1. Bioinformatics in Vaccine Design

2. Vaccination
Administration of a substance to a person with the purpose of preventing a disease
Vaccination works by creating a type of immune response that enables the memory cells to later respond to a similar organism before it can cause disease
Vaccines traditionally composed of attenuated or killed micro-organisms
More recently, parts of micro-organisms have been used (subunit vaccines)
e.g. a single purified protein, or a mixture of such proteins

3. Vaccines work by preparing the immune system for attack

4. Vaccine Design Strategies
Classical Approaches
Killed Vaccines (e.g. typhoid)
Live Attenuated Strains (e.g. BCG)
Subunit Vaccines (e.g. tetanus)
Modern Approaches
Genetically modified attenuated strains
“Naked” and recombinant DNA vaccines
Subunit vaccines: a bioinformatics approach

5. Antigen Processing T cell Binding Proteasomal Cleavage Cathepsin
MHC class I
Peptide Binding
Proteasomal
Cleavage
T cell Binding
Cathepsin
TAP Transporter
Binding
MHC class II

6. Transporter associated with antigen processing (TAP)
TAP is a member of the ATP-binding cassette transporter family.
It delivers cytosolic peptides into the endoplasmic reticulum (ER), where they bind to nascent MHC class I molecules.
The TAP structure is formed of two proteins: TAP-1 and TAP-2, which have one hydrophobic region and one ATP-binding region each. They assemble into a heterodimer, which results in a four-domain transporter.

7. Subunit Vaccine Characteristics
A protein that is a good subunit vaccine candidate…
readily visible to the host immune system
cleaved into peptide
bind to MHC molecules
transport to the cell surface
form stable complexes with T-cell receptors
and elicit a T-cell immune response
conserved between bacterial / viral strains

8. Software for Signal Prediction
PROGRAM
FUNCTION
METHOD
SignalP
Predicts the presence and location of signal peptide cleavage sites
Neural network /
Hidden Markov model
SPScan
Scans protein for the presence of secretary signal peptides
Weight Matrix
SigCleave
Reports protein signal cleavage sites
Matrix

9. Predicting Inner Membrane Proteins
Proteins span cytoplasmic membranes with helical sequences
Transmembrane helices are segments of about 20 predominantly hydrophobic amino acids
There are many accurate programs for predicting transmembrane helices
e.g. TMpred

10. TMpred can rule out inner membrane proteins
The first transmembrane segment is the signal sequence
Proteins with more than one “helical segment” will stay in the inner membrane
These proteins will only be readily accessible to the immune system in Gram positive bacteria
TMpred plot:
Bovine rhodopsin

11. Predicting Proteasomal Cleavage
Relevant in Class I pathway only
~20% of all peptide bonds are cleaved
Average peptide length 6-8 amino acids
Not all peptide bonds are equally likely cleaved
Cleavage more likely after hydrophobic than after hydrophilic amino acids
NetChop is one program for predicting cleavage points
It uses neural network methods

12. Peptide Binding to the MHC
Class I MHC:
Binds peptides from the cytosol or nuclear compartment
Most often from viruses
Some bacterial peptides (e.g. from Mycobacterium tuberculosis)
Complexes bind CD8 receptors on “killer” T cells
Class II MHC:
Binds peptides from intracellular vesicles
The most important pathway for most bacterial peptides
Complexes bind CD4 receptors on “helper” T cells
This stimulates antibody production by B cells

13. The MHC locus is POLYGENIC: DO
The MHC locus is POLYGENIC:
Each individual’s immune system has 8 different MHC class II molecules.
The MHC locus is also highly POLYMORPHIC:
These 8 MHC class II molecules vary between members of a given population.

14. Predicting MHC Binding
Many programs predict binding from sequence patterns
Most use “matrix” type methods, assuming amino acid binding pockets are independent
MHC Class I binding is easier to predict
Less interaction between binding pockets
More amino acid specificity
But the class II pathway is the more important for bacterial infection

15. Epitopes/ Antigenic determinants
The portions of the antigen molecules which are responsible for specificity of the antigens in antigen-antibody (Ag-Ab) reactions
Types of Epitopes
Sequential / Continuous epitopes:
recognized by Th cells
linear peptide fragments
amphipathic helical 9-12 mer
Conformational / Discontinuous epitopes:
recognized by both Th & B cells
non-linear discrete amino acid sequences, come together due to folding
exposed mer

16. Properties of Epitopes
They occur on the surface of the protein and are more flexible than the rest of the protein
They have high degree of exposure to the solvent
They have beta-turns in the secondary structure
The amino acids making the epitope are usually charged and hydrophilic

17. Epitope prediction algorithms
B cell:
Hopp and Woods –1981
Welling et al –1985
Parker & Hodges
Kolaskar & Tongaonkar – 1990
Kolaskar & Urmila Kulkarni
T cell:
Margalit, Spouge et al
Rothbard & Taylor – 1988
Stille et al –1987
Tepitope -1999

18. Hopp & Woods method
Pioneering work
Based on the fact that only the hydrophilic nature of amino acids is essential for an sequence to be an antigenic determinant
Local hydrophilicity values are assigned to each amino acid by the method of repetitive averaging using a window of six
Not very accurate

19. Welling’s method
Based on the percentage of each amino acid present in known epitopes compared with the percentage of amino acid in the average composition of a protein.
Assigns an antigenicity value for each amino acid from the relative occurrence of the amino acid in an antigenic determinant site.
Regions of 7 amino acid with relatively high antigenicity are extended to amino acid depending on the antigenicity values of neighboring residues.

20. Parker & Hodges method
Utilizes 3 parameters :
Hydrophilicity
Accessibility
Flexibility
Hydrophilicity parameter was calculated using HPLC from retention co-efficients of model synthetic peptides.
Surface profile was determined by summing the parameters for each residue of a seven-residue segment and assigning the sum to the fourth residue.
One of the most useful prediction algorithms

21. Kolaskar & Tongaonkar’s method
Semi-empirical method which uses physiological properties of amino acid residues.
Frequencies of occurrence of amino acids in experimentally known epitopes.
Data of 169 epitopes from 34 different proteins was collected of which 156 which have less than 20 aa per determinant were used.

22. T-cell epitope prediction algorithms
Considers amphipathic helix segments, tetramer and pentamer motifs (charged residues or glycine) followed by 2-3 hydrophobic residues and then a polar residue.
Sequence motifs of immunodominant secondary structure capable of binding to MHC with high affinity.
Virtual matrices which are used for predicting MHC polymorphism and anchor residues.

23. Ab-binding sites: Sequential & Conformational Epitopes!
Paratope
Sequential
Conformational
Ab-binding sites

24. VAXIPRED: A software package for predicting subunit vaccine targets

25. Immune epitope tools