1. Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using technique Z then X is better than Y w.r.t. Z Motifs could be functional sites in proteins or functional regions in non- coding DNA

2. Protein Functional Site Prediction The identification of protein regions responsible for stability and function is an especially important post-genomic problem With the explosion of genomic data from recent sequencing efforts, protein functional site prediction from only sequence is an increasingly important bioinformatic endeavor.

3. What is a “Functional Site”? Defining what constitutes a “functional site” is not trivial Residues that include and cluster around known functionality are clear candidates for functional sites We define a functional site as catalytic residues, binding sites, and regions that clustering around them.

4. Protein

5. Protein + Ligand

6. Functional Sites (FS)

7. Regions that Cluster Around FS

8. Phylogenetic motifs PMs are short sequence fragments that conserve the overall familial phylogeny Are they functional? How do we detect them?

9. Phylogenetic motifs PMs are short sequence fragments that conserve the overall familial phylogeny Are they functional? How do we detect them? First we design a simple heuristic to find them Then we see if the detected sites are functional

10. Scan for Similar Trees Whole Tree

11. Scan for Similar Trees Whole Tree

12. Scan for Similar Trees Windowed Tree Whole Tree

13. Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

14. Scan for Similar Trees Partition Metric Score: 8 Windowed Tree Whole Tree

15. Scan for Similar Trees Partition Metric Score: 4 Windowed Tree Whole Tree

16. Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

17. Scan for Similar Trees Partition Metric Score: 8 Windowed Tree Whole Tree

18. Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

19. Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

20. Scan for Similar Trees Partition Metric Score: 0 Windowed Tree Whole Tree

21. Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

22. Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

23. Scan for Similar Trees Partition Metric Score: 8 Windowed Tree Whole Tree

24. Scan for Similar Trees Partition Metric Score: 0 Windowed Tree Whole Tree

25. Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

26. Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

27. Scan for Similar Trees Partition Metric Score: 6 Windowed Tree Whole Tree

28. Phylogenetic Motif Identification Compare all windowed trees with whole tree and keep track of the partition metric scores Normalize all partition metric scores by calculating z-scores Call these normalized scores Phylogenetic Similarity Z-scores (PSZ) Set a PSZ threshold for identifying windows that represent phylogenetic motifs

29. Set PSZ Threshold

30. Regions of PMs

31. Map PMs to the Structure

32. Set PSZ Threshold

33. Map PMs to the Structure Map Set PSZ Threshold

34. Map PMs to the Structure Map Set PSZ Threshold

35. PMs in Various Structures

36. PMs and Traditional Motifs

37. TIM Phylogenetic Similarity False Positive Expectation

38. TIM Phylogenetic Similarity False Positive Expectation

39. TIM Phylogenetic Similarity False Positive Expectation

40. TIM Phylogenetic Similarity False Positive Expectation

41. Cytochrome P450 Phylogenetic Similarity False Positive Expectation

42. Cytochrome P450 Phylogenetic Similarity False Positive Expectation

43. Enolase Phylogenetic Similarity False Positive Expectation

44. Glycerol Kinase Phylogenetic Similarity False Positive Expectation

45. Glycerol Kinase Phylogenetic Similarity False Positive Expectation

46. Myoglobin Phylogenetic Similarity False Positive Expectation

47. Myoglobin Phylogenetic Similarity False Positive Expectation

48. Evaluating alignments For a given alignment compute the PMs Determine the number of functional PMs Those identifying more functional PMs will be classified as better alignments

49. Protein datasets

50. Running time

51. Functional PMs PAl=blue MUSCLE=red Both=green (a)=enolase, (b)ammonia channel, (c)=tri-isomerase, (d)=permease, (e)=cytochrome