Computer-aided Drug Discovery G.P.S. Raghava - PowerPoint PPT Presentation

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Computer-aided Drug Discovery G.P.S. Raghava

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Drug. Informatics. Searching and analyzing ... Nucleic Acids Research 2004, 32:W414-9. HSLpred: Sub cellular localization ... Nucleic Acids Research 33:W154-9 ... – PowerPoint PPT presentation

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Title: Computer-aided Drug Discovery G.P.S. Raghava


1
Computer-aided Drug Discovery G.P.S. Raghava
  • Annotation of genomes
  • Searching drug targets
  • Properties of drug molecules
  • Protein-chemical interaction
  • Prediction of drug-like molecules

Drug Informatics
2
  • 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

3
  • 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.
  • Tbpred Subcellular Localization of Mycobaterial
    Proteins (M.Tb)
  • BMC Bioinformatics 2007,
  • Work in Progress Subcellular localization of
    malaria

4
  • 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.

5
  • 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)

6

7
  • 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

8
  • 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

9
  • 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

10
  • 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

11
  • 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
  • DMKpred Prediction of binding affinity of drug
    molecules with kinase
  • Future Plan
  • Classification of proteins based on chemical
    interaction
  • Clustering drug molecules based on interaction
    with proteins

12
Thankyou
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