Lecture-11 Applications of Immunoinformatics.pptx

The complexity of Immune system such as huge Numbers of diverse MHC Class I haplotypes, T-cell receptors, B-cell clonotypes, Linear epitopes and conformational epitopes,
T-cell and B-cell Epitopes based on hydrophilicity, flexibility and sec. structure will be evaluated using web servers IEDB, RANKPEP, PEPVAC, IEDB to design Vaccine.

1. Lecture-11 APPLICATIONS OF IMMUNOINFORMATICS Computer Aided Vaccine Design Prof. Sreerama Krupanidhi Email: [email protected]

2. OBJECTIVES To Understand the complexity of Immune system to design computer aided vaccine. To predict T-cell and B-cell Epitopes. To familiar with the web servers viz ., RANKPEP, PEPVAC, IEDB. 8/30/2024 Prof. Sreerama Krupanidhi 2

3. EXPECTED OUTCOMES The complexity of Immune system such as huge Numbers of diverse MHC Class I haplotypes, T-cell receptors, B-cell clonotypes, Linear epitopes and conformational epitopes will be known. Prediction of T-cell and B-cell Epitopes based on hydrophilicity, flexibility and sec. structure will be known. The web servers IEDB, RANKPEP, PEPVAC will be familiarized to design Vaccine. 8/30/2024 Prof. Sreerama Krupanidhi 3

4. Which immune response does the vaccine elicit? T-helper cell mediated immunity Cytotoxic T cell mediated immunity Humoral immune responses Key Questions in Vaccine Design 8/30/2024 4 Prof. Sreerama Krupanidhi

5. Why Comp. Aided Vaccine Design? Immune system is complex 10 13 MHC class I haplotypes (IMGT- HLA) 10 7 -10 15 different T-cell receptors 10 12 B-cell clonotypes in an individual 10 1 1 l i n ear e p i t o pes c o m p o sed o f ni n e amino acids >>10 11 conformational epitopes 8/30/2024 5 Prof. Sreerama Krupanidhi

6. Vac c ine Edward Jenner produced first live vaccine. He produced the vaccine for smallpox from cowpox virus pustule. Nowadays, vaccines are used to prevent many diseases like measles, mumps, malaria, HPV, poliomyelitis, tuberculosis , rubella, yellow fever, rabies, typhoid, influenza, hepatitis B , Cholera, COVID-19, etc. 8/30/2024 6 Prof. Sreerama Krupanidhi

7. VACCINE TYPE VACCINES . RECOMMENDED CHILDHOOD (AGES 0-6 years) Live, attenuated Measles, mumps, rubella (MMR combined vaccine) Varicella (chickenpox) Influenza (nasal spray) Rotavirus Inactivated/Killed Polio ( IPV) , Hepatitis A , Covaxin Toxoid (inactivated toxin) Diphtheria, tetanus (part of DTaP combined immunization) Subunit/conjugate /DNA Hepatitis B ( HbSAg ), Influenza Haemophilus influenza type b (Hib) Pertussis (part of DTaP combined immunization) Pneumococcal , Meningococcal Comirnaty ( mrna vaccine), Covishield , S putnik V (Vectored ) Corbevax 8/30/2024 Prof. Sreerama Krupanidhi 7

8. B-Cell epitopes 8/30/2024 8 Recognized by antibodies Linear B- cell Epitopes Conformational B-cell Epitopes Prof. Sreerama Krupanidhi

9. Consist of contiguous sequence of amino acids e.g. SSAGGQQQESS – a linear epitope of MSP of A. marginale ( major surface protein, Anaplasma marginale ) Also known as Sequential epitopes http://www.imtech.res.in/raghava/bcip e for information on B-cell epitopes 8/30/2024 9 Linear B-cell Epitopes Prof. Sreerama Krupanidhi

10. Linear B-cell Epitopes: easy to predict but not good immunogens Prediction based on: Hydrophilicity Flexibility Secondary structures 8/30/2024 10 P r e di c tio n o f L i n e a r B- c ell Epitopes Prof. Sreerama Krupanidhi

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12. Prediction of Conformational Epitopes Much more difficult than for linear B-cell Epitopes Based on similarity of 3-D structures of different antigens Hence, 3-D structure of antigen MUST be known 8/30/2024 12 Prof. Sreerama Krupanidhi

