1 / 51

Protein Functional Site Prediction

Protein Functional Site Prediction. The identification of protein regions responsible for stability and function is an especially important post-genomic problem

Download Presentation

Protein Functional Site Prediction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. 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.

  2. 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.

  3. Protein

  4. Protein + Ligand

  5. Functional Sites (FS)

  6. Regions that Cluster Around FS

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

  8. 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

  9. Scan for Similar Trees Whole Tree

  10. Scan for Similar Trees Whole Tree

  11. Scan for Similar Trees Whole Tree Windowed Tree

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  27. 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

  28. Set PSZ Threshold

  29. Regions of PMs

  30. Map PMs to the Structure

  31. Map PMs to the Structure Set PSZ Threshold

  32. Map PMs to the Structure Map Set PSZ Threshold

  33. Map PMs to the Structure Map Set PSZ Threshold

  34. PMs in Various Structures

  35. PMs and Traditional Motifs

  36. TIM Phylogenetic Similarity False Positive Expectation

  37. TIM Phylogenetic Similarity False Positive Expectation

  38. TIM Phylogenetic Similarity False Positive Expectation

  39. TIM Phylogenetic Similarity False Positive Expectation

  40. Cytochrome P450 Phylogenetic Similarity False Positive Expectation

  41. Cytochrome P450 Phylogenetic Similarity False Positive Expectation

  42. Enolase Phylogenetic Similarity False Positive Expectation

  43. Glycerol Kinase Phylogenetic Similarity False Positive Expectation

  44. Glycerol Kinase Phylogenetic Similarity False Positive Expectation

  45. Myoglobin Phylogenetic Similarity False Positive Expectation

  46. Myoglobin Phylogenetic Similarity False Positive Expectation

  47. 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

  48. Protein datasets

  49. Running time

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

More Related