...Biological background

Similarity between viral epitopes and human epitopes Shay SadeEldad Mataraso

Project advisor: Prof. Yoram Louzoun

Final Project

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

Biological background

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.

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

Biological background Positive selection

Biological background Positive selectionNegative 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.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.

Biological background Negative selectionBiological background Negative selection

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.

Our research purpose

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.

significance of similarity between virus and self

Escape One 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).

significance of similarity between virus and selfA. Comparison between the following results:

The data resulted from running the algorithm with epitopes from human host viruses on the human genome.

The data resulted from running the algorithm with non human viral epitopes on the human genome.

Statistical comparisonsB. Comparison between the following results:

The data resulting from running the algorithm with random viral segments of 810 amino acids on the human genome.(not done yet)

The data resulting from running the algorithm with real viral epitopes on the human genome.

Statistical comparisonsPeptibaseThe 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 ProteasomeBinding to TAPBinding 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.

14The problem of finding k similar charachters between given sequences is a known problem in the field of sequence comparisons.

A A D D D D S S G G G 6/11A B D C C D S K G H G

The algorithm backroundNave 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).

The algorithm backroundHuman genome

HFDDSDSSDFFGHHYDSSDDFFDSSDFDSAAY

Virus genome

FGFDFGFDFGFGDDDDDHHHHHDDDDDDS

The algorithm backround

Nave solution:

This run we will keep only the sequences that have 5 or more "hits. The algorithm backroundHuman genome

AAABBNMGGFDCVGFFDDSXDCCFFCCDDFGG

HFDSSASTSFAGHHYDSSDDFFDSSDFDSAAY

Virus ninmerscompare: result: 5/9 save

SHAAASFAGSAGAYSOG.

SSASTSFAGThe algorithm backroundNave 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). The algorithm backroundOur 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.

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 Genome ninemersABCDEFGHIJKLMABCDEFGHIBCDEFGHIJCDEFGHIJK..The algorithm that we developed each ninemer is separated to nine fourmers in a round sliding window form:EEERTHFFGEEEREERTERTHRTHFTHFFHFFGFFGEFGEEGEEEEach fourmer belongs to this given ninemer The algorithm that we developedWhy 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.

The algorithm that we developedAll 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 .

The algorithm that we developedEach file contains :human index of the ninemer The pos of the fourmer (0,18).

For example: the file represented by the fourmer AGHJ will look as fallowing:file Name: AGBBAVGAGBBIG 3AGBBAAGGG 0

The algorithm that we developedOur 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.

The algorithm that we developedcut the virus ninemer in to nine fourmers (as a)Example:

Virus: ninemer: a a a b b c d e g

Fst former inx 1: a a b b

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 inxF(a?bb)=19 inxF(aa?b)=19 inxF(aab?)=19 inx

Search

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. corectness

9 out of 98 out of 97 out of 96 out of 95 out of 9P-values0.2053840.0917810.1108290.0099710.010291Analysis of the Results

Analysis of the ResultsRed - Non HumanBlue - Human

Average of Human Papilloma viruses VS Average of non Human Papilloma virusesAnalysis of the Results32 Analysis of the Results Analysis of the Results

Similarity between Human Hepatitis viruses VS Non human Hepatitis virusesRed - Non HumanBlue - Human Conclusions Its 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).

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.

36 Thanks to.. Prof. Yoram Louzoun

Royi Itzhak and all Yorams lab members

Ariel Azia AmitaiChart10.00013059170.00047333760.00899438830.0193064980.41831416670.35117526.76586.1055875.31370.3648

Non Human virusesHuman virusessimilarityaverage of similar epitopes in virusesaverage of Human Papilloma virus similarity VS average of Non Human Papilloma virus

human_epsHuman similarityNon Human Similarity9 out of 98 out of 97 out of 96 out of 95 out of 99 out of 98 out of 97 out of 96 out of 95 out of 900.00182060.233265.367463.9230.0069680.00124010.294926.18773.2900.00475510.388586.405476.21100.01140.385146.770674.803000.310546.039368.77100.00466560.470017.556684.26900.00178270.460186.834279.80600.0189890.429226.810275.63500.000494340.127074.325957.51300.0482681.29399.168771.584000.197514.602554.55600.00601340.259916.761874.90400.0147310.282315.617568.19300.00139090.303816.693277.353000.461727.312584.804000.265575.722169.40300.0165460.298086.068867.71300.010171.11638.802872.60100.0400630.277015.187665.25200.00345510.492455.899374.244000.183026.318469.00600.000517070.319495.401163.17100.140580.711888.280383.01200.00823660.511087.769588.73000.372025.620474.84600.0165070.377976.85387800.027830.370065.422361.72100.00237560.392087.651281.7540.000189440.00494520.294176.047466.1600.0203340.62857.21877.82900.00199950.181224.514455.97900.000749430.424736.623674.018000.200694.874460.11400.00136410.241696.153776.522000.22745.860666.11700.00621180.29035.342665.16700.0214240.266626.84873.40400.0131820.353486.724276.82200.00632460.215724.631257.2910.00313420.0130080.392758.413389.389000.328144.862465.8700.000967420.562597.164183.0270.00202960.0123570.416366.45967.441000.209545.610767.42100.000204450.306066.848778.00500.0181730.570857.07177.58000.284326.27775.15900.0145180.238246.267872.78900.128560.928667.842981.92500.0224280.418357.490277.03700.00969780.324266.436269.46700.00761570.227215.883669.84900.00300250.38385.911771.88200.0103920.364066.909275.0930.009614400.758458.20385.22600.00812620.352226.963877.24100.00350750.176115.450566.56500.00654140.373776.822873.6200.00768290.288335.854172.59900.0296020.612516.927668.8529 out of 98 out of 97 out of 96 out of 95 out of 900.0432070.242975.890271.3690.00013059170.00899438830.41831416676.765875.313avergae0.00013059170.00899438830.41831416676.765875.3130.00047333760.0193064980.35117526.1055870.36480.00047333760.0193064980.35117526.1055870.3648averagep-values9 out of 98 out of 97 out of 96 out of 95 out of 90.20538402450.09178129010.11082851190.00997070940.0102910323

human_eps0000000000

Non Human virusesHuman virusessimilarityaverage of similar epitopes in virusesaverage of Human Papilloma virus similarity VS average of Non Human Papilloma virus