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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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Presentation on theme: "1 Computer-aided Vaccine and Drug Discovery G.P.S. Raghava Understanding immune system Breaking complex problem Adaptive immunity Innate Immunity Vaccine."— Presentation transcript:

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2 1 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

3 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 Innate ImmunityVaccine Delivery Protective AntigensAdaptive Immunity Bioinformatics Centre IMTECH, Chandigarh WHOLE ORGANISM Attenuated Epitopes (Subunit Vaccine) Purified Antigen T cell epitope

4 3 Pathogens/Invaders Disease Causing Agents Innate ImmunityVaccine Delivery Protective AntigensAdaptive Immunity Bioinformatics Centre IMTECH, Chandigarh

5 4 Prediction of CTL Epitopes (Cell-mediated immunity) ER TAP Innate ImmunityVaccine Delivery Protective AntigensAdaptive Immunity Bioinformatics Centre IMTECH, Chandigarh

6 5 Innate ImmunityVaccine Delivery Protective AntigensAdaptive Immunity Bioinformatics Centre IMTECH, Chandigarh

7 6 Innate ImmunityVaccine Delivery Protective AntigensAdaptive Immunity Bioinformatics Centre IMTECH, 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 epitopes Highly Cited ( ~ 70 citations)

8 7 Prediction of MHC II Epitopes ( T helper Epitopes) Propred: Promiscuous of binders for 51 MHC Class II bindersPropred: –Virtual matrices –Singh and Raghava (2001) Bioinformatics 17:1236 HLADR4pred: Prediction of HLA-DRB1*0401 binding peptidesHLADR4pred –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 allelesMHC2Pred: –Human and mouse –Support vector machine (SVM) technique –Extension of HLADR4pred MMBpred: Prediction pf Mutated MHC BinderMMBpred –Mutations required to increase affinityMutations –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 ImmunityVaccine Delivery Protective AntigensAdaptive Immunity Bioinformatics Centre IMTECH, Chandigarh

9 8 Prediction of MHC I binders and CTL Epitopes Propred1Propred1: Promiscuous binders for 47 MHC class I alleles –Cleavage site at C-terminal –Singh and Raghava (2003) Bioinformatics 19:1109 nHLApred: nHLApred: Promiscuous binders for 67 alleles using ANN and QM –Bhasin and Raghava (2007) J. Biosci. 32:31-42 TAPpred: TAPpred: Analysis and prediction of TAP binders –Bhasin and Raghava (2004) Protein Science 13:596 PcleavagePcleavage: 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: CTLpred: Direct method for Predicting CTL Epitopes –Bhasin and Raghava (2004) Vaccine 22:3195 Innate ImmunityVaccine Delivery Protective AntigensAdaptive Immunity Bioinformatics Centre IMTECH, Chandigarh

10 9 Innate ImmunityVaccine Delivery Protective AntigensAdaptive Immunity Bioinformatics Centre IMTECH, Chandigarh

11 10 Innate ImmunityVaccine Delivery Protective AntigensAdaptive Immunity Bioinformatics Centre IMTECH, Chandigarh Saha et al.(2005) BMC Genomics 6:79. Saha et al. (2006) NAR (Online) BCIPEP: A database of B-cell epitopes.

12 11 Prediction of B-Cell Epitopes BCEpred: Prediction of Continuous B-cell epitopesBCEpred: –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 ABCpred: ANN based method for B-cell epitope predictionABCpred: –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 EpitopesALGpred: –Allergenic proteins –IgE epitope and mapping –Saha and Raghava (2006) Nucleic Acids Research 34:W202-W209 Innate ImmunityVaccine Delivery Protective AntigensAdaptive Immunity Bioinformatics Centre IMTECH, Chandigarh

13 12 HaptenDB: A database of hapten molecules Innate ImmunityVaccine Delivery Protective AntigensAdaptive Immunity Bioinformatics Centre IMTECH, Chandigarh

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15 14 Innate ImmunityVaccine Delivery Protective AntigensAdaptive 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

16 15 Innate ImmunityVaccine Delivery Protective AntigensAdaptive Immunity Bioinformatics Centre IMTECH, Chandigarh

17 16 Innate ImmunityVaccine Delivery Protective AntigensAdaptive 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

18 17 Drug Informatics Searching and analyzing druggable targetsPrediction and analysis of drug molecules NucleotideProtein Biologicals Chemical s Genome Annotation Proteome Annotation Protein Struct. Target Proteins Protein Drugs Protein-drug Interaction Drug-like Molecules

19 18 FTGpred: Prediction of Prokaryotic genesFTGpred: –Ab initio method for gene prediction using FFT technique –Issac et al. (2002) Bioinformatics 18:197 EGpred: Prediction of eukaryotic genesEGpred: –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 findersGeneBench: –Collection of different datasets –Tools for evaluating a method –Creation of own datasets SRF: Spectral Repeat finderSRF: –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 Drug Informatics Searching and analyzing druggable targetsPrediction and analysis of drug molecules NucleotideProtein Biologicals Chemical s Genome Annotation Proteome Annotation Protein Struct. Target Proteins Protein Drugs Protein-drug Interaction Drug-like Molecules

20 19 Comparative genomics GWFASTA: Genome Wide FASTA SearchGWFASTA: –Analysis of FASTA search for comparative genomics –Biotechniques 2002, 33:548 GWBLAST: Genome wide BLAST searchGWBLAST: 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 Drug Informatics Searching and analyzing druggable targetsPrediction and analysis of drug molecules NucleotideProtein Biologicals Chemical s Genome Annotation Proteome Annotation Protein Struct. Target Proteins Protein Drugs Protein-drug Interaction Drug-like Molecules

