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Similarity between viral epitopes and human epitopes

Similarity between viral epitopes and human epitopes. Shay Sade Eldad Mataraso Project advisor: Prof. Yoram Louzoun Final Project . Biological background …. About 98% of thymocytes die during the development processes in the thymus by failing either positive

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Similarity between viral epitopes and human epitopes

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  1. Similarity between viral epitopes and human epitopes Shay Sade EldadMataraso Project advisor: Prof. YoramLouzoun Final Project

  2. Biological background … • About 98% of thymocytes die during the development processes in the thymus by failing either positive selection or negative selection.

  3. Biological background… Positive selection • Positive selection "selects for" T-cells capable of interacting with MHC. • Only those thymocytes that bind the MHC/antigen complex with adequate affinity will receive a vital "survival signal." • The implication of this binding is that all T cells must be able to recognize self antigens to a certain degree.

  4. Biological background… Positive selection • The thymocytes with no affinity for self antigens die by apoptosis and are engulfed by macrophages. • This process does not remove thymocytes that may cause autoimmunity. • The potentially autoimmune cells are removed by the process of negative selection

  5. Biological background… Positive selection

  6. Biological background… Negative selection • Negative selection removes thymocytes that are capable of strongly binding with "self" peptides presented by MHC. • They are again presented with self-antigen in complex with MHC molecules on antigen-presenting cells (APCs) such as dendritic cells and macrophages.

  7. Biological background… Negative selection • Thymocytes that interact too strongly with the antigen receive an apoptotic signal that leads to cell death. • The vast majority of all thymocytes end up dying during this process.

  8. Biological background… Negative selection

  9. Our research purpose • Our main purpose is to check whether viruses have evolved to remove epitopes in order to avoid Cytotoxic T cell induced cell destruction. • We will check this possibility by comparing the similarity of viral to human epitopes in human and non-human viruses.

  10. significance of similarity between virus and self • Autoimmunity: The similarity of epitopes between viruses and their host may be a reason for some autoimmune reactions. The similarity between hosts and antigens amino acid sequences may result in the host immune system attacking the organs expressing the self antigens that are similar to the viral antigens, therefore inducing autoimmune reactions.

  11. significance of similarity between virus and self • EscapeOne of the factors that can possibly affect the immune recognition and help viruses to escape the immune system is the similarity to self. This mechanism can allow viruses to survive undetected by the immune system. Tolerance can be obtained since the immune system does not recognize self antigens (beside perhaps some cases of autoimmune diseases).

  12. Statistical comparisons A. Comparison between the following results: • The data resulted from running the algorithm with epitopesfrom human host viruses on the human genome. • The data resulted from running the algorithm with non human viral epitopes on the human genome.

  13. Statistical comparisons B. Comparison between the following results: • The data resulting from running the algorithm with random viral segments of 8–10 amino acids on the human genome.(not done yet) • The data resulting from running the algorithm with real viral epitopes on the human genome.

  14. Peptibase • The Peptibase server was developed by our lab and is used to predict epitopes within AA sequences. • The analysis performed in Peptibase is conducted on the 31 most frequent HLA alleles, taking into account the HLA allele frequency in the human population. • Given an AA sequence, Peptibase uses 3 cut-offs on a 9-mer AA sliding window to predict its epitopes: • Cleavage by the Proteasome • Binding to TAP • Binding to MHC-I • For each 9-mer, cleavage, TAP and MHC-I binding scores are computed. • 9-mers passing all three stages are defined as epitopes.

  15. The algorithm backround • The problem of finding k similar charachters between given sequences is a known problem in the field of sequence comparisons. A ADDDDSSGGG 6/11 ABDCCDSKGHG

  16. The algorithm backround Naïve solution:Divided the human genome to its all possible sequences in the length of 9 amino acids .(we can also divide it into 8 or 10 amino acids). we wish to find k resemblance between our human nines and a given viral nine (we also divide the virus into nines).

