Presented by Nicholas Svitek, Lucilla Steinaa, Rosemary Saya, Elias Awino, Morten Nielsen, Søren Buus, Vishvanath Nene at the ILRI BioSciences Day, Nairobi, 27 November 2013

1. The quest for the needle (antigen) in the haystack (pathogen):
Immunoinformatics to the rescue
Nicholas Svitek, Lucilla Steinaa, Rosemary Saya, Elias Awino, Morten
Nielsen, Søren Buus, Vishvanath Nene
ILRI BioSciences Day, Nairobi, 27 November 2013

2. Immune Response Elicited during Theileria
parva Infection: Cellular Immunology 101
T. parva infected white blood cells
T. parva
T. parva
peptides
The butler
T CD8+= CTL
(killers of
infected cells)
The butler (the MHC class I molecule)
is serving T. parva peptides to the “Killer T cell”
Killing

3. The Challenge: Many Locks & Keys Are Available
and these need to be Tracked Down
CTL need to find infected cells through
a lock-key mechanism
T. parva
T. parva
T. parva
peptides
The butler
T. parva
peptides
The butler

4. The Challenge: Searching for an Antigen is
like Searching for a Needle in a Haystack

5. The Problem: using Conventional Methods for
Ag ID are Expensive & Time Consuming
Infection & Treatment Method (ITM)
Induction of lifelong CTL response
Restricting BoLA
class I molecule
214VGYPKVKEEML
224
Tp2
27SHEELKKLGML
37
3) 2*01201
Tp2
49KSSHGMGKVGK
59
4) BoLA-T2c
Tp2
96FAQSLVCVL
104
5) BoLA-T5
Tp5
87SKADVIAKY
95
6) 3*00101
Tp8
379CGAELNHFL
387
7) 1*02301
Oxytetracycline
Tp1
2) 6*04101
+
CTL Epitope
1) 6*01301
Muguga
cocktail
T. parva
antigen
Tp9
199AKFPGMKKSK
208

6. Reverse Immunology as a Method to Speed
Up Antigen Identification
Whole Genome Sequence
from T. parva
In silico antigen predictions
In vitro characterization of
predicted antigens
Computer algorithms
trained on biological data
Selection of
immunodominant peptides
Challenge with T. parva
Vaccinate
Naïve cattle

7. Reverse Immunology :
the NetMHCpan Platform
T. parva genome
Prediction of parasite peptides
that binds to
bovine MHC class I molecules
Immunoinformatics
Computer algorithms
trained on biological data
(NetMHCpan)

8. Results 1: The True CTL Epitope that Binds to
BoLA-6*04101 is Tp229-37
NetMHCpan:
Antigen
Binder
Tp2
27SHEELKKLGML
Tp2
29EELKKLGML
37
6*04101
NO (FP 0.058)
37
6*04101
YES (FP 0.002)
Binding assay:
0.9
O.D. (@450 nm)
0.8
0.7
0.6
Tp2.29-37 (Alt)
Tp229-37
0.5
Tp2.27-37
Tp2
27-37
0.4
0.3
0.2
0.1
0
[Peptide]
Variants:
SHEELKKLGML
Var1 SDEELKKLGML
Var2 SDDELDTLGML
Var3 SNEELKKLGMV
Var4 TNEELKKLGMV

9. Using peptide-MHC Class I Tetramers to Confirm
CTL Specificity towards Predicted Epitope
Tetramerization
Recombinant MHC Production
Incubation for
24-48 hours
β2m
CD8+/Tetramer +
CD8+
CD8
http://flow.csc.mrc.ac.uk/?page_id=852

10. Results 2: Identification of Tp1 p-MHC
Tetramer Positive Cells in Ex Vivo Assays
Animal 1
Animal 2
Animal 3
Tp1+ (PE)
Day 8 p.i.
Day 15-17 p.i.
CD8+
(PerCP)

11. Conclusion
• Immunoinformatic (NetMHCpan) can potentially
be used to identify new CTL epitopes.
• p-MHC class I tetramers & NetMHCpan can be
used for correct epitope mapping.

12. Where to from now?
- Combining NetMHCpan with other prediction
parameters; in collaboration with Dr. Morten Nielsen:
•
•
•
Amino acid composition
Protein functional category
Subcellular localization
- T. parva comparative genomics to identify proteins that
are under positive selection [potential targets of the
immune response] ; in collaboration with Dr. Joana Silva.
- Use of immunoinformatics for ASF virus antigen
identification (Dr. Richard Bishop).

13. Acknowledgements
Søren Buus,
tetramers
Morten Nielsen,
NetMHCpan

14. Partners & Donors

15. better lives through livestock
ilri.org
The presentation has a Creative Commons licence. You are free to re-use or distribute this work, provided credit is given to ILRI.