21 20 Subcellular localization Methods PSLpred: PSLpred: Subcellular localization of prokaryotic proteins –5 major sub cellular localization –Bioinformatics 2005, 21: 2522 ESLpred: 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 HSLpred –Need to develop organism specific methods –84% accuracy for human proteins –Journal of Biological Chemistry 2005, 280: MITpred: Prediction of Mitochndrial proteins –Exclusive mitochndrial domain and SVM –J Biol Chem. 2005, 281: Work in Progress: Subcellular localization of M.Tb. and malaria Drug Informatics Searching and analyzing druggable targetsPrediction and analysis of drug molecules NucleotideProtein Biologicals Chemical s Genome Annotation Proteome Annotation Protein Struct. Target Proteins Protein Drugs Protein-drug Interaction Drug-like Molecules

22 21 Regular Secondary Structure Prediction ( -helix -sheet) APSSP2: Highly accurate method for secondary structure predictionAPSSP2: Competete in EVA, CAFASP and CASP (In top 5 methods) Irregular secondary structure prediction methods (Tight turns) Betatpred: Consensus method for -turns predictionBetatpred –Statistical methods combined –Kaur and Raghava (2001) Bioinformatics Bteval : Benchmarking of -turns predictionBteval –Kaur and Raghava (2002) J. Bioinformatics and Computational Biology, 1:495:504 BetaTpred2: Highly accurate method for predicting -turns (ANN, SS, MA)BetaTpred2 –Multiple alignment and secondary structure information –Kaur and Raghava (2003) Protein Sci 12: BetaTurns: Prediction of -turn types in proteinsBetaTurns –Kaur and Raghava (2004) Bioinformatics 20: AlphaPred: Prediction of -turns in proteinsAlphaPred –Kaur and Raghava (2004) Proteins: Structure, Function, and Genetics 55:83-90 GammaPred: Prediction of -turns in proteinsGammaPred –Kaur and Raghava (2004) Protein Science; 12: Drug Informatics Searching and analyzing druggable targetsPrediction and analysis of drug molecules NucleotideProtein Biologicals Chemical s Genome Annotation Proteome Annotation Protein Struct. Target Proteins Protein Drugs Protein-drug Interaction Drug-like Molecules

23 22 Supersecondary Structure BhairPred: Prediction of Beta HairpinsBhairPred –Secondary structure and surface accessibility used as input –Manish et al. (2005) Nucleic Acids Research 33:W154-9 TBBpred: 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:ARNHpred: Analysis and prediction side chain, backbone interactions –Prediction of aromatic NH interactions –Kaur and Raghava (2004) FEBS Letters 564: ChpredictChpredict: Prediction of C-H.. O and PI interaction –Kaur and Raghava (2006) In-Silico Biology 6:0011 SARpredSARpred: Prediction of surface accessibility (real accessibility) –Multiple alignment (PSIBLAST) and Secondary structure information –Garg et al., (2005) Proteins 61: Secondary to Tertiary Structure PepStrPepStr: Prediction of tertiary structure of Bioactive peptides –Kaur et al. (2007) Protein Pept Lett. (In Press) Drug Informatics Searching and analyzing druggable targetsPrediction and analysis of drug molecules NucleotideProtein Biologicals Chemical s Genome Annotation Proteome Annotation Protein Struct. Target Proteins Protein Drugs Protein-drug Interaction Drug-like Molecules

24 23 Drug Informatics Searching and analyzing druggable targetsPrediction and analysis of drug molecules NucleotideProtein Biologicals Chemical s Genome Annotation Proteome Annotation Protein Struct. Target Proteins Protein Drugs Protein-drug Interaction Drug-like Molecules

25 24 Nrpred Nrpred: Classification of nuclear receptors –BLAST fails in classification of NR proteins –Uses composition of amino acids Journal of Biological Chemistry 2004, 279: GPCRpredGPCRpred: Prediction of G-protein-coupled receptors –Predict GPCR proteins & class –> 80% in Class A, further classify Nucleic Acids Research 2004, 32:W383 GPCRsclassGPCRsclass: 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: Drug Informatics Searching and analyzing druggable targetsPrediction and analysis of drug molecules NucleotideProtein Biologicals Chemical s Genome Annotation Proteome Annotation Protein Struct. Target Proteins Protein Drugs Protein-drug Interaction Drug-like Molecules

26 25 Drug Informatics Searching and analyzing druggable targetsPrediction and analysis of drug molecules NucleotideProtein Biologicals Chemical s Genome Annotation Proteome Annotation Protein Struct. Target Proteins Protein Drugs Protein-drug 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:W 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

27 26 Drug Informatics Searching and analyzing druggable targetsPrediction and analysis of drug molecules NucleotideProtein Biologicals Chemical s Genome Annotation Proteome Annotation Protein Struct. Target Proteins Protein Drugs Protein-drug Interaction Drug-like Molecules 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: ) 5.Analysis and prediction of druggable proteins/peptide

28 27 Drug Informatics Searching and analyzing druggable targetsPrediction and analysis of drug molecules NucleotideProtein Biologicals Chemical s Genome Annotation Proteome Annotation Protein Struct. Target Proteins Protein Drugs Protein-drug 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 Future Plan 1.QSAR models for ADMET 2.QSAR + docking for ADMET 3.Prediction of drug like molecules 4.Open access in Chemoinformatics

29 28 Drug Informatics Searching and analyzing druggable targetsPrediction and analysis of drug molecules NucleotideProtein Biologicals Chemical s Genome Annotation Proteome Annotation Protein Struct. Target Proteins Protein Drugs Protein-drug 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 Future Plan Classification of proteins based on chemical interaction Clustering drug molecules based on interaction with proteins

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