Vaccine development

1. PROTEOMICS A SEARCH TOOL FOR
VACCINES
BY
LAWRENCE OKOROR (Ph.D)
DEPARTMENT OF MICROBIOLOGY AND BIOTECHNOLOGY,
WESTERN DELTA UNIVERSITY, OGHARA.
DELTA STATE, NIGERIA

2. INTRODUCTION
 *In order to be able to synthesize vaccines successfully a good
knowledge of proteomics is necessary. This will help in knowing
the proper interaction of the vaccine in the cell.
 *The focus of proteomics is a biological group called the
proteome. The proteome is dynamic, defined as the set of
proteins expressed in a specific cell, given a particular set of
conditions. The study of an organism’s complete complement of
protein is proteomics.
 *The task of studying the proteome has its share of challenges.
One involves the sheer number of proteins that need to be
identified. The 35,000 genes in the human genome can code for at
least ten times as many proteins; in extreme cases a single gene
alone can code for over 1,000.

3. INTRODUCTION CONT’D
 *Within a given human proteome, the number of
proteins can be as large as 2 million
 *Proteins themselves are macromolecules: long
chains of amino acids. This amino acid chain is
constructed when the cellular machinery of the
ribosome translates RNA transcripts from DNA in
the cell's nucleus.
 *The transfer of information within cells
commonly follows this path, from DNA to RNA to
protein.

4. INTRODUCTION CONT’D

5. INTRO CONT’D
 Each level of protein structure is essential to
the finished molecule's function. The primary
sequence of the amino acid chain
determines where secondary structures will
form, as well as the overall shape of the final
3D conformation. The 3D conformation of
each small peptide or subunit determines the
final structure and function of a protein
conglomerate.

6. CONT’D
 There are many different subdivisions of proteomics,
including:
 Structural proteomics -- in-depth analysis of protein
structure
 Expression proteomics -- analysis of expression and
differential expression of proteins
 Interaction proteomics -- analysis of interactions
between proteins to characterize complexes and
determine function.

7. CONT’D
 Proteomics has both a physical laboratory
component and a computational component.
These two parts are often linked together; at
times data derived from laboratory work can
be fed directly into sequence and structure
prediction algorithms. Mass spectrometry of
multiple types is used most frequently for this
purpose.

8. CONT’D
 the study of proteins, however, has been a
scientific focus for a much longer time.
Studying proteins generates insight on how
proteins affect cell processes. Conversely,
this study also investigates how proteins
themselves are affected by cell processes or
the external environment.

9. Cont’d
 Some proteins may not be present in healthy
cells making them useful as target in drug
discovery.
 And vaccines are drugs although not
conventional type of drug.

10. CONT’D

11. WORKFLOW

12. LASSA FEVER
 Lassa fever is caused by lassa virus which
occurs on epidemic scale all over West
Africa.
 It causes close to 350000 deaths annually in
West Africa.
 A vaccine was developed against the virus
using the nucleoprotein but it failed to protect
in human but in guinea pigs.

13. LASSA CONT’D
 Lassa virus glycoprotein was
comprehensively studied in order to is
effectiveness for use as a vaccine.
 In this regard it comprehensive function was
studied as well as the possible array of
proteins and a possible interaction in human
cell as well as the rat since the rodent is the
reservoir for the virus.

14. Cont’d
 Methods used are
 Global sequence alignment
 Multiple sequence alignment
 Secondary structure prediction which
includes hydrophorbicity, transmembrane
helices, alpha helices, profile of individual
amino acids, which could be done using
protscale at expasy, TMpred, PHDtm and
prof scale.

15. Cont’d
 A proper set of the windows will definitely
give a good prediction with high score but it
is imperative to start with the default window.
 The 3D structure is very important to
determination of the function and its likely
ability to function in the proteome.

16. Cont’d
 Proscan at expasy can be used to determine
the components of the protein
 The virulent part of the protein could be
removed by writing a program in python if
after running alignment the removal does not
affect the function, domain repeats could be
carried out to improve the molecular weight
and then antigenicity.

17. Cont’d
 It is also important to see if the molecular
weight of the protein will function properly in
the proteome towards antigenicity and elicit
the production of antibody.
 This could be carried out using the multiple
domain repeats which might increase the
molecular weight and thereby antigenicity.

