Functional prediction in proteins (purifying and positive selection)

1. Functional prediction in proteins (purifying and positive selection)

2. 1. Introduction: evolution & sequence analysis

3. Darwin – the theory of natural selection • Adaptive evolution: Favorable traits will become more frequent in the population

4. Adaptive evolution • When natural selection favors a single allele and therefore allele frequency continuously shifts in one direction

5. Kimura – the theory of neutral evolution • Neutral evolution: Most molecular changes have no effect on the phenotype (neutral) Selection operates to preserve a trait (no change)

6. Purifying Selection • Stabilizes a trait in a population:Small babies  more illnessLarge babies  more difficult birth… • Baby weight is stabilized round 3-4 Kg

7. Purifying selection(conservation) -the molecular level • Histone 3

8. Synonymous vs. non-synonymous substitutions Non-synonymous substitution: GUUGCU Synonymous substitution: GUUGUC Purifying selection: excess of synonymous substitutions relative to non-synonymous substitutions

9. Synonymous vs. non-synonymous substitutions Histone 3 Non-syn. Syn.

10. Conservation as a means of predicting function Infer the rate of evolution at each site

11. Conservation as a means of predicting function Low rate of evolution  constraints on the site to prevent disruption of function/structure: active sites, protein-protein interactions, protein core etc.

12. Which site is more conserved?

13. Use phylogenetic information

14. ConSurf/ConSeq web servers:Prediction of conserved residues by estimating evolutionary rates at each site

15. Find homologous protein sequences (psi-blast) Perform multiple sequence alignment (removing doubles) Construct an evolutionary tree Project the results on the 3D structure Calculate the conservation score for each site Working process Input a protein with a known 3D structure (PDB ID or file provided by the user)

16. ConSurf example: potassium channel • An integral membrane protein with sequence similarity to all known K+ channels, particularly in the pore region. • PDB ID: 1bl8 chain A

17. ConSurf results

18. http://conseq.bioinfo.tau.ac.il/ • ConSeq performs the same analysis as ConSurf but presents the results on the sequence. • Predicts buried/exposed relation • exposed & conserved  functionally important sites • buried & conserved  structurally important sites

19. 2. Positive selection & drug resistance

20. Darwin – the theory of natural selection • Adaptive evolution: Favorable traits will become more frequent in the population

21. Adaptive evolution at the molecular level ?

22. Adaptive evolution at the molecular level Look for changes which confer an advantage

23. Naïve detection • Observe a multiple sequence alignment:variable regions = adaptive evolution??

24. Naïve detection X • The problem – how do we know which sites are not under any selection pressure (“non-important” sites) and which are under adaptive evolution?

25. Solution – we look at the DNA synonymous non-synonymous

26. Solution – we look at the DNA Adaptive evolution = Positive selectionNon-syn > Syn Purifying selectionSyn > Non-syn NeutralselectionSyn = Non-syn

27. Also known as… Ka/Ks (or dn/ds, or ω) ratio • Purifying selection: Ka < Ks (Ka/Ks <1) • Neutral selection: Ka = Ks (Ka/Ks = 1) • Positive selection: Ka > Ks (Ka/Ks >1) Ka Ks Non-synonymous substitution rate Synonymous substitution rate

28. Examples for positive selection • Proteins involved in the immune system • Proteins involved in host-pathogen interaction (‘arms-race’) • Proteins following gene duplication • Proteins involved in reproduction systems

29. Synonymous vs. non-synonymous substitutions Accumulation of substitutions (syn. or non-syn.) depends on the evolutionary time that elapsed since the divergence of the analyzed species. When distant species are analyzed saturation of syn. substitutions is often encountered

30. Selecton – a server for the detection of purifying and positive selection http://selecton.bioinfo.tau.ac.il Stern et al., Nucleic Acids Res 35, W506 (2007).

31. Detecting drug resistance using Selecton

32. HIV: molecular evolution paradigm Rapidly evolving virus: • High mutation rate (low fidelity of reverse transcriptase) • High replication rate

33. Drug resistance No drug Drug Adaptive evolution (positive selection)

34. HIV Protease Protease is an essential enzyme for viral replication Drugs against Protease are always part of the “cocktail”

35. Ritonavir Inhibitor • Ritonavir (RTV) is a specific protease inhibitor (drug) C37H48N6O5S2

36. Used Selecton to analyse HIV-1 protease gene sequences from patients that were treated with RTV only

38. Example: HIV Protease • Primary mutations • Secondary mutations •  novel predictions (experimental validation)

39. Rate shifts and HIV sub-types

40. Rate shifts V Human V Chimp V Rhesus A Squirrel K Rat M Mouse

41. V V V A K M Rate shifts Low evolutionary rate High evolutionary rate

42. V Human V Chimp V Rhesus A Squirrel A Rat A Mouse Rate shifts Specificity determinants: • Different phylogenetic groups Gain of function?

43. V S. cervisiae V S. paradoxus V S. mikatae A S. cervisiae A S. paradoxus A S. mikatae Rate shifts Specificity determinants: • Following gene duplication Tropomyosin 1 Tropomyosin 2

44. Rate shifts in HIV subtypes

45. HIV subtypes

46. Which sites are responsible for the differences between the subtypes? • Detection of rate-shifts in all 9 subtypes

47. Significant rate shift in all HIV genes

48. Gag Position12 • Wild-type (E) • Site which contributes to Protease Inhibitor (Amprenavir) drug resistance (K)

49. G K E Q N R C A J F C D

50. Summary • Sequence analysis can provide valuable information about protein function • The basic signal: conservation: http://consurf.tau.ac.il • Positive “Darwinian” selection: http://selecton.bioinfo.tau.ac.il • Rate-shifts (specificity determinants)