))
• If DES code of s ! to its minimum DFS code
• return
• C lt- Generate all children of s (by growing an
  edge, ext)
• Maximal lt- true
• For each c in C (in DFS lexicographical order) do
• If support (c) gt minSup
• Subgraphs (c, minSup, c.ext)
• maximal lt- false
• If (maximal)
• output s

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Datasets used

• Database of interacting proteins (DIP)
• data constructed from high-throughput
• experiments
• String Database
• confidence weighted predicted data
• WI-PHI
• weighted yeast interactome enriched
  for direct
• physical interactions

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Gene Ontology annotations

• Used to assign functional category labels to the
  proteins in PPI network
• Collaborative effort to address the need of
  consistent descriptions of the gene products in
  different databases
• Provides description for biological processes,
  cellular components, and molecular functions

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GO slim terms

• Provides a broad overview of the functional
  categories in GO
• GO Slim Molecular Function Terms for S.
  Cerevisiae
• Term ID Definition
• GO3674 molecular function unknown
• GO16787 hydrolase activity
• GO16740 transferase activity
• GO5515 protein binding
• Total of 22 broad functional categories

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Research Steps

• Label the nodes with functional categories with
  GO annotations
• Consider molecular function hierarchy
• Focus on functional interaction patterns in
  arbitrarily topologies
• Find non-overlapping embeddings using PPISpan

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Problems faced

• Noise in PPI network
• False positives
• False negatives
• Accuracy and specificity of annotations of
  proteins

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Supporting embedding

• Specific instance of the functional pattern
  realized by certain proteins in the PPI network

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Experiment details

• Implemented in C
• Searched for frequent interaction patterns of
  support gt 15

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Pattern frequency in different datasets

• Number of patterns found

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Observation

• Most of the patterns are trees
• Star topology most abundant
• Cycles rare

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Comparison with known molecular complexes and
pathways

• Ignore topology and treat patterns as set of
  proteins for comparison
• Molecular complexes from MIPS (Munich Information
  Center for Protein Sequences) complex catalogue
  database
• Signaling, transport, and regulatory pathways
  from KEGG database
• Use high quality complexes

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cpcount

• Average number of different complexes or pathways
  the embeddings of a frequent interaction pattern
  overlaps with
• To speculate on the location of interacting
  patterns

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cpoverlap

• Quantifies the overlap between proteins in an
  embedding and known complexes and pathways
• Ratio of proteins in an embedding that are
  members of known functional modules

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Observations from comparison

• For some of the observed patterns, topology is
  more important than underlying functional
  annotations
• Comparison of all the patterns with random
  patterns in terms of overlap with MIPS complexes
• Comparison of all the patterns with random
  patterns in terms of overlap with transport and
  signaling pathways

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Analysis of patterns with MIPS complexes

• Selected patterns from DIP and WI-PHI networks
• Selected patterns from the STRING network
• cpoverlap of selected patterns with respect to
  MIPS complexes
• cpcount of selected patterns with respect to MIPS
  complexes

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Analysis of patterns with KEGG pathways

• Selected patterns from DIP, STRING and WI-PHI
  networks
• cpoverlap of selected patterns with respect to
  transport and signaling pathways
• cpcount of selected patterns with respect to
  transport and signaling pathways

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Some interesting Functional interaction patterns

• A frequent functional interaction pattern in the
  DIP network
• A frequent functional interaction pattern in the
  WI-PHI network
• A functional interaction pattern related to the
  MAPK signaling pathwaysignaling pathways
• A functional interaction pattern related to the
  SNARE interactions in vesicular transport

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Conclusions

• Proposed new frequent pattern identification
  technique, PPISpan
• utilized molecular function Gene Ontology
  annotations to assign non-unique labels to
  proteins of a PPI network
• identified significantly frequent functional
  interaction patterns
• Frequent patterns offer a new perspective into
  the modular organization of protein-protein
  interaction networks

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QUESTIONS ?
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THANK YOU