Hon Nian Chua, Wing-Kin Sung and Limsoon Wong

1. Exploiting indirect neighbors and topological weight to predict protein function from protein–protein interactions Hon Nian Chua,Wing-Kin Sung and Limsoon Wong

2. Motivation Predicting the protein function from Protein-protein interaction data. • Previous studies considers level 1 neighbors • Can level-2 neighbors play an significant role in this prediction?

3. Summarizing the output of the study • level-2 neighbors does show functional association. • A significant no. Proteins were observed to be having associations with level-2 neighbors but not with level-1 neighbors. • A predicting algorithm: • 1) weight Level 1 & 2 neighbors based on functional similarity. • 2) each function was also allotted a score based on its weighted frequency in neighbors

4. Conventional approaches • using only direct interactions i.e level-1 neighbors • Consider a radius in the interaction neighborhood network • Calculate a functional distance and use clustering to make some functional classes.

5. Protein-Protein interactions as an undirected graph • G=(V,E) • (u, v) as two protein nodes • And edge e between them as interaction • U and v being , K-level neighbors– concept of path with k-edges between u and v. • Set of neighbors-- Sk

6. Indirect Functional Association

7. Significance • out of 4162 annotated proteins, only 1999 or 48% share some function with level-1 neighbors.

8. Sets of neighborhood pairs

9. Simple neighbor counting • Discuss– M and N • M- total predicted N-total functions known

10. The Algorithm • 1) Functional similarity Weight Previous approaches use CD-distance between proteins u and v given by

11. A simple example

12. When a fraction ‘x’ of protein’s ‘u’s neighbors is common to protein ‘v’s neighbors then x is proportional to the probability that u’s functions are shared with v through common neighbors. (and vice versa for y protion of v ‘s neighbor common with neighbor of u)

13. 2) integrating reliability of experimental sources: The prediction results can be improved by taking differences in reliability of sources into account. So between u and v , the reliability of the interaction is estimated as: • i source no. Euv set of sources with interaction u, v n no . Of times in which interaction btween u and v was observed

14. So, integrated equation becomes

15. Transitive functional Association • If u is similar to w and w is similar to v then there can be a similarity between u and v given by:

16. Functional Similarity Weighted Averaging • the likelihood of protein p having function x: • STR(u,v)  Transitive FS weight • r_int  fraction of all the proteins who share this considered function • Sigma(p,x) = 1 if p has function x else =0 • Pi_x frequency of function x in proteins

17. Results • 1) ORIGINAL NEIGHBOR COUNTING • 2) Neighbor counting with FS-weight • 3) scheme in (2)+ level-2 neighbors are considered.

18. Comparison with other schemes

19. Improvements? • Threshold at level-2..