Computer Programs for Biological Problems: Is it Computer Programs for Biological Problems: Is it Service or Science ?Service or Science ?

G. P. S. RaghavaG. P. S. Raghava

Simple Computer ProgramsSimple Computer Programs Immunological methodsImmunological methods Methods in molecular biologyMethods in molecular biology Other methodsOther methods

Protein structure predictionProtein structure prediction Secondary & Supersecondary structure predictionSecondary & Supersecondary structure prediction Supersecondary and Tertiary Structure predictionSupersecondary and Tertiary Structure prediction

Immunoinformatics: Tools for computer-aided vaccine designImmunoinformatics: Tools for computer-aided vaccine design B-cell epitopeB-cell epitope T-cell epitopeT-cell epitope

Genome annotation: Gene and Repeat predictionGenome annotation: Gene and Repeat prediction Functional annotation of proteinsFunctional annotation of proteins

Subcellular localizationSubcellular localization Classification of receptorsClassification of receptors Analysis of Microarray DataAnalysis of Microarray Data

Work in Progress & FutureWork in Progress & Future

Immunological MethodsImmunological Methods

Computation of Ab/Ag Concentration from EISA dataComputation of Ab/Ag Concentration from EISA data Graphical MethodGraphical Method Raghava et al., 1992, J. Immuno. Methods Raghava et al., 1992, J. Immuno. Methods 153: 263153: 263

Determination of affinity of Monoclonal AntibodyDetermination of affinity of Monoclonal Antibody Using non-competitive ELISAUsing non-competitive ELISA Serial dilution of both Ab and Ag concentration Serial dilution of both Ab and Ag concentration Law of mass equationLaw of mass equation Raghava and Agrewala (1994) J. Immunoassay, 15: 115Raghava and Agrewala (1994) J. Immunoassay, 15: 115

Measurement and computation of IL-4 and Interfron-Measurement and computation of IL-4 and Interfron- Ability to induce IgG1 and IgG2Ability to induce IgG1 and IgG2 Agrewala et al. 1994, J. Immunoassay, Agrewala et al. 1994, J. Immunoassay, 14: 8314: 83

Computer programs in GW-BASIC for PC, freely availableComputer programs in GW-BASIC for PC, freely available

Methods in Molecular BiologyMethods in Molecular Biology

GMAP: A program for mapping potential restriction sitesGMAP: A program for mapping potential restriction sites RE sites in ambiguous and non-ambiguous DNA sequenceRE sites in ambiguous and non-ambiguous DNA sequence Minimum number of silent mutations required for introducing a RE sitesMinimum number of silent mutations required for introducing a RE sites Set theory for searching RE sitesSet theory for searching RE sites Raghava and Sahni (1994) Biotechniques 16:1116Raghava and Sahni (1994) Biotechniques 16:1116

DNASIZE: Improved estimation of DNA size from Gel ElectrophoresisDNASIZE: Improved estimation of DNA size from Gel Electrophoresis Graphical method to improved predictionGraphical method to improved prediction Raghava (1994) Biotechniques 17:100Raghava (1994) Biotechniques 17:100

DNAOPT: Optimization of gel conditions of gel electrophoresis and DNAOPT: Optimization of gel conditions of gel electrophoresis and SDS-PAGESDS-PAGE

Optimization of gel conditionsOptimization of gel conditions Sufficient distance between two fragmentsSufficient distance between two fragments Small fragment in rangeSmall fragment in range Raghava (1995) Biotechniques 18:274Raghava (1995) Biotechniques 18:274

Other MethodsOther Methods Hemolytic potency of drugsHemolytic potency of drugs

Raghava et al., (1994) Biotechniques 17: 1148Raghava et al., (1994) Biotechniques 17: 1148

FPMAP: methods for classification and identification of microorganisms FPMAP: methods for classification and identification of microorganisms 16SrRNA16SrRNA

graphical display of restriction and fragment map of genes; graphical display of restriction and fragment map of genes; compare the restriction and fragment map of genes compare the restriction and fragment map of genes generate the fragment map of sequences in PHYLIP formatgenerate the fragment map of sequences in PHYLIP format Raghava et al., (2000) Biotechniques 29:108-115 Raghava et al., (2000) Biotechniques 29:108-115

Nihalani, D., Nihalani, D., Raghava, G.P.S Raghava, G.P.S and Sahni, G (1997). Mapping of the and Sahni, G (1997). Mapping of the plasminogen binding site of streptokinase with short synthetic plasminogen binding site of streