Structural Immunoinformatics – two case studies

1. Medical University of Sofia Faculty of Pharmacy Structural Immunoinformatics – two case studies M. Atanasova, I. Dimitrov, A. Patronov, I. Doytchinova Regional Conference in Supercomputing Applications in Science and Industry, 20-21 Sept. 2011, Sunny Beach, Bulgaria

2. Medical University of Sofia Faculty of Pharmacy Immunoinformatics (Computational Immunology, Theoretical Immunology) Immunoinformastic Approaches: Structure - based Sequence - based peptide pIC50exp ILDPFPVTV 8.654 ALDPFPPTV 8.170 VLDPFPITV 8.139 ................ ....... FLDPFPATV8.270 Affinity = f(Interaction energy) Moleculardocking Molecular dynamics Affinity = f(Chemical Structure) Motif-based, QMs, ANN, SVM

3. Medical University of Sofia Faculty of Pharmacy Case study 1: T-cell epitope prediction of proteins from Boophilus microplus • Ticks are hematophagous parasites that feed on variety of domestic animals. • B. microplus tick: • a hard tick; • transmits lethal pathogens; • causes disease and death. Boophilus microplus tick. Aim: to predict peptides originating from B. microplus and binding with high affinity to murine MHC class II proteins IAd and IEd. Collaborator: University of Pretoria, SA

4. Medical University of Sofia Faculty of Pharmacy Case study 1: T-cell epitope prediction of proteins from Boophilus microplus MHCPred and RANKPEP servers for MHC class II binding prediction Sequence - based Approaches used Structure - based Molecular docking calculations

5. Medical University of Sofia Faculty of Pharmacy Case study 1: T-cell epitope prediction of proteins from Boophilus microplus Workflow: B. Microplus number Protein peptides Contig2828 59 Contig7420 93 CK181624 61 Presentation of the selected proteins as sets of overlapping peptides. Selection of high immunogenic B. microplus proteins by VaxiJen server. 1. Selection of input X-ray structures of complexes of murine MHC II protein with a peptide. Homology modeling of IEk to IEd structure 2. Ova/IAd (pdb code: 2iad) HB/IEk (pdb code:1iea) Optimization of complexes of each peptide with each MHC II protein. Docking calculations 3. biding site - 6Å; Chemscore – scoring function; fixed protein and peptide backbone; ranking – by score; GOLD v.5.0.2.

6. Medical University of Sofia Faculty of Pharmacy Case study 1: T-cell epitope prediction of proteins from Boophilus microplus Binding affinity prediction to IAd by MHCPred and RANKPEP MHCPred:Predicted binders with IC50 < 50 nM are highlighted in green. RANKPEP: Predicted binders with binding threshold: 7.10 are highlighted in purple.

7. Medical University of Sofia Faculty of Pharmacy Case study 1: T-cell epitope prediction of proteins from Boophilus microplus Binding affinity prediction to IAd and IEd by Molecular docking IAd IEd The top 2 best clusters of binders are highlighted in magenta.

8. Medical University of Sofia Faculty of Pharmacy Case study 1: T-cell epitope prediction of proteins from Boophilus microplus Peptides selected for further experimental studies

9. Respiratory Coughing Sore Throat Physchological Lethargy Lack of appetite Intestinal Diarrhea Nasopharynx Sneezing Mucous: nose/eyes Systemic Fever Weight loss Poor growth Swine influenza symptoms Medical University of Sofia Faculty of Pharmacy Case study 2: Prediction of peptide binding to Swine Leukocyte Antigen (SLA-1) • Swine Influenza in pigs: • An acute respiratory disease; • High morbidity depending on the • immune status; • Can results in important economic • losses. Aim: to generate quatitative matrices (QMs) for prediction of peptides binding to SLA-1 CReSA Centre de Recerca en Sanitat Animal

10. P9 P2 P1 P7 P3 P6 P5 Medical University of Sofia Faculty of Pharmacy Case study 2: Prediction of peptide binding to Swine Leukocyte Antigen (SLA-1) • Workflow: • 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). • Modeled proteins: • SLA-1*0101 SLA-1*0401 • SLA-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.

11. Combinatorial library peptide avr score normalized PKYVKQNTLKLAT - 71.47 + 0.456 PKXVKQNTLKLAT - 63.72 - 0.123 PKYXKQNTLKLAT … … PKYVKXNTLKLAT … … PKYVKQNXLKLAT …… PKYVKQNTXKLAT … … PKYVKQNTLKXAT … … PKYVKQNTLKLXT … … QM aa\pocket 1 2 3 5 6 7 9 A … … … … … … … C … … … … … … … D … … … … … … … E … … … … … … … …… … … … … … … Medical University of Sofia Faculty of Pharmacy Case study 2: Prediction of peptide binding to Swine Leukocyte Antigen (SLA-1) • Workflow: • 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).

12. 0101 - Leu 0501 - Trp 0101 – Asn Medical University of Sofia Faculty of Pharmacy Case study 2: Prediction of peptide binding to Swine Leukocyte Antigen (SLA-1)

13. 1101 - Met 0401 - Met Medical University of Sofia Faculty of Pharmacy Case study 2: Prediction of peptide binding to Swine Leukocyte Antigen (SLA-1)

14. Medical University of Sofia Faculty of Pharmacy Case study 2: Prediction of peptide binding to Swine Leukocyte Antigen (SLA-1) SIV proteins screened to predict SLA binders: - hemagglutinin (HA) - nucleocapsid protein (NP) - matrix protein 1 (M1) - polymerase PB1 (PB1)

15. Thank you for your attention!