  17. The algorithm backround Human genome HFDDSDSSDFFGHHYDSSDDFFDSSDFDSAAY… Virus genome FGFDFGFDFGFGDDDDDHHHHHDDDDDDS…

  18. The algorithm backround Naïve solution: This run we will keep only the sequences that have 5 or more "hits“.

  19. The algorithm backround Human genomeAAABBNMGGFDCVGFFDDSXDCCFFCCDDFGG HFDSSASTSFAGHHYDSSDDFFDSSDFDSAAY… Virus ninmers compare: result: 5/9 save SHAAASFAG SAGAYSOG…. SSASTSFAG

  20. The algorithm backround Naïve solution – complexity : run of all given viral nines. if the number of nines in the virus is 'm' and the number of nines in human is 'n' the complexity of this algorithm will be : O(m*n), and for one given nine O(9n)~O(n).

  21. The algorithm that we developed • Our algorithm has two parts a. construction of a library from the human genome. b. search for the nearest ninemer in our library to the given virus ninemer.

  22. The algorithm that we developed Construction the library run over the human genome in a sliding window form by alleles and save all of the human ninemers Genome Genomeninemers ABCDEFGHIJKLM… ABCDEFGHI BCDEFGHIJ CDEFGHIJK . .

  23. The algorithm that we developed • each ninemer is separated to nine fourmers in a round sliding window form: EEERTHFFG EEER EERT ERTH RTHF THFF HFFG FFGE FGEE GEEE Each fourmer belongs to this given ninemer

  24. The algorithm that we developed • Why do we need fourmers? • We will use a function F in order to turn each former in to a unique number so that different fourmers will receive a different numbers. • Every number represents a file name. • All ninemers which, share an identical number will be saved in a file named after the shared fourmer.

  25. The algorithm that we developed • All of the 20 amino acids were numbered from 0 to 19. • The Function Calculation:why 160000? • for example F(‘aaac’) = 2 • the ninemer will be saved in maximum 9 files. • In total we have (20^4) files .

  26. The algorithm that we developed • Each file contains :human index of the ninemer The pos of the fourmer (0,1…8). • For example: the file represented by the fourmer AGHJ will look as fallowing: file Name: AGBB AVGAGBBIG 3 AGBBAAGGG 0

  27. The algorithm that we developed • Our algorithm has two parts a. construction of a library from the human genome. b. search for the nearest ninemer in our library to the given virus ninemer.

  28. Search • cut the virus ninemer in to nine fourmers (as a) • Example: Virus: ninemer: aaabbcdeg Fst former – inx 1: aabb Search 77 files: 76 files of neighbors (3/4) + 1 identical (4/4) Which files? • F(aaab) = 0+0+20+1=21 Do the Same formula over 76 neighbors files: F(?abb)=…19 inx F(a?bb)=…19 inx F(aa?b)=…19 inx F(aab?)=…19 inx

  29. corectness memory(20^4)*2=160,000 files in each file 11,300,000*9\160000=635 ninemers Timeevery fourmer has 4*19=76 neighbors. Every ninmer has 9 fourmer.Total : 4*19*9 +9 = 693 files.

  30. Analysis of the Results

  31. Analysis of the Results • Red - Non Human • Blue - Human

  32. Analysis of the Results Average of Human Papilloma viruses VS Average of non Human Papilloma viruses

  33. Analysis of the Results

  34. Analysis of the Results Similarity between Human Hepatitis viruses VS Non human Hepatitis viruses • Red - Non Human • Blue - Human

  35. Conclusions • It’s seems that non human viruses are more similar to self in 5/9 and 6/9 similarity which is relatively small similarity • While in 7/9 similarity and more their is a tendency for higher similarity in human host viruses. • This can be supported biologically by our assumption that • human viruses evolved to remove epitopes in order to avoid • Cytotoxic T cell induced cell destruction • The results are more clear and tend to support out conclusions in the Papilloama viruses than in the Hepatitis viruses . This maybe explained by the size of the dataset that we • worked on in each virus type(bigger in papilloma).

  36. Future Goals • This days we are waiting for the results of HIV and Herpes viruses human and non human both. • After we will finish gathering more viruses results that hopefully will confirm our current conclusions.

  37. Thanks to.. • Prof. Yoram Louzoun • Royi Itzhak and all Yoram’s lab members • Ariel Azia Amitai

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