18. Cont’d
 It is also very important run a global
alignment again to see if the domain repeat
has changed the structure of the protein. And
if it will protect against a wide strain of the
virus.
 The protein can then be synthesized and
used to vaccinate guinea pigs before human
trials.

19. Cont’d
 Databases like prints, coils and block
databases could also be queried.
 Signal peptide is important in determining if
there is an interaction between the protein
and the cell.
 A study of the protein-protein interaction is
also important to know how the protein is
interacting with cellular protein.

20. Cont’d
 The type of interaction determines the effect
of the protein in the cell.

21. Using the scale Hydrophobicity. OMH/Sweet et al., the
Individual values for the 20 amino acids are:
 Ala: -0.400 Arg: -0.590 Asn: -0.920 Asp: -1.310 Cys: 0.170
 Gln: -0.910 Glu: -1.220 Gly: -0.670 His: -0.640 Ile: 1.250 Leu: 1.220
 Lys: -0.670 Met: 1.020 Phe: 1.920 Pro: -0.490 Ser: -0.550 Thr: -0.280
 Trp: 0.500 Tyr: 1.670 Val: 0.910 Asx: -1.115 Glx: -1.065 Xaa: 0.000
 a. The amino acids with the highest probability of hydrophobicity like
phenalanine determine the hydrophobicity of the protein. And the
determination of such factor like the hydrophobicity is imperative
during vaccine production. It is also important in determining the
administration of the eventual vaccine.

22. Using the scale alpha-helix/Levitt, the individual values
for the 20 amino acids are:
 Ala: 1.290 Arg: 0.960 Asn: 0.900 Asp: 1.040 Cys: 1.110
 Gln: 1.270 Glu: 1.440 Gly: 0.560 His: 1.220 Ile: 0.970 Leu: 1.300 Lys:1.230
Met: 1.470 Phe: 1.070 Pro: 0.520 Ser: 0.820 Thr: 0.820 Trp: 0.990
 a. The alpha –helix defines the probability of assigning a helix to the individual
amino acids. And most of the amino acids gave high probabilities. The helical
structure of the protein, and knowing the helical nature of individual amino acids
could help determine the stability of the vaccine to be produced from the protein
especially when domain repeats is to be used to increase the molecular weight
of the of the protein to make it more antigenic.

23. The Ramanchandran backbone

24. The hidden markov’s model

25. Lassa virus glycoprotein 3D structure

26. Ramancharan backbone
 The Ramanchandran backbone of the lassa virus glycoprotein
from expasy server. Note the clustering of residues in areas
with red colour labeled E, H and B and that most of the
exceptions occur in Glycine residues (labeled G). H represents
the areas with helix while the L represents the probability of
assigning a strand and E is either a strand or a helix. The
allowed regions generate standard conformations. A stretch of
consecutive residues in the H conformation (typically 6–20 in
native states of globular proteins) generates an -helix.
Repeating the L conformation generates an extended β -strand.
Helices are 'standard' or 'prefabricated' structural pieces that
form components of the conformations of most proteins. They
are stabilized by relatively weak interactions, hydrogen bonds,
between mainchain atoms.

27. Hidden markov’s model
 The hidden markov’s model structure of the protein.
The bars along the diagonal represent the amino
acid type: black bars for non-polar, grey bars for
uncharged polar, no bar for charged side chains,
yellow-green bar for glycine. The upper triangle
shows the contact potential for each pair of
positions, colour red for low energy to blue for high
energy. Hence the lassa virus glycolprotein has high
potential for energy.

28. 3D model
 The 3D structure of the lassa virus
glycoprotein using phyre at the expasy
server. The red colour shows area of alpha
helix and are at most 60% conserved. The
blue colour shows area of beta strands which
are least conserved while others are the coil
region. They are also conserved. The coil
regions are also hydrophobic.

29. Protein interaction
 The protein interaction within the cell was
done with genecards and 4 major antigenic
protein was found.

30. Conclusion
 It is imperative to determine the stability of
the vaccine first computationally using the
hidden markov’s model and the
ramanchandran backbone. And later invitro
and invivo.