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

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discovering functional interaction patterns in protein protein interactions networks

Discovering functional interaction patterns in Protein-Protein Interactions Networks

Authors:

Mehmet E Turnalp

Tolga Can

Presented By:

Sandeep Kumar

background
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
aim of study
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
yeast as a model
Saccharomyces cerevisiaeYeast as a model
  • Why yeast genomics? A model eukaryote organism …
  • Well known PPI network
slide5
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
slide6
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

example frequent subgraphs
Example: Frequent Subgraphs

GRAPH DATASET

(A)

(B)

(C)

FREQUENT PATTERNS

(MIN SUPPORT IS 2)

(1)

(2)

the algorithm ppispan
The Algorithm - PPISpan
  • Based on gSpan
  • Modified to adapt for PPI network
  • Candidate generation
  • Frequency counting
algorithm ppispan g l minsup
Algorithm: PPISpan (G, L, minSup)
  • Set the vertex labels in G with GO terms from the desired GO level L
  • S <- 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
algorithm subpgraphs s minsup ext
Algorithm: Subpgraphs (s, minSup, ext)
  • If (feasible (s, ext))
  • If DES code of s != to its minimum DFS code
  • return
  • C <- Generate all children of s (by growing an edge, ext)
  • Maximal <- true
  • For each c in C (in DFS lexicographical order) do
  • If support (c) >= minSup
  • Subgraphs (c, minSup, c.ext)
  • maximal <- false
  • If (maximal)
  • output s
datasets used
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

gene ontology annotations
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
go slim terms
GO slim terms

Provides a broad overview of the functional categories in GO

GO Slim Molecular Function Terms for S. Cerevisiae

Term ID Definition

GO:3674 molecular function unknown

GO:16787 hydrolase activity

GO:16740 transferase activity

GO:5515 protein binding

Total of 22 broad functional categories

research steps
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
problems faced
Problems faced
  • Noise in PPI network
  • False positives
  • False negatives
  • Accuracy and specificity of annotations of proteins
supporting embedding
Supporting embedding
  • Specific instance of the functional pattern realized by certain proteins in the PPI network
experiment details
Experiment details
  • Implemented in C++
  • Searched for frequent interaction patterns of support >= 15
observation
Observation
  • Most of the patterns are trees
  • Star topology most abundant
  • Cycles rare
comparison with known molecular complexes and pathways
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
cpcount
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
cpoverlap
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
observations from comparison
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
analysis of patterns with mips complexes
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
analysis of patterns with kegg pathways
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
some interesting functional interaction patterns
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
conclusions
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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