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

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Drug Informatics. 2. Limitations of methods of subunit vaccine design ... Bhasin and Raghava (2005) Nucleic Acids Research 33: W202-7 ... – PowerPoint PPT presentation

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


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

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
4
Endogenous Antigen Processing
ER
TAP
Prediction of CTL Epitopes (Cell-mediated
immunity)
5
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6
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)
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

9
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10
BCIPEP A database of B-cell epitopes.
Saha et al.(2005) BMC Genomics 679. Saha et al.
(2006) NAR (Online)
11
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

12
HaptenDB A database of hapten molecules
13
VAXIPRED A Software Package for Predicting
Subunit Vaccine Targets
14
PRRDB is a database of pattern recognition
receptors and their ligands
500 Pattern-recognition Receptors 228 ligands
(PAMPs) 77 distinct organisms 720 entries
15
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16
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

17
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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

29
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