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

2 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 searchCOPID LGEpred: Expression of a gene from its Amino acid sequenceLGEpred: –BMC Bioinformatics 2005, 6:59 ECGpred: Expression from its nucleotide sequenceECGpred: 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

3 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: MITpredMITpred: Prediction of Mitochndrial proteins –Exclusive mitochndrial domain and SVM –J Biol Chem. 2005, 281: TbpredTbpred: Subcellular Localization of Mycobaterial Proteins (M.Tb) –BMC Bioinformatics 2007, Work in Progress: Subcellular localization of 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

4 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

5 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

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

7 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: Pprint: RNA interacting residues in proteins – Proteins: Structure, Function and Bioinformatics (In Press) GSTpred: 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

8 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

9 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

10 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

11 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 DMKpredDMKpred: 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

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