G.P.S. Raghava
 Understanding immune system
 Breaking complex problem
 Adaptive immunity
 Innate Immunity
 Vaccine delivery system
 ADMET of peptides
 Annotation of genomes
 Searching drug targets
 Properties of drug molecules
 Protein-chemical interaction
 Prediction of drug-like molecules
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Adaptive Immunity
Innate Immunity
WHOLE
ORGANISM
Bioinformatics Centre
IMTECH, Chandigarh
Purified Antigen
Protective Antigens
Vaccine Delivery
Epitopes (Subunit
Vaccine)
T cell epitope
Attenuated
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 2

Adaptive Immunity
Innate Immunity
Disease Causing
Agents
Bioinformatics Centre
IMTECH, Chandigarh
Protective Antigens
Vaccine Delivery
3
Pathogens/Invaders

Adaptive Immunity
Innate Immunity
Protective Antigens
Vaccine Delivery
ER
TAP
Prediction of CTL Epitopes (Cell-mediated immunity)
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Adaptive Immunity
Innate Immunity
Protective Antigens
Vaccine Delivery
5

Adaptive Immunity
Innate Immunity
MHCBN: 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)
Protective Antigens
Vaccine Delivery
6

Adaptive Immunity
Innate Immunity
Protective Antigens
Vaccine Delivery
•
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Prediction of MHC II Epitopes ( T helper
Epitopes)
Propred: Promiscuous of binders for 51 MHC Class II binders
–
Virtual matrices
– Singh and Raghava (2001) Bioinformatics 17:1236
HLADR4pred : Prediction of HLA-DRB1*0401 binding peptides
–
Dominating MHC class II allele
–
ANN and SVM techniques
– Bhasin and Raghava (2004) Bioinformatics 12:421.
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, 22:229
MOT : Matrix optimization technique for binding core
MHCBench: Benchmarting of methods for MHC binders
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Adaptive Immunity
Innate Immunity
Protective Antigens
Vaccine Delivery
Propred1 : Promiscuous binders for 47 MHC class I alleles
– Cleavage site at C-terminal
–
Singh and Raghava (2003) Bioinformatics 19:1109 nHLApred: Promiscuous binders for 67 alleles using ANN and QM
– Bhasin and Raghava (2007) J. Biosci. 32:31-42
TAPpred: Analysis and prediction of TAP binders
– Bhasin and Raghava (2004) Protein Science 13:596
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
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Bhasin and Raghava (2004) Vaccine 22:3195 8

Adaptive Immunity
Innate Immunity
Protective Antigens
Vaccine Delivery
9

Adaptive Immunity
Innate Immunity
BCIPEP: A database of
B-cell epitopes.
Saha et al.(2005) BMC Genomics 6:79.
Saha et al. (2006) NAR (Online)
Protective Antigens
Vaccine Delivery
10

Adaptive Immunity
Innate Immunity
Protective Antigens
Vaccine Delivery
Prediction of B-Cell Epitopes
• BCEpred: Prediction of Continuous B-cell epitopes
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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,65:40-48
• ALGpred: Mapping and Prediction of Allergenic Epitopes
– Allergenic proteins
–
IgE epitope and mapping
– Saha and Raghava (2006) Nucleic Acids Research 34:W202-W209 11

Adaptive Immunity
Innate Immunity
Protective Antigens
Vaccine Delivery
HaptenDB: A database of hapten molecules
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Adaptive Immunity
Innate Immunity
Bioinformatics Centre
IMTECH, Chandigarh
PRRDB is a database of pattern recognition receptors and their ligands
~500 Pattern-recognition Receptors
228 ligands (PAMPs)
77 distinct organisms
720 entries
Protective Antigens
Vaccine Delivery
14

Adaptive Immunity
Innate Immunity
Bioinformatics Centre
IMTECH, Chandigarh
Protective Antigens
Vaccine Delivery
15

Adaptive Immunity
Innate Immunity
Bioinformatics Centre
IMTECH, Chandigarh
Major 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
Protective Antigens
Vaccine Delivery
16

