1 Computer-aided Vaccine and Drug Discovery G.P.S. Raghava Understanding immune system Breaking complex problem Adaptive immunity Innate Immunity Vaccine.

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Computer-aided Vaccine and Drug Discovery

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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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

Innate Immunity Vaccine Delivery

Protective AntigensAdaptive Immunity

Bioinformatics CentreIMTECH, Chandigarh

WHOLEORGANISM

Attenuated

Epitopes (Subunit Vaccine)

Purified Antigen

T cell epitope

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Pathogens/Invaders

Disease Causing Agents

Innate Immunity Vaccine Delivery

Protective AntigensAdaptive Immunity

Bioinformatics CentreIMTECH, Chandigarh

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Prediction of CTL Epitopes (Cell-mediated immunity)

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TAP

Innate Immunity Vaccine Delivery

Protective AntigensAdaptive Immunity

Bioinformatics CentreIMTECH, Chandigarh

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Innate Immunity Vaccine Delivery

Protective AntigensAdaptive Immunity

Bioinformatics CentreIMTECH, Chandigarh

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Innate Immunity Vaccine Delivery

Protective AntigensAdaptive Immunity

Bioinformatics CentreIMTECH, Chandigarh

MHCBN: A database of MHC/TAP binders and T-cell epitopes

Distributed by EBI, UK

Bhasin et al. (2003) Bioinformatics 19: 665

Bhasin et al. (2004) NAR (Online)

Reference database in T-cell epitopesHighly Cited ( ~ 70 citations)

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Prediction of MHC II Epitopes ( Thelper 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

Innate Immunity Vaccine Delivery

Protective AntigensAdaptive Immunity

Bioinformatics CentreIMTECH, Chandigarh

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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 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– Bhasin and Raghava (2004) Vaccine 22:3195

Innate Immunity Vaccine Delivery

Protective AntigensAdaptive Immunity

Bioinformatics CentreIMTECH, Chandigarh

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Innate Immunity Vaccine Delivery

Protective AntigensAdaptive Immunity

Bioinformatics CentreIMTECH, Chandigarh

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Innate Immunity Vaccine Delivery

Protective AntigensAdaptive Immunity

Bioinformatics CentreIMTECH, Chandigarh

Saha et al.(2005) BMC Genomics 6:79.

Saha et al. (2006) NAR (Online)

BCIPEP: A database of B-cell epitopes.

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Prediction 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,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

Innate Immunity Vaccine Delivery

Protective AntigensAdaptive Immunity

Bioinformatics CentreIMTECH, Chandigarh

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HaptenDB: A database of hapten molecules

Innate Immunity Vaccine Delivery

Protective AntigensAdaptive Immunity

Bioinformatics CentreIMTECH, Chandigarh

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Innate Immunity Vaccine Delivery

Protective AntigensAdaptive Immunity

Bioinformatics CentreIMTECH, Chandigarh

PRRDB 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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Innate Immunity Vaccine Delivery

Protective AntigensAdaptive Immunity

Bioinformatics CentreIMTECH, Chandigarh

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Innate Immunity Vaccine Delivery

Protective AntigensAdaptive Immunity

Bioinformatics CentreIMTECH, 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

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DrugInformatics

Searching and analyzing druggable targets Prediction and analysis of drug molecules

Nucleotide Protein Biologicals Chemicals

GenomeAnnotation

ProteomeAnnotation

ProteinStruct.

TargetProteins

ProteinDrugs

Protein-drugInteraction

Drug-likeMolecules

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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

DrugInformatics

Searching and analyzing druggable targets Prediction and analysis of drug molecules

Nucleotide Protein Biologicals Chemicals

GenomeAnnotation

ProteomeAnnotation

ProteinStruct.

TargetProteins

ProteinDrugs

Protein-drugInteraction

Drug-likeMolecules

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Comparative genomics • GWFASTA: Genome Wide FASTA Search

– Analysis of FASTA search for comparative genomics– Biotechniques 2002, 33:548

• 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

DrugInformatics

Searching and analyzing druggable targets Prediction and analysis of drug molecules

Nucleotide Protein Biologicals Chemicals

GenomeAnnotation

ProteomeAnnotation

ProteinStruct.

