Role of Bioinformatics in designing

1

• Role of Bioinformatics in designing
• vaccines
• Urmila Kulkarni-Kale
• Information Scientist
• Bioinformatics Centre
• University of Pune, Pune 411 007 India
• urmila_at_bioinfo.ernet.in

2
Biological Research

• Biology is study of life and is a descriptive
  science.
• Macro micro properties
• Research methods are
• In vivo
• In vitro
• In silico

Diversity forms and functions
3
What is Bioinformatics?
Bioinformatics is a scientific discipline that
encompasses all aspects of biological information
acquisition, processing, storage, distribution,
analysis and interpretation.
4
B I O L O G Y
P H Y S I O M I C S
C E L L O M I C S
B I O T E C H
E V O L U T I O N
I N F O T E C H
O N T O L O G Y
P R O T E O M I C S
M O L E C U L A R M O D E L I N G
M A T H E M A T I C S
M E T A B O L O M I C S
T R A N S C R I P T O M I C S
G E N O M I C S
S T A T I S T I C S
Bioinformatics bridges many disciplines
Bioinformatics combines the tools of Biology,
Chemistry, Mathematics, Statistics and Computer
Science to understand Life its processes.
5
The omics Series
B I O L O G Y
P H Y S I O M I C S
C E L L O M I C S
B I O T E C H
E V O L U T I O N
I N F O T E C H
O N T O L O G Y
P R O T E O M I C S
M O L E C U L A R M O D E L I N G
M A T H E M A T I C S
M E T A B O L O M I C S
T R A N S C R I P T O M I C S
G E N O M I C S
S T A T I S T I C S
Omics is Latin word for Give us money!
Transcriptomics Expression profiles of mRNA
Genomics Gene identification characterization
Proteomics functions interactions of proteins
Structural Genomics Large scale structure
determination
Cellinomics Metabolic Pathways Cell-cell
interactions
Pharmacogenomics Genome-based drug design
6
Nature of Biological data
Post-Genomic era Genomes Proteomes Metabolomes
7
Approaches for vaccine development

• Recombinant DNA vaccines
• Peptide Vaccines
• Polytope Vaccines

8

• Vaccine development
• In Post-genomic era
• Reverse Vaccinology
• Approach.
• Rappuoli R. (2000). Reverse vaccinology. Curr
  Opin Microbiol. 3445-450.

9
Genome Sequence
Proteomics Technologies
In silico analysis
DNA microarrays
High throughput Cloning and expression
In vitro and in vivo assays for Vaccine candidate
identification
Global genomic approach to identify new vaccine
candidates
10
In Silico Analysis
Peptide Multiepitope vaccines
VACCINOME
Candidate Epitope DB
Epitope prediction
Disease related protein DB
Gene/Protein Sequence Database
11
(No Transcript)
12
Epitopes
B-cell epitopes
Th-cell epitopes
13
Methods to identify epitopes

• Immunochemical methods
• ELISA Enzyme linked immunosorbent assay
• Immunoflurorescence
• Radioimmunoassay
• X-ray crystallography Ag-Ab complex is
  crystallized and the structure is scanned for
  contact residues between Ag and Ab. The contact
  residues on the Ag are considered as the epitope.
• Prediction methods Based on the X-ray crystal
  data available for Ag-Ab complexes, the
  propensity of an amino acid to lie in an epitope
  is calculated.

14
Epitope prediction methods

• B cell epitope prediction algorithms
• Hopp and Woods 1981
• Welling et al 1985
• Parker Hodges - 1986
• Kolaskar Tongaonkar 1990
• Kolaskar Urmila Kulkarni - 1999
• T cell epitope prediction algorithms
• Margalit, Spouge et al - 1987
• Rothbard Taylor 1988
• Stille et al 1987
• Tepitope -1999

15
Resources

• Programs available
• ANTIGEN Kolaskar Tongaonkars method.
  Available in EMBOSS program as antigenic.
• url http//bioweb.pasteur.fr/seqanal/interfaces/a
  ntigenic.html
• EPIPLOT Compilation of T and B cell prediction
  algorithms. Stand-alone program for PC.
• Databases of interest
• BIMAS
• SYFPEITHI

16
Prediction of epitopes

• Knowledge of antigenic structure
• Delineation of sequential and conformational
  epitopes
• Knowledge of the 3-D structure of antigen
• A method to map conformational epitopes

17
Conformational epitope prediction method
18
(No Transcript)
19
(No Transcript)
20
(No Transcript)
21
Methods Materials
22
(No Transcript)
23
We Have Chosen JE Virus, Because

• JE virus is endemic in South-east Asia including
  India.
• JE virus causes encephalitis in children between
  5-15 years of age with fatality rates between
  21-44.
• Man is a "DEAD END" host.

24
We Have Chosen JE Virus, Because

• Killed virus vaccine purified from mouse brain is
  used presently which requires storage at specific
  temperatures and hence not cost effective in
  tropical countries.
• Protective prophylactic immunity is induced only
  after administration of 2-3 doses.
• Cost of vaccination, storage and transportation
  is high.

25
Why Synthetic Peptide Vaccines?

• Chemically well defined, selective and safe.
• Stable at ambient temperature.
• No cold chain requirement hence cost effective in
  tropical countries.
• Simple and standardised production facility.