Searching and analyzing druggable targets
Nucleotide
Genome
Annotation
Proteome
Annotation
Protein
Protein
Struct.
Target
Proteins
Drug
Informatics
Prediction and analysis of drug molecules
Biologicals
Protein
Chemical s
Protein-drug
Drugs
Interaction
Drug-like
Molecules
17

Searching and analyzing druggable targets
Nucleotide
Genome
Annotation
Proteome
Annotation
Protein
Protein
Struct.
Target
Proteins
Drug
Informatics
Prediction and analysis of drug molecules
Biologicals
Protein
Chemical s
Protein-drug
Drugs
Interaction
Drug-like
Molecules
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FTGpred: Prediction of Prokaryotic genes
– Ab initio method for gene prediction using FFT technique
– Issac et al. (2002) Bioinformatics 18:197
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 14:1756
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
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•
•
Searching and analyzing druggable targets
Nucleotide
Genome
Annotation
Proteome
Annotation
Protein
Protein
Struct.
Target
Proteins
Comparative genomics
GWFASTA: Genome Wide FASTA
Search
– Analysis of FASTA search for comparative genomics
– Biotechniques 2002, 33:548
Drug
Informatics
GWBLAST: Genome wide BLAST search
COPID: Composition based similarity search
LGEpred: Expression of a gene from its Amino acid sequence
– BMC Bioinformatics 2005, 6:59
ECGpred: Expression from its nucleotide sequence
Prediction and analysis of drug molecules
Biologicals
Protein
Chemical s
Protein-drug
Drugs
Interaction
Drug-like
Molecules
19

Searching and analyzing druggable targets
Nucleotide Protein
Genome
Annotation
Proteome
Annotation
Protein
Struct.
Target
Proteins
Drug
Informatics
Prediction and analysis of drug molecules
Biologicals
Protein
Chemical s
Protein-drug
Drugs
Interaction
Drug-like
Molecules
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, 32:W414-9
HSLpred : Sub cellular localization of Human proteins
– Need to develop organism specific methods
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84% accuracy for human proteins
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Journal of Biological Chemistry 2005, 280:14427-32
MITpred: Prediction of Mitochndrial proteins
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Exclusive mitochndrial domain and SVM
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J Biol Chem. 2005, 281:5357-63.
Work in Progress: Subcellular localization of M.Tb. and malaria
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Searching and analyzing druggable targets
Nucleotide
Genome
Annotation
Proteome
Annotation
Protein
Protein
Struct.
Target
Proteins
Drug
Informatics
Prediction and analysis of drug molecules
Biologicals
Protein
Chemical s
Protein-drug
Drugs
Interaction
Drug-like
Molecules
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, 1:495:504
• BetaTpred2 : Highly accurate method for predicting

-turns (ANN, SS, MA)
– Multiple alignment and secondary structure information
– Kaur and Raghava (2003) Protein Sci 12:627-34
• BetaTurns : Prediction of

-turn types in proteins
– Kaur and Raghava (2004) Bioinformatics 20:2751-8.
• AlphaPred : Prediction of

-turns in proteins
– Kaur and Raghava (2004) Proteins: Structure, Function, and Genetics 55:83-90
•
GammaPred : Prediction of

-turns in proteins
– Kaur and Raghava (2004) Protein Science; 12:923-929 .
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Searching and analyzing druggable targets
Nucleotide
Genome
Annotation
Proteome
Annotation
Protein
Protein
Struct.
Target
Proteins
Drug
Informatics
Prediction and analysis of drug molecules
Biologicals
Protein
Chemical s
Protein-drug
Drugs
Interaction
Drug-like
Molecules
Supersecondary Structure
BhairPred : Prediction of Beta Hairpins
– Secondary structure and surface accessibility used as input
– Manish et al. (2005) Nucleic Acids Research 33:W154-9
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: 56:11-8
ARNHpred: Analysis and prediction side chain, backbone interactions
– Prediction of aromatic NH interactions
– Kaur and Raghava (2004) FEBS Letters 564:47-57 .
Chpredict : Prediction of C-H .. O and PI interaction
– Kaur and Raghava (2006) In-Silico Biology 6:0011
SARpred : Prediction of surface accessibility (real accessibility)
– Multiple alignment (PSIBLAST) and Secondary structure information
– Garg et al., (2005) Proteins 61:318-24
Secondary to Tertiary Structure
PepStr : Prediction of tertiary structure of Bioactive peptides
– Kaur et al. (2007) Protein Pept Lett. (In Press)
22