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14. T- cell Epitopes Have major differences to B-cell epitopes Are linear and produced by antigen processing Presented in conjunction with MHC class I or II molecules to CD8 + Cytotoxic T- cells and CD4 + helper T- cells, respectively 8/30/2024 14 Prof. Sreerama Krupanidhi

15. Complexity of T-cell Epitopes Over 10 13 MHC class I haplotypes There are 10 11 possible linear epitopes composed of 9 amino acids How does the immune system discriminate between epitopes and non- epitopes? 8/30/2024 15 Prof. Sreerama Krupanidhi

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17. Prediction of MHC class I Epitopes Motif based methods Quantitative matrices Structure based methods 8/30/2024 17 Prof. Sreerama Krupanidhi

18. Motif based Epitope Prediction Based on the presence of specific amino acids at certain positions of the peptide These are known as anchor residues Motifs are less accurate (60-65% accuracy) as not all peptides that bind to a given MHC molecule have exact motifs 8/30/2024 18 Prof. Sreerama Krupanidhi

19. Quantitative Matrices are refined motifs, essentially covering all amino acids of the peptide. the contribution of each amino acid at specific position within the binding peptide is quantified. the quantitative matrices are generated from experimental binding data of a large ensemble of variant sequences. Epivax maintains an in-house database on this . 8/30/2024 19 Prof. Sreerama Krupanidhi

20. The score of a peptide is calculated by summing up the individual scores Of the amino acids at each position. 8/30/2024 Prof. Sreerama Krupanidhi 20

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23. In silico Vaccine Discovery A wide range of softwares are available How do you choose the best? Prediction accuracy HLA allele population coverage Promiscuous epitope prediction T-cell epitope Hotspots Conserved epitope prediction 8/30/2024 23 Prof. Sreerama Krupanidhi

24. http://bio.dfci.harvard.edu/PEPVAC / P E PVAC Broad population coverage of HLA alleles Can predict promiscuous epitopes Can identify conserved epitopes Can predict epitopes with proteasome cleavage sites Produces less number of epitopes hence making lab. experimentation easier 8/30/2024 24 Prof. Sreerama Krupanidhi

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26. MULTIPRED Can predict promiscuous epitopes High sensitivity and specificity T-cell Hotspots identification http://research.i2r.a-star.edu.sg/multipred / 8/30/2024 26 Prof. Sreerama Krupanidhi

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28. FUTURE From Genomics to Immunomics Establishment of Immunome Databases- the complete set of epitopes in an organism Modelling of cytokine networks In silico vaccine trials 8/30/2024 28 Prof. Sreerama Krupanidhi

29. Summary 10 13 MHC class I haplotypes (IMGT-HLA), 10 7 -10 15 different T-cell receptors, 10 12 B-cell clonotypes in an individual. 10 1 1 l i n ear e p i t o pes c o m p o sed o f ni n e amino acids, >>10 11 conformational epitopes are shown. Sequential and conformational epitopes and their prediction features such as hydrophilicity, flexibility and secondary structures are shown. The databases, webserver for the prediction of MHC class I and II binding are shown. 8/30/2024 29 Prof. Sreerama Krupanidhi

30. Study questions Elaborate on the computer aided vaccine design. Mention the complexity of Immune system. What are epitopes? Distinguish between sequential and conformational epitopes. Indicate the software tools to predict epitopes. 8/30/2024 30 Prof. Sreerama Krupanidhi

31. A few Bioinformatics server from India 8/30/2024 Prof. Sreerama Krupanidhi 31 http://www.vetbifg.ac.in/tools.php https://india.usegalaxy.eu/ https://ibdc.dbtindia.gov.in/ https://nccs.res.in/Facilities/Bioinformatics http://www.scfbio-iitd.res.in/biogrid/biogrid.htm https://biodb.com/ https://bioinfo.unipune.ac.in/BioDB/Home.html https://aiimsnagpur.edu.in/pages/drug_information_centre https://karnatakadruginfo.com/ https://www.imtech.res.in/facilities/bioinformatics-centre-bic

32. Acknowledgements 8/30/2024 We acknowledge the online resources and public domains for the Preparation of the content to develop teaching material and for the dissemination of knowledge. Prof. Sreerama Krupanidhi 32

33. Thank you 8/30/2024 33 Prof. Sreerama Krupanidhi