1. Predicting Protein Function Annotation using Protein- Protein Interaction Networks By Tamar Eldad Advisor: Dr. Yanay Ofran 89-385 Computational Biology - Projects Workshop Bar-Ilan University, the Mina and Everard Goodman Faculty of Life Sciences 1

2.  Exponential increase in the number of proteins being identified by sequence genomics projects  Impossible to perform functional assay for every uncharacterized gene  Turn to sophisticated computational methods for assistance in annotating the huge volume of sequence and structure data being produced  homology-based annotation transfer  sequence patterns  structure similarity  structure patterns  genomic context  microarray data Protein Function Prediction 2

3.  Biological function has more than one aspect  Sub-cellular to whole-organism context  Physiological aspect  Phenotype What is Function? The need of a well-defined vocabulary 3

4. Protein Sequence: Protein Structure: 4

5. EC – Enzyme Commission Classification EC4.1 carbon-carbon EC4.1.1 carboxy layses EC4.1.2 aldehyde layses 4.1.1.1 pyruvate decarboxylase 4.1.1.2 oxolate decarboxylase ……. 5

6. The Gene Ontology project is a major bioinformatics initiative with the aim of standardizing the representation of gene and gene product attributes across species and databases. The project provides a controlled vocabulary of terms for describing gene product characteristics and gene product annotation data. The Gene Ontology 6

7. Cellular component Molecular function Biological process DAG (1….N parent nodes) General  Specific Term is assigned to Gene Product 7

8. The Gene Ontology 8

9. Classical Biology – collect a set of features for each protein Systems Biology – study protein function in the context of a network A New Approach Assemblies represent more than the sum of their parts 9

10. Protein Interactions Data on thousands of interactions in humans and most model species have become available mass spectrometry genome-wide chromatin immunoprecipitation yeast two-hybrid assays combinatorial reverse genetic screens rapid literature mining techniques 10

11. PPI Networks Data are represented as networks, with nodes representing proteins and edges representing the detected PPIs. 11

12.  Alignment – aligning sequence-matching proteins between species and checking if they also share network alignment can teach us about conserved pathways between species  Integration - data from different types of networks (i.e. protein, genetic, and transcriptional interaction networks) are integrated in order to get a better picture of the whole biological system  Querying - find sub-networks similar to functional units (by comparing interactions and the proteins themselves) - likely to be functioning units too Existing Methods 12

13. conserved network motifs between two species convey evidence for function similarity of the individual proteins that make up these motifs New Method HUMAN YEAST 2e-10 8e-13 1e-09 5e-15 13

14. What do we need? 1. list of proteins in human cell 2. list of proteins in yeast cell 3. interactions in each cell 4. sequence similarity grades 5. known GO annotations 6. function distance calculation New Method 14

15. Protein Lists - UniProt DB 15

16. Interaction Databases HPRD - The Human Protein Reference Database. Dip - Database of Interacting Proteins. Mips -Munich information center of proteins sequences IntAct – interaction molecular database. Reliable interaction performs one of these conditions: 1. was at least observed in 2 different experiments. OR 2. was reported in 3 different articles. 16

17. Sequence Similarity Grades BLAST - bl2seq 1234 1-0.0083e-18X 210-0.023.6 HUMAN YEAST 17

18. GO annotations – UniProt DB 18

19. Evidence Codes 19

20. Function Distance Calculation 20

21. 1. Prepare similarity matrix for cutoff e-value 2. Find all components of size N – 1 (DFS search) 3. Compare sub-graphs found using similarity matrix 4. Add N-th non-similar component to each pair of matching graphs 5. Get GO function annotation of N-th components 6. Calculate average distance of N-th component’s function Implementation 21

22. 1.Compare to random-pair annotation No-sequence similarity 2.Compare to sequence-similar annotation BLAST Only proteins under cut-off value Human genes only Quality Assurance 22

23. Detailed Results Eval averagedistterm typego funcnew compgraph2go func new compgraph1 0.0794MolecularFunctionGO:0005515Q9UHD2,4253,1335,2447,2353,GO:0005515Q12495,4814,4256,591,1584, 0.0793MolecularFunctionGO:0030528Q9UHD2,4253,1335,2447,2353,GO:0030528Q12495,4814,4256,591,1584, 0.0790BiologicalProcessGO:0006334Q9UHD2,4253,1335,2447,2353,GO:0006334Q12495,4814,4256,591,1584, 0.0791MolecularFunctionGO:0005515O15111,4253,1335,2447,2353,GO:0005515Q12495,4814,4256,591,1584, 0.07912MolecularFunctionGO:0005515O15111,4253,1335,2447,2353,GO:0005515Q12495,4814,4256,591,1584, 0.0621BiologicalProcessGO:0016584P55060,4354,2303,2890,3693,GO:0016584P16649,4819,2,236,234, 0.0621MolecularFunctionGO:0016565Q96KB5,4354,2303,2890,3693,GO:0016565P16649,4819,2,236,234, 0.0628BiologicalProcessGO:0016584Q15699,4354,2303,2890,3693,GO:0016584P16649,4819,2,236,234, 0.0625BiologicalProcessGO:0016584Q15699,4354,2303,2890,3693,GO:0016584P16649,4819,2,236,234, 0.0414CellularComponentGO:0000120P63279,4387,1383,1452,2289,GO:0000120P13393,4867,2966,168,1224, 0.0413CellularComponentGO:0000120P63279,4387,1383,1452,2289,GO:0000120P13393,4867,2966,168,1224, 0.0417CellularComponentGO:0000126P63279,4387,1383,1452,2289,GO:0000126P13393,4867,2966,168,1224, 23

24. Results E-value 5e-05 24

25. Change graph size Lower e-value Start with larger amount of connected components Use only graphs with higher connectivity Non-similar proteins can be any protein in the graph Different network topology Limit number of paired proteins Play with Parameters 25

26. Results 26

27. Conclusions Most results are random Significant improvement only for Biological Process prediction Still far behind Homology Based Transfer 27

28. Summary Functional annotation is one of the greatest challenges in the post-genomic era PPI data for functional annotation as a new approach for promoting this field Method tried out is unsuccessful Other Ideas: Find a more specific search pattern Start from best results – what specializes them? 28

29. References Friedberg,I. (2006) Automated function prediction: the genomic challenge. Brief. Bioinform. Accepted for publication Sharan R, Ulitsky I, Shamir R: Network-based prediction of protein function. Mol Syst Biol 2007, 3:88. Sharan R, Ideker T: Modeling cellular machinery through biological network comparison. Nature Biotechnology 24, 4: 427 - 433. http://www.geneontology.org/ http://www.chem.qmul.ac.uk/iubmb/enzyme/ 29

30. Thanks Advisor – Dr. Yanay Ofran Guys at the lab – Rotem, Vered, Sivan Roi Adadi & Omer Erel 30

31. Alignment

32. Querying

33. Integration

34. 1234 1-0.0083e-18X 210-0.023.6 E-value = 0.0005 TRUE FALSE TRUE HUMAN YEAST Similarity Matrix

35. Neighboring matrix 1234 1-TRUEFALSETRUE 2 -FALSE HUMAN CELL INTERACTIONS