Searching and analyzing druggable targets
Nucleotide Protein
Genome
Annotation
Proteome
Annotation
Protein
Struct.
Target
Proteins
Drug
Informatics
Prediction and analysis of drug molecules
Biologicals
Protein
Chemical s
Protein-drug
Drugs
Interaction
Drug-like
Molecules
23

Searching and analyzing druggable targets
Nucleotide Protein
Genome
Annotation
Proteome
Annotation
Protein
Struct.
Target
Proteins
Drug
Informatics
Prediction and analysis of drug molecules
Biologicals
Protein
Chemical s
Protein-drug
Drugs
Interaction
Drug-like
Molecules
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
– > 80% in Class A, further classify
Nucleic Acids Research 2004, 32:W383
GPCRsclass : Amine type of GPCR
–
Major drug targets, 4 classes,
– Accuracy 96.4%
Nucleic Acids Research 2005, 33:W172
VGIchan: Voltage gated ion channel
– Genomics Proteomics & Bioinformatics 2007, 4:253-8
Pprint: RNA interacting residues in proteins
– Proteins: Structure, Function and Bioinformatics (In Press)
GSTpred: Glutathione S-transferases proteins
–
Protein Pept Lett. 2007, 6:575-80
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Searching and analyzing druggable targets
Nucleotide
Genome
Annotation
Proteome
Annotation
Protein
Protein
Struct.
Target
Proteins
Drug
Informatics
Prediction and analysis of drug molecules
Biologicals
Protein
Chemical s
Protein-drug
Drugs
Interaction
Drug-like
Molecules
Antibp: Analysis and prediction of antibacterial peptides
• Searching and mapping of antibacterial peptide
•
BMC Bioinformatics 2007, 8:263
ALGpred: Prediction of allergens
•Using allergen representative peptides
•
Nucleic Acids Research 2006, 34:W202-9.
BTXpred: Prediction of bacterial toxins
•Classifcation of toxins into exotoxins and endotoxins
•Classification of exotoxins in seven classes
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In Silico Biology 2007, 7: 0028
• NTXpred: Prediction of neurotoxins
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Classification based on source
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Classification based on function (ion channel blockers, blocks
Acetylcholine receptors etc.)
• In Silico Biology 2007, 7, 0025
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Searching and analyzing druggable targets
Nucleotide
Genome
Annotation
Proteome
Annotation
Protein
Protein
Struct.
Target
Proteins
Drug
Informatics
Prediction and analysis of drug molecules
Biologicals
Protein
Chemical s
Protein-drug
Drugs
Interaction
Drug-like
Molecules
1. Prediction of solubility of proteins and peptides
2. Understand drug delivery system for protein
3. Degradation of proteins
4. Improving thermal stability of a protein ( Protein
Science 12:2118-2120 )
5. Analysis and prediction of druggable proteins/peptide
26

Searching and analyzing druggable targets
Nucleotide
Genome
Annotation
Proteome
Annotation
Protein
Protein
Struct.
Target
Proteins
Drug
Informatics
Prediction and analysis of drug molecules
Biologicals
Protein
Chemical s
Protein-drug
Drugs
Interaction
Drug-like
Molecules
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
1. QSAR models for ADMET
2. QSAR + docking for ADMET
3. Prediction of drug like molecules
4. Open access in Chemoinformatics
27

Searching and analyzing druggable targets
Nucleotide Protein
Genome
Annotation
Proteome
Annotation
Protein
Struct.
Target
Proteins
Drug
Informatics
Prediction and analysis of drug molecules
Biologicals
Protein
Chemical s
Protein-drug
Drugs
Interaction
Drug-like
Molecules
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
• Classification of proteins based on chemical interaction
•
Clustering drug molecules based on interaction with proteins
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