Title: Computeraided Vaccine and Drug Discovery G'P'S' Raghava
1Computer-aided Vaccine and Drug Discovery
G.P.S. Raghava
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- Understanding immune system
- Breaking complex problem
- Adaptive immunity
- Innate Immunity
- Vaccine delivery system
- ADMET of peptides
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- Annotation of genomes
- Searching drug targets
- Properties of drug molecules
- Protein-chemical interaction
- Prediction of drug-like molecules
Vaccine Informatics
Drug Informatics
2- Limitations of methods of subunit vaccine design
- Methods for one or two MHC alleles
- Do not consider pathways of antigen processing
- Limited to T-cell epitopes
- Initiatives taken by our group
- Understand complete mechanism of antigen
processing - Develop better and comprehensive methods
- Promiscuous MHC binders
3 Pathogens/Invaders
4Endogenous Antigen Processing
ER
TAP
Prediction of CTL Epitopes (Cell-mediated
immunity)
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6MHCBN A database of MHC/TAP binders and T-cell
epitopes
Distributed by EBI, UK
Reference database in T-cell epitopes Highly
Cited ( 70 citations)
Bhasin et al. (2003) Bioinformatics 19
665 Bhasin et al. (2004) NAR (Online)
7- Prediction of MHC II Epitopes ( Thelper Epitopes)
- Propred Promiscuous of binders for 51 MHC Class
II binders - Virtual matrices
- Singh and Raghava (2001) Bioinformatics 171236
- HLADR4pred Prediction of HLA-DRB10401 binding
peptides - Dominating MHC class II allele
- ANN and SVM techniques
- Bhasin and Raghava (2004) Bioinformatics 12421.
- MHC2Pred Prediction of MHC class II binders for
41 alleles - Human and mouse
- Support vector machine (SVM) technique
- Extension of HLADR4pred
- MMBpred Prediction pf Mutated MHC Binder
- Mutations required to increase affinity
- Mutation required for make a binder promiscuous
- Bhasin and Raghava (2003) Hybrid Hybridomics,
22229 - MOT Matrix optimization technique for binding
core - MHCBench Benchmarting of methods for MHC binders
8- Prediction of MHC I binders and CTL Epitopes
- Propred1 Promiscuous binders for 47 MHC class I
alleles - Cleavage site at C-terminal
- Singh and Raghava (2003) Bioinformatics 191109
- nHLApred Promiscuous binders for 67 alleles
using ANN and QM - Bhasin and Raghava (2007) J. Biosci. 3231-42
- TAPpred Analysis and prediction of TAP binders
- Bhasin and Raghava (2004) Protein Science 13596
- Pcleavage Proteasome and Immuno-proteasome
cleavage site. - Trained and test on in vitro and in vivo data
- Bhasin and Raghava (2005) Nucleic Acids Research
33 W202-7 - CTLpred Direct method for Predicting CTL
Epitopes - Bhasin and Raghava (2004) Vaccine 223195
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10BCIPEP A database of B-cell epitopes.
Saha et al.(2005) BMC Genomics 679. Saha et al.
(2006) NAR (Online)
11Prediction of B-Cell Epitopes
- BCEpred Prediction of Continuous B-cell epitopes
- Benchmarking of existing methods
- Evaluation of Physico-chemical properties
- Poor performance slightly better than random
- Combine all properties and achieve accuracy
around 58 - Saha and Raghava (2004) ICARIS 197-204.
- ABCpred ANN based method for B-cell epitope
prediction - Extract all epitopes from BCIPEP (around 2400)
- 700 non-redundant epitopes used for testing and
training - Recurrent neural network
- Accuracy 66 achieved
- Saha and Raghava (2006) Proteins,6540-48
- ALGpred Mapping and Prediction of Allergenic
Epitopes - Allergenic proteins
- IgE epitope and mapping
- Saha and Raghava (2006) Nucleic Acids Research
34W202-W209
12 HaptenDB A database of hapten molecules
13VAXIPRED A Software Package for Predicting
Subunit Vaccine Targets
14PRRDB is a database of pattern recognition
receptors and their ligands
500 Pattern-recognition Receptors 228 ligands
(PAMPs) 77 distinct organisms 720 entries
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16Major Challenges in Vaccine Design
- ADMET of peptides and proteins
- Activate innate and adaptive immunity
- Prediction of carrier molecules
- Avoid cross reactivity (autoimmunity)
- Prediction of allergic epitopes
- Solubility and degradability
- Absorption and distribution
- Glycocylated epitopes
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18- FTGpred Prediction of Prokaryotic genes
- Ab initio method for gene prediction using FFT
technique - Issac et al. (2002) Bioinformatics 18197
- EGpred Prediction of eukaryotic genes
- BLASTX against RefSeq BLASTN against intron
database - NNSPLICE program is used to reassign splicing
signal site positions - Issac and Raghava (2004) Genome Research 141756
- GeneBench Benchmarking of gene finders
- Collection of different datasets
- Tools for evaluating a method
- Creation of own datasets
- SRF Spectral Repeat finder
- FFT based repeat finder
- Sharma et al. (2004) Bioinformatics 20 1405
- Work in Progress
- Prediction of polyadenylation signal (PAS) in
human coding DNA - Understanding DICER cutter sites and siRNA/miRNA
efficacy - Predict transcription factor binding sites in DNA
sequences
19- Comparative genomics
- GWFASTA Genome Wide FASTA Search
- Analysis of FASTA search for comparative genomics
- Biotechniques 2002, 33548
- GWBLAST Genome wide BLAST search
- COPID Composition based similarity search
- LGEpred Expression of a gene from its Amino acid
sequence - BMC Bioinformatics 2005, 659
- ECGpred Expression from its nucleotide sequence
20 - Subcellular localization Methods
- PSLpred Subcellular localization of prokaryotic
proteins - 5 major sub cellular localization
- Bioinformatics 2005, 21 2522
- ESLpred Subcellular localization of Eukaryotic
proteins - SVM based method
- Amino acid, Dipetide and properties composition
- Sequence profile (PSIBLAST)
- Nucleic Acids Research 2004, 32W414-9
- HSLpred Sub cellular localization of Human
proteins - Need to develop organism specific methods
- 84 accuracy for human proteins
- Journal of Biological Chemistry 2005,
28014427-32 - MITpred Prediction of Mitochndrial proteins
- Exclusive mitochndrial domain and SVM
- J Biol Chem. 2005, 2815357-63.