26
Egp of JEV as an Antigen

• Is a major structural antigen.
• Responsible for viral haemagglutination.
• Elicits neutralising antibodies.
• 500 amino acids long.
• Structure of extra-cellular domain (399) was
  predicted using knowledge-based homology modeling
  approach.

27
(No Transcript)
28
(No Transcript)
29
Multiple alignment of Predicted TH-cell epitope
in the JE_Egp with corresponding epitopes in
Egps of other Flaviviruses 426 457 JE
DFGSIGGVFNSIGKAVHQVFGGAFRTLFGGMS MVE
DFGSVGGVFNSIGKAVHQVFGGAFRTLFGGMS WNE
DFGSVGGVFTSVGKAIHQVFGGAFRSLFGGMS KUN
DFGSVGGVFTSVGKAVHQVFGGAFRSLFGGMS SLE
DFGSIGGVFNSIGKAVHQVFGGAFRTLFGGMS DEN2
DFGSLGGVFTSIGKALHQVFGAIYGAAFSGVS YF
DFSSAGGFFTSVGKGIHTVFGSAFQGLFGGLN TBE
DFGSAGGFLSSIGKAVHTVLGGAFNSIFGGVG COMM DF S GG S
GK H V G F G Multiple alignment of
JE_Egp with Egps of other Flaviviruses in the
YSAQVGASQ region. 151
183 JE SENHGNYSAQVGASQAAKFTITPNAPSITLKLG MVE
STSHGNYSTQIGANQAVRFTISPNAPAITAKMG WNE
VESHG----KIGATQAGRFSITPSAPSYTLKLG KUN
VESHGNYFTQTGAAQAGRFSITPAAPSYTLKLG SLE
STSHGNYSEQIGKNQAARFTISPQAPSFTANMG DEN2
HAVGNDTG-----KHGKEIKITPQSSTTEAELT YF
QENWN--------TDIKTLKFDALSGSQEVEFI TBE
VAANETHS----GRKTASFTIS--SEKTILTMG

30
STEPS in Homology Modeling

• Template structure (PDB entry 1SVB). (Rey et
  al., 1995).
• Alignment of Egp of JEV and Egp of TBEV.
• Definition of SCRs and Loops.
• Assignment of Initial co-ordinates to Backbone
  Side-chains.
• Rotamer search for the favored side-chain
  conformations.

31
Model RefinementPARAMETERS USED

• force field AMBER all atom
• Dielectric const Distance dependent
• Optimisation Steepest Descents
• Conjugate Gradients.
• rms derivative 0.1 kcal/mol/A for SD
• rms derivative 0.001 kcal/mol/A for CG
• Biosym from InsightII, MSI and modules therein.m

32
ORDER OF REFINEMNT of MODEL

• Loops
• MD at 300?K for 500ps and equilibration of 100ps.
• SCRs adjacent to the loop
• SCRn-1, loopn, SCRn1
• Domains I, II, III
• Full molecule

33
Model For Solvated Protein

• Egp of JEV molecule was soaked in the water layer
  of 10A?.
• 4867 water molecules were added.
• The system size was increased to 20,648 atoms
  from 6047.

34
Model Evaluation IEnergy Profile
35
Model Evaluation II Ramachandran Plot
36
(No Transcript)
37
(No Transcript)
38
Strain specific properties JEVN JEVS
39
Peptide Modeling
Initial random conformation Force field
Amber Distance dependent dielectric constant
4rij Geometry optimization Steepest descents
Conjugate gradients Molecular dynamics at 400 K
for 1ns Peptides are SENHGNYSAQVGASQ
NHGNYSAQVGASQ YSAQVGASQ YSAQVGASQAAKFT
NHGNYSAQVGASQAAKFT SENHGNYSAQVGASQAAKFT 149
168
40
(No Transcript)
41
(No Transcript)
42
(No Transcript)
43
Publication/Patent

• A.S. Kolaskar and Urmila Kulkarni-Kale, 1999 -
  Prediction of three-dimensional structure and
  mapping of conformational epitopes of envelope
  glycoprotein of japanese encephalitis
  virus,Virology, 261, 31-42.
• Chimeric T helper - B cell peptide as a vaccine
  for Flaviviruses
• Gore, MM Dewasthaly, SS Kolaskar, AS
  Kulkarni-Kale, Urmila

44
Epitope prediction References

• Hopp, Woods, 1981, Prediction of protein
  antigenic determinants from amino acid sequences,
  PNAS U.S.A 78, 3824-3828
• Parker, Hodges et al, 1986, New hydrophilicity
  scale derived from high performance liquid
  chromatography peptide retention data
  Correlation of predicted surface residues with
  antigenicity and X-ray derived accessible sites,
  Biochemistry25, 5425-32
• Kolaskar, Tongaonkar, 1990, A semi empirical
  method for prediction of antigenic determinants
  on protein antigens, FEBS 276, 172-174
• Menndez-Arias, L. Rodriguez, R. (1990), A
  BASIC microcomputer program forprediction of B
  and T cell epitopes in proteins, CABIOS, 6,
  101-105
• Peter S. Stern (1991), Predicting antigenic sites
  on proteins, TIBTECH, 9, 163-169
• A.S. Kolaskar and Urmila Kulkarni-Kale, 1999 -
  Prediction of three-dimensional structure and
  mapping of conformational epitopes of envelope
  glycoprotein of Japanese encephalitis
  virus,Virology, 261, 31-42

45
Acknowledgements

• Department of Biotechnology, Govt. of India.
• Immunology Div., NIV.