TargetProteins

ProteinDrugs

Protein-drugInteraction

Drug-likeMolecules

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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– 84% accuracy for human proteins– Journal of Biological Chemistry 2005, 280:14427-32

MITpred: Prediction of Mitochndrial proteins– Exclusive mitochndrial domain and SVM– J Biol Chem. 2005, 281:5357-63.

Work in Progress: Subcellular localization of M.Tb. and malaria

DrugInformatics

Searching and analyzing druggable targets Prediction and analysis of drug molecules

Nucleotide Protein Biologicals Chemicals

GenomeAnnotation

ProteomeAnnotation

ProteinStruct.

TargetProteins

ProteinDrugs

Protein-drugInteraction

Drug-likeMolecules

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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.

DrugInformatics

Searching and analyzing druggable targets Prediction and analysis of drug molecules

Nucleotide Protein Biologicals Chemicals

GenomeAnnotation

ProteomeAnnotation

ProteinStruct.

TargetProteins

ProteinDrugs

Protein-drugInteraction

Drug-likeMolecules

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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 StructurePepStr: Prediction of tertiary structure of Bioactive peptides

– Kaur et al. (2007) Protein Pept Lett. (In Press)

DrugInformatics

Searching and analyzing druggable targets Prediction and analysis of drug molecules

Nucleotide Protein Biologicals Chemicals

GenomeAnnotation

ProteomeAnnotation

ProteinStruct.

TargetProteins

ProteinDrugs

Protein-drugInteraction

Drug-likeMolecules

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DrugInformatics

Searching and analyzing druggable targets Prediction and analysis of drug molecules

Nucleotide Protein Biologicals Chemicals

GenomeAnnotation

ProteomeAnnotation

ProteinStruct.

TargetProteins

ProteinDrugs

Protein-drugInteraction

Drug-likeMolecules

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Nrpred: Classification of nuclear receptors– BLAST fails in classification of NR proteins– Uses composition of amino acidsJournal of Biological Chemistry 2004, 279: 23262

GPCRpred: Prediction of G-protein-coupled receptors– Predict GPCR proteins & class– > 80% in Class A, further classifyNucleic 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

DrugInformatics

Searching and analyzing druggable targets Prediction and analysis of drug molecules

Nucleotide Protein Biologicals Chemicals

GenomeAnnotation

ProteomeAnnotation

ProteinStruct.

TargetProteins

ProteinDrugs

Protein-drugInteraction

Drug-likeMolecules

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DrugInformatics

Searching and analyzing druggable targets Prediction and analysis of drug molecules

Nucleotide Protein Biologicals Chemicals

GenomeAnnotation

ProteomeAnnotation

ProteinStruct.

TargetProteins

ProteinDrugs

Protein-drugInteraction

Drug-likeMolecules

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•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

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DrugInformatics

Searching and analyzing druggable targets Prediction and analysis of drug molecules

Nucleotide Protein Biologicals Chemicals

GenomeAnnotation

ProteomeAnnotation

ProteinStruct.

TargetProteins

ProteinDrugs

Protein-drugInteraction

Drug-likeMolecules

Work in Progress (Future Plan)

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

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DrugInformatics

Searching and analyzing druggable targets Prediction and analysis of drug molecules

Nucleotide Protein Biologicals Chemicals

GenomeAnnotation

ProteomeAnnotation

ProteinStruct.

TargetProteins

ProteinDrugs

Protein-drugInteraction

Drug-likeMolecules

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

1. QSAR models for ADMET

2. QSAR + docking for ADMET

3. Prediction of drug like molecules

4. Open access in Chemoinformatics

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DrugInformatics

Searching and analyzing druggable targets Prediction and analysis of drug molecules

Nucleotide Protein Biologicals Chemicals

GenomeAnnotation

ProteomeAnnotation

ProteinStruct.

TargetProteins

ProteinDrugs

Protein-drugInteraction

Drug-likeMolecules

Understanding Protein-Chemical InteractionPrediction 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

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