Immunological Bioinformatics 2011 Exam! ! ! Wednesday, 22 June 2011

11 Questions of 3 points each1 Exercise Question of 10 points 7 pages

Name:____________________________________________________________

Student number: ___________________

General questions (Q1-Q11)Q1:What is shown in the figure below (circle the correct answer)

MHC class I + ligand! MHC classII + ligand! Antibody + ligand!

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Q2:We use binding data to train our prediction methods.We have a training set consisting of 2223 peptides with corresponding biochemically measured affinities. 1325 of these have a measured binding affinity stronger than 500 nM(i.e., the IC50 value is below) 500. We will train Artificial Neural Networks to predict the binding affinity. For this we set op a 5-fold cross-validation training scheme. Why do we do this?

DatabasesWe want to check if the following peptide that is residing in an HIV protein is an epitope:

SLYNTVATL

Use the IEDB database (not predictors) we have been using, to verify if this peptide have been shown to be/be a part of an epitope (substring). It might not!If found write the full found epitope sequence and in case of T cell epitopes write the specific HLA allele(s).

Q3:

Class I/CTL epitope:

ClassII/Helper epitope:

Linear Bcell epitope:

Discontinuous (Non_peptidic) Bcell epitope:

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Supertypes, logos and allele frequenciesQ4:Given the following data:

Peptide A is a CTL epitope restricted to HLA-A*01:01Peptide B is a CTL epitope restricted to HLA-B*35:01

Which of the logos below matches the binding motif of the above alleles.

A:! ! ! ! B:! ! ! ! C:

! !

D:! ! ! ! E:! ! ! ! F:

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Q5:Given the standard supertype definitions shown below, and the following allele frequencies, how large a part of the given population does the two epitopes from Q4 cover. Assuming no linkage between A and B

A*01:01! 0.202 B*07:02 0.173A*02:01 0.274 B*15:01 0.038A*11:01 0.080 B*35:01 0.055

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PredictionsWe have done some immunological predictions using this Hemagglutinin protein sequence originating from a Danish influenza patient:

>gi|295842673|gb|ADG42303.1| hemagglutinin [Influenza A virus (A/Odense/INS143/2009(H1N1))]MKAILVVLLYTFATANADTLCIGYHANNSTDTVDTVLEKNVTVTHSVNLLEDKHNGKLCKLKGVAPLHLGKCNIAGWILGNPECESLSTASSWSYIVETSSSDNGTCYPGDFIDYEELREQLSSVSSFERFEIFPKTSSWPNHDSNKGVTAACPHAGAKSFYKNLIWLVKKGNSYPKLSKSYINDKGKEVLVLWGIHHPSTSADQQSLYQNADAYVFVGTSRYSKKFKPEIAIRPKVRDQEGRMNYYWTLVEPGDKITFEATGNLVVPRYAFAMERNAGSGIVISDTPVHDCNTTCQTPKGAINTSLPFQNIHPITIGKCPKYVKSTKLRLATGLRNVPSIQSRGLFGAIAGFIEGGWTGMVDGWYGYHHQNEQGSGYAADLKSTQNAIDEITNKVNSVIEKMNTQFTAVGKEFNHLEKRIENLNKKVDDGFLDIWTYNAELLVLLENERTLDYHDSNVKNLYEKVRSQLKNNAKEIGNGCFEFYHKCDNTCMESVKNGTYDYPKYSEEAKLNREEIDGVKLESTRIYQILAIYSTVASSLVLVVSLGAISFWMCSNGSLQCRICI

Q6:We have predicted potential 15mer T helper epitopes for DRB1*03:01, DRB1*13:01, and  DRB1*13:02 using NetMHCIIpan(see output here)

What is the rank score of the three best binding peptide for each allele.

Q7Using allelefrequencies.net and the Q6 predictions what is the frequency in Denmark of the top ranking DRB1*13:02 peptide?

Q8:We have predicted the affinity of HLA-A*24:02, HLA-A*11:01 and HLA-B*35:01 binding of 10mers using NetMHCpan.(See output ) here

Which of the the three alleles have the best averaged rank score of the three best binding peptides.

Q9:Which of the above two types of peptides (HLA-A/B or HLA-DRB1 binding) should also be tested for for correct proteasomal cleavage and TAP binding.

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Q10:Look at the output from CPHModels when submitting the Hemagglutinin sequence. The output is linked here.

A) Which PDB entry is the best hit: PBD: Chain:

B) Is this likely to be a close homolog? Yes No (Circle the correct)

C) How many residues in the aligned area differs between your template and your model?

Q11:We have submitted the model to DiscoTope and the result is linked here.

We have also submitted the template structure which DiscoTope output is linked here.

A) Which of the two structures have the most predicted B cell epitopes?

Template! ! ! CPHmodel model (circle the correct answer)

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Exercise question (10 points)

We have chosen the following cell surface protein as a potential TB (Mycobacterium tuberculosis) vaccine candidate.

>gi|15610946|ref|NP_218327.1| exported repetitive protein precursor PirG (cell surface protein) (EXP53) [Mycobacterium tuberculosis H37Rv]MPNRRRRKLSTAMSAVAALAVASPCAYFLVYESTETTERPEHHEFKQAAVLTDLPGELMSALSQGLSQFG 70INIPPVPSLTGSGDASTGLTGPGLTSPGLTSPGLTSPGLTDPALTSPGLTPTLPGSLAAPGTTLAPTPGV 140GANPALTNPALTSPTGATPGLTSPTGLDPALGGANEIPITTPVGLDPGADGTYPILGDPTLGTIPSSPAT 210TSTGGGGLVNDVMQVANELGASQAIDLLKGVLMPSIMQAVQNGGAAAPAASPPVPPIPAAAAVPPTDPIT 280VPVA We have prepared NetCTLpan, NetMHCIIpan, predictions using the 12 supertype representatives.Bepipred output is also prepared.

The outputs are linked to the respective server names above.

Which residue(s) are most often predicted to be involved in any kind of epitope.Calculated like this:

Number of alleles for which the residue is in a CTL epitope +Number of alleles for which the residue is in a T helper epitope core (identical cores are counted only once pr. allele) +1 if predicted by BepiPred.

(use position number(s) using the above sequence numbering as reference)

THE END!

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