Discovering functional interaction patterns in Protein-Protein Interactions Networks

1. Discovering functional interaction patterns in Protein-Protein Interactions Networks Authors: Mehmet E Turnalp Tolga Can Presented By: Sandeep Kumar

2. 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

3. 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

4. Saccharomyces cerevisiae Yeast as a model • Why yeast genomics? A model eukaryote organism … • Well known PPI network

5. 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

6. 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

7. Example: Frequent Subgraphs GRAPH DATASET (A) (B) (C) FREQUENT PATTERNS (MIN SUPPORT IS 2) (1) (2)

8. The Algorithm - PPISpan • Based on gSpan • Modified to adapt for PPI network • Candidate generation • Frequency counting

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. Problems faced • Noise in PPI network • False positives • False negatives • Accuracy and specificity of annotations of proteins

16. Supporting embedding • Specific instance of the functional pattern realized by certain proteins in the PPI network

17. Experiment details • Implemented in C++ • Searched for frequent interaction patterns of support >= 15

18. Pattern frequency in different datasets Number of patterns found

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

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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

28. QUESTIONS ?

29. THANK YOU