1. Inferring Functional Information from Domain co-evolution Yohan Kim, Mehmet Koyuturk, Umut Topkara, Ananth Grama and Shankar Subramaniam Gaurav Chadha Deepak Desore

2. Layout Motivation Computational Methods and Algorithms Results Conclusion Questions

3. Motivation (1 of 2..) Prior Work Focused on understanding Protein function at the level of entire protein sequences Assumption: Complete Sequence follows single evolutionary trajectory It is well known that a domain can exist in various contexts, which invalidates the above assumption for multi-domain protein sequences

4. Motivation (2 of 2..) Our approach Improvement of Multiple Profile method Constructs Co-evolutionary Matrix to assign phylogenetic similarity scores to each protein pair Identifies Co-evolving regions using residue- level conservation

5. Computational Methods & Algorithms Constructing phylogenetic profiles Protein(single) phylogenetic profiles Segment(Multiple) phylogenetic profiles Residue phylogenetic profiles Computing Co-evolutionary matrices Deriving phylogenetic similarity scores

6. Protein phylogenetic profiles Phylogenetic profile is a vector which tells about the existence of a protein in a genome. Let P = {P 1,P 2,…,P n } be the set of proteins and, G = {G 1,G 2,…,G m } be the set of Genomes Every row represents binary phylogenetic profile of a protein.

7. Protein phylogenetic profiles(contd.) Single phylogenetic profile ψ i for protein P i is, ψ i (j) = - 1,1 <= j <= m log(E ij ) where E ij is minimum BLAST E-value of local alignment between P i and G j Advantage: gives degree of sequence divergence

8. Protein phylogenetic profiles(contd.) Mutual Information I(X,Y) defined as, I(X,Y) = H(X) + H(Y) – H(X,Y), where H(X), Shannon Entropy of X is defined as, H(X) = ∑ p x * log(p x ), x Є X andp x = P[X = x] Phylogenetic similarity between ψ i (j) and ψ i (j) is, μ s (P i,P j ) = I(ψ i, ψ i )

9. Segment phylogenetic profiles Single profile based methods could miss significant interactions. Domain D 1 2 of P 2 follows evolutionary trajectory similar to P 1 and P 3 which single profile method didn’t capture.

10. Segment phylogen. profiles(contd.) Dividing each protein P i into fixed size segments S 1 i,S 2 i,…,S k i Phylogenetic similarity between two proteins, μ M (P i,P j ) = max I(ψ s i, ψ t j ), s,t where ψ s i is phylogenetic profile of segment S k i of protein P i

11. Residue phylogenetic profiles Problem with multiple phylogenetic profiles: Both domains covered together by the segment S 2 2, overriding their individual phylogenetic profiles. Significant local alignment between two proteins corresponds to the residues covered in the alignment rather than the whole sequences.

12. Residue phylog. profiles(contd.) A(P i,G j ) – set of significant local alignments between Protein P i and Genome G j T(A) = [r b,r e ] – interval of residues on P i corresponding to each alignment A Є A(P i,G j ) For each residue r on P i phylogenetic profile is ψ r i (j) = min - 1,1 <= j <= m A Є A r log(E(A)) A r = {A Є A(P i,G j ): r Є T(A)} is the set of local alignments that contain r

13. Computing co-evolutionary matrices For each protein pair P i and P j with lengths l i and l j, co-evolutionary matrix entry M ij (r,s) is, M ij (r,s) = I (ψ r i, ψ s j ), where1 <= r <= l i and 1 <= s <= l j The Co-evolutionary Matrix contains Information about which regions of the two proteins co- evolved The co-evolved domain(s) appear as a block of high mutual information scores in the matrix

14. Deriving phylogenetic similarity scores Phylogenetic similarity scores between two proteins P i and P j is, μ C (P i,P j ) = max minM ij (a,b) 1<= r <= l i r <= a <= r + W 1<= s <= l j s <= a <= s + W where W is the window parameter that quantifies the minimum size of the region on a protein to be considered as a conserved domain.

15. Results Implemented and tested on 4311 E.coli proteins 152 Genomes(131 Bacteria,17 Archaea,4 Eukaryota) Value of f (down-sampling factor) = 30, W = 2 These values translate in overlapping segments of 60 residue long Excluded homologous proteins from analysis Define p-value as fraction of non-homologous protein pairs (N)

16. Results (contd.) MIS – Mutual Information Score PP – No. of predicted protein pairs PPV = TP / (TP + FP) For all μ*, coverage = TP + FP TN and FN are the no. of protein pairs that do not meet the threshold

17. Results (contd.) Co-evolutionary matrix has 1.5 times greater coverage at PPV = 0.7 than the single profile method At same no. of PP, Co-evolutionary matrix has better PPV and sensitivity values than single profile method

18. Results (contd.) Mutual Information score distribution for interacting and non-interacting protein pairs At 0 MIS, SP shows a peak while CM doesn’t. In other ways, at low MIS scores, SP scores over CM

19. Results (contd.) Shows p-values of Single Profile method v/s Co-evolutionary Matrix method Scattered circles show that the two methods can predict very differently

20. Results (contd.) – Phosphotransferase system Domain IIA(residues 1-170) and domain IIB(residue 170-320) Darker region shows that the domains have co-evolved. So we can conclude that IIB evolved with IIC rather than IIA Top-20 predicted interacting partners of protein IIAB for both methods

21. Results (contd.) - Chemotaxis N-terminus of CheA(residues 1-200) and C-terminus of CheA(residues 540-670) co-evolved with C- terminus region of CheB (residues 170-340) Top-20 predicted interacting partners of protein CheA using both methods

22. Results (contd.) – Kdp System N-terminal domain of KdpD (residues 1-395) co-evolved with KdpC Top-10 predicted interacting partners of protein KdpD using both methods

23. Conclusion Results in this paper strongly suggest that co- evolution of proteins should be captured at the domain level Because domains with conflicting evolutionary histories can co-exist in a single protein sequence Regions that are important for supporting both functional and physical interactions between proteins can be detected

24. Questions Thank You !!