Download - Structural Immunoinformatics – two case studies

Structural Immunoinformatics – two case studies

M. Atanasova, I. Dimitrov, A. Patronov, I. Doytchinova

Medical University of Sofia Faculty of Pharmacy

Regional Conference in Supercomputing Applications in Science and Industry, 20-21 Sept. 2011, Sunny Beach, Bulgaria


Immunoinformastic Approaches:

Sequence - based Structure - based

peptide pIC50exp

ILDPFPVTV 8.654ALDPFPPTV 8.170VLDPFPITV 8.139................ .......FLDPFPATV 8.270

Affinity = f (Chemical Structure)Motif-based, QMs, ANN, SVM

Affinity = f (Interaction energy)Molecular docking

Molecular dynamics

Immunoinformatics (Computational Immunology, Theoretical Immunology)


Case study 1: T-cell epitope prediction of proteins from Boophilus microplus

Boophilusmicroplus tick.

Ticks are hematophagous parasites that feed on variety of domestic animals. B. microplus tick:

• a hard tick;• transmits lethal pathogens;•causes disease and death.

Collaborator: University of Pretoria, SA

Aim:to predict peptides originating from B. microplus and binding with high affinity to murine MHC class II proteins IAd and IEd.



Approaches used

Sequence - based

Structure - based

MHCPred and RANKPEP servers for MHC class II binding prediction

Molecular docking calculations



Selection of high immunogenic B. microplus proteins by VaxiJen server.

Presentation of the selected proteins as sets of overlapping peptides.

1.

2.

B. Microplus numberProtein peptides

Contig2828 59Contig7420 93CK181624 61

Selection of input X-ray structures of complexes of murine MHC II protein with a peptide.

Ova/IAd (pdb code: 2iad)HB/IEk (pdb code:1iea)

Homology modelingof IEk to IEd structure

3. Optimization of complexes of each peptide with each MHC II protein.

Dockingcalculations

biding site - 6Å; Chemscore – scoring function; fixed protein and peptide backbone; ranking – by score; GOLD v.5.0.2.

Workflow:



Binding affinity prediction to IAd by MHCPred and RANKPEP

MHCPred: Predicted binders with IC50 < 50 nMare highlighted in green.

RANKPEP: Predicted binders with binding threshold:

7.10 are highlighted in purple.



Binding affinity prediction to IAd and IEd by Molecular docking

IAd IEdThe top 2 best clusters of binders are highlighted in magenta.



Peptides selected for further experimental studies


Case study 2: Prediction of peptide binding to Swine Leukocyte Antigen (SLA-1)

Aim: to generate quatitative matrices (QMs) for prediction of peptides binding to SLA-1

IntestinalDiarrhea

RespiratoryCoughingSore Throat

PhyschologicalLethargyLack of appetite

Swine influenza symptoms

NasopharynxSneezingMucous: nose/eyesSystemicFeverWeight lossPoor growth

Swine Influenza in pigs:- An acute respiratory disease;- High morbidity depending on the immune status;- Can results in important economic losses.

CReSACentre de Recerca en Sanitat Animal

Modeled proteins:

SLA-1*0101 SLA-1*0401SLA-1*0501 SLA-1*1101

7 anchor positions X 19 aa = 133 + 1 original ligand = 134 peptides

biding site - 6Å; Chemscore – scoring function; fixed protein and ligand apart from the residues from the tested peptide position; ranking – by lowest RMS; GOLD v.5.0.2.

normalization of the binding energies and compilation into QMs.

P1 P2P3

P5

P6P7

P9


Workflow:

1. Homology modeling of SLA-1 from HLA*0201 (pdb:3pwj).

2. Construction of combinatorial library of peptides.

3. Molecular docking of peptides to SLA proteins.

4. Forming of docking score-based QMs (DS-QMs).



Workflow:

1. Homology modeling of SLA-1 from HLA*0201 (pdb:3pwj).

2. Construction of combinatorial library of peptides.

3. Molecular docking of peptides to SLA proteins.

4. Forming of docking score-based QMs (DS-QMs).


Combinatorial library peptide avr score normalized PKYVKQNTLKLAT - 71.47 + 0.456 PKXVKQNTLKLAT - 63.72 - 0.123 PKYXKQNTLKLAT … … PKYVKXNTLKLAT … … PKYVKQNXLKLAT … … PKYVKQNTXKLAT … … PKYVKQNTLKXAT … … PKYVKQNTLKLXT … …

aa\pocket 1 2 3 5 6 7 9

A … … … … … … … C … … … … … … … D … … … … … … … E … … … … … … … … … … … … … … …

QM

0101 – Asn0101 - Leu 0501 - Trp



SLA allele Pocket 2 profile P2 accepts0101 Tyr9, Ala24, Val34, Met45, Glu63, Lys66, Gln67 Leu, Met, Asn

0501 Ser9, Ala24, Val34, Met45, Glu63, Lys66, Gln67 Trp, Leu, Phe

0401 Tyr9, Ala24, Val34, Met45, Glu63, Asn66, Val67 Leu, Met, Thr

1101 Ser9, Glu24, Val34, Met45, Glu63, Arg66, Val67 Leu, Met, Ile

0401 - Met 1101 - Met



SLA allele Pocket 2 profile P2 accepts0101 Tyr9, Ala24, Val34, Met45, Glu63, Lys66, Gln67 Leu, Met, Asn

0501 Ser9, Ala24, Val34, Met45, Glu63, Lys66, Gln67 Trp, Leu, Phe

0401 Tyr9, Ala24, Val34, Met45, Glu63, Asn66, Val67 Leu, Met, Thr

1101 Ser9, Glu24, Val34, Met45, Glu63, Arg66, Val67 Leu, Met, Ile



binders to SLA-1*0101>gi|91177888|gb|ABE27153.1| hemagglutinin [Influenza A virus (A/swine/Spain/53207/2004(H1N1))]MEAKLFVLFCAFTALKADTICVGYHANNSTDTVDTILEKNVTVTHSVNLLENSHNGKLCSLNGKAPLQLGNCNVAGWILGNPECDLLLTANSWSYIIETSNSKNGACYPGEFADYEELREQLSTVSSFERFEIFPKATSWPNHETTKGTTVACSHSGANSFYRNLLWIVKKGNSYPKLSKSYTNNKGKEVLVIWGVHHPPTDSNQQTLYQNNHTYVSVGSSKYYQRFTPEIVARPKVREQAGRMNYYWTLLDQGDTITFEATGNLIAPWHAFALNKGSSSGIMMSDAHVHNCTTKCQTPHGALKSNLPFQNVHPITIGECPKYVKSTQLRMATGLRNIPSTQSRGLFGAIAGFIEGGWTGMIDGWYGYHHQNEQGSGYAADQKSTQIAIDGISNKVNSVIEKMNIQFTSVGKEFNNLEKRIENLNKKVDDGFLDVWTYNAELLILLENERTLDFHDFNVKNLYEKVKSQLRNNAKEIGNGCFEFYHKCDNECMESVKNGTYNYPRYSEESKLNREEIDGVKLESVGVHQILAIYSTVASSLVLLVSLGAISFWMCSNGSLQCRICI




SIV proteins screenedto predict SLA binders:- hemagglutinin (HA)- nucleocapsid protein (NP)- matrix protein 1 (M1)- polymerase PB1 (PB1)

Thank you for your attention!

Download - Structural Immunoinformatics – two case studies

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