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..