Khaled Monsoor's presentation on a paper on "Maximizing hidden stop codon on gene design

1. Synthetic gene design with a large number of hidden stops Authors: Phan, V., Saha, S., Pandey, A., Wong, T-Y Published in: Intl. Journal of Data Mining and Bioinformatics Vol. 4, No. 4, 2010 Presented by: Khaled Monsoor Bioinformatics Masters Program The University of Memphis Mail: kmonsoor@memphis.edu Date: Nov 05, 2010

3. Why ?

4. How ?

5. Result ?

6. Conclusion Synthetic gene design with a large number of hidden stops

8. Sleeping is waste of precious time Stay awake Like him …

10. Can we “redesign” genes to include more Hidden stops ?

12. Why ?

13. How ?

14. Result ?

15. Conclusion Synthetic gene design with a large number of hidden stops

18. Has evolved through millions of years

19. A protein is a sequence of amino acids

20. Contains 20(twenty) amino acids8

21. Universal Genetic Code

22. mRNA: ATGTCCAAACCT Protein: M S LP 10 Translation

23. Triplets representing P (Proline) 11 CCT, CCC, CCA, CCG all represent P (Proline) A mutation in the 3rd positions does not change the amino acid

24. Deletion/Insertion is dangerous Deletion creates frame shifts, which change entire subsequence content RNA: ….. CAT.CAT.CAT.CAT …. Protein: …HHHH… (chain of Histidine) Deletion of 3rd character (T): CAC.ATC.ATC.AT Protein: HII ... Totally bizarre something else !!! 12

25. Like them … :-(

26. Regular Expression for a Protein (start) (codon)k (stop) Start – ATG Stop – TAA, TAG, TGA Codon– any triplet not equal to TAA, TAG, orTGA Example: ATG.ACC.AAT.CGG.TAA 14 Stop codon (but hidden)

27. Why a hidden stop is good ? Hidden stops can protect against frame shifts by terminating consequence translation early Without hidden stops, frame shifts can cause very long non-functional proteins, resulting to not only waste of time, amino acid resources (money), ATP (energy) but also produce some deadly toxin Ref: Seligmann and Pollock, DNA and Cell Biology, 2004 15

29. Why ?

30. How ?

31. Result ?

32. Conclusion Synthetic gene design with a large number of hidden stops

34. Constraints: None, by matching GC content, and by matching codon usage 17

35. Example: protein is MSDSKED 18

36. Hidden Stops Consider this protein is MSDSKED Both sequences encode for this protein: ATG.AGT.GAT.AGT.AAA.GAA.GAC.TAA ATG.TCC.GAT.TCG.AAA.GAA.GAC.TAA Sequence (1) is better! It has 4 hidden stops! 19

38. Dynamic Programming approach Idea: Optimal design of whole sequence is based on optimal design of partial sequences H(i, j) = optimal design up to ith amino acid, Ai, which is coded by its jthcodon 21

39. Optimal Substructure of algorithm This formula can be computed recursively (in linear time, O(n)) H(i, j) = maxk { H(i-1, k) + Ikj } Maximizing over all k codons coding the previous amino acid, Ai-1 Ikj = 1 if the kth codon of Ai-1 and jth codon of Ai is a stop codon 22

40. Strategy: Back Translation Protein  DNA This is a 1-to-many mapping Back translation should: Satisfy constraints imposed by host genomes, Serve specific design purpose 23

41. Main algorithm (2 parts)

42. Constrained by GC Content GC content = number of G & C in sequence GC content relates to the stability of DNA Algorithm’s objectives: maximizenumber of hidden stops, then, matchGC content of host genome 25

43. Algorithm considering GC content Constraint and “Fitting” approach

46. Still “better” than wild-type genes27

47. For a particular amino acid, triplets are not distributed uniformly 28 For Leucine, codon CUG is used 51% in E. Coli.

48. Algorithm considering Codon Usage Constraints

50. Why ?

51. How ?

52. Result ?

53. Conclusion Synthetic gene design with a large number of hidden stops

54. Comparison “Wild type” (genes from NCBI) Random gene (constrained by Codon usage of “wild type” “Optimal” – design with no constraint (max stop codon) Constrained by GC content of wild type Constrained by Codon usage of wild type 31

55. Genes for re-design study . . .

56. Overall comparison of all approaches Number of hidden stop codon

58. Why ?

59. How ?

60. Result ?

62. Any question ? As a lagging grad student, I’ll try my best to answer …

63. Thank you for attending his boring presentation … oh