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

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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 cerevisiae

Yeast as a model

  • Why yeast genomics? A model eukaryote organism …

  • Well known PPI network


Discovering functional interaction patterns in protein protein interactions networks

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


Discovering functional interaction patterns in protein protein interactions networks

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:3674molecular 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


Pattern frequency in different datasets

Pattern frequency in different datasets

Number of patterns found


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


Questions

QUESTIONS ?


Thank you

THANK YOU


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