Computeraided Vaccine and Drug Discovery G'P'S' Raghava

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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

Vaccine Informatics
Drug Informatics
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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

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Pathogens/Invaders
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Endogenous Antigen Processing
ER
TAP
Prediction of CTL Epitopes (Cell-mediated
immunity)
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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)
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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 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

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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 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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BCIPEP A database of B-cell epitopes.
Saha et al.(2005) BMC Genomics 679. Saha et al.
(2006) NAR (Online)
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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,6540-48
• ALGpred Mapping and Prediction of Allergenic
  Epitopes
• Allergenic proteins
• IgE epitope and mapping
• Saha and Raghava (2006) Nucleic Acids Research
  34W202-W209

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HaptenDB A database of hapten molecules
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VAXIPRED A Software Package for Predicting
Subunit Vaccine Targets
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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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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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• 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

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• 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

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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, 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

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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, 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.

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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
  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)

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• 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

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• 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

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• 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

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• 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

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• 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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Thankyou