- Work in Progress Subcellular localization of
M.Tb. and malaria
21- Regular Secondary Structure Prediction (?-helix
?-sheet) - APSSP2 Highly accurate method for secondary
structure prediction - Competete in EVA, CAFASP and CASP (In top 5
methods) - Irregular secondary structure prediction methods
(Tight turns) - Betatpred Consensus method for ?-turns
prediction - Statistical methods combined
- Kaur and Raghava (2001) Bioinformatics
- Bteval Benchmarking of ?-turns prediction
- Kaur and Raghava (2002) J. Bioinformatics and
Computational Biology, 1495504 - BetaTpred2 Highly accurate method for predicting
?-turns (ANN, SS, MA) - Multiple alignment and secondary structure
information - Kaur and Raghava (2003) Protein Sci 12627-34
- BetaTurns Prediction of ?-turn types in proteins
- Kaur and Raghava (2004) Bioinformatics 202751-8.
- AlphaPred Prediction of ?-turns in proteins
- Kaur and Raghava (2004) Proteins Structure,
Function, and Genetics 5583-90 - GammaPred Prediction of ?-turns in proteins
- Kaur and Raghava (2004) Protein Science
12923-929.
22- Supersecondary Structure
- BhairPred Prediction of Beta Hairpins
- Secondary structure and surface accessibility
used as input - Manish et al. (2005) Nucleic Acids Research
33W154-9 - TBBpred Prediction of outer membrane proteins
- Prediction of trans membrane beta barrel proteins
- Application of ANN and SVM Evolutionary
information - Natt et al. (2004) Proteins 5611-8
- ARNHpred Analysis and prediction side chain,
backbone interactions - Prediction of aromatic NH interactions
- Kaur and Raghava (2004) FEBS Letters 56447-57 .
- Chpredict Prediction of C-H .. O and PI
interaction - Kaur and Raghava (2006) In-Silico Biology 60011
- SARpred Prediction of surface accessibility
(real accessibility) - Multiple alignment (PSIBLAST) and Secondary
structure information - Garg et al., (2005) Proteins 61318-24
- Secondary to Tertiary Structure
- PepStr Prediction of tertiary structure of
Bioactive peptides - Kaur et al. (2007) Protein Pept Lett. (In Press)
23 24 - Nrpred Classification of nuclear receptors
- BLAST fails in classification of NR proteins
- Uses composition of amino acids
- Journal of Biological Chemistry 2004, 279 23262
- GPCRpred Prediction of G-protein-coupled
receptors - Predict GPCR proteins class
- gt 80 in Class A, further classify
- Nucleic Acids Research 2004, 32W383
- GPCRsclass Amine type of GPCR
- Major drug targets, 4 classes,
- Accuracy 96.4
- Nucleic Acids Research 2005, 33W172
- VGIchanVoltage gated ion channel
- Genomics Proteomics Bioinformatics 2007,
4253-8 - Pprint RNA interacting residues in proteins
- Proteins Structure, Function and Bioinformatics
(In Press) - GSTpred Glutathione S-transferases proteins
- Protein Pept Lett. 2007, 6575-80
25- Antibp Analysis and prediction of antibacterial
peptides - Searching and mapping of antibacterial peptide
- BMC Bioinformatics 2007, 8263
- ALGpred Prediction of allergens
- Using allergen representative peptides
- Nucleic Acids Research 2006, 34W202-9.
- BTXpred Prediction of bacterial toxins
- Classifcation of toxins into exotoxins and
endotoxins - Classification of exotoxins in seven classes
- In Silico Biology 2007, 7 0028
- NTXpred Prediction of neurotoxins
- Classification based on source
- Classification based on function (ion channel
blockers, blocks Acetylcholine receptors etc.) - In Silico Biology 2007, 7, 0025
26- Work in Progress (Future Plan)
- Prediction of solubility of proteins and peptides
- Understand drug delivery system for protein
- Degradation of proteins
- Improving thermal stability of a protein (Protein
Science 122118-2120) - Analysis and prediction of druggable
proteins/peptide
27- MELTpred Prediction of melting point of chemical
compunds - Around 4300 compounds were analzed to derive
rules - Successful predicted melting point of 277
drug-like molecules - Future Plan
- QSAR models for ADMET
- QSAR docking for ADMET
- Prediction of drug like molecules
- Open access in Chemoinformatics
28- Understanding Protein-Chemical Interaction
- Prediction of Kinases Targets and Off Targets
- Kinases inhibitors were analyzed
- Model build to predict inhbitor against kinases
- Cross-Specificity were checked
- Useful for predicting targets and off targets
- Future Plan
- Classification of proteins based on chemical
interaction - Clustering drug molecules based on interaction
with proteins
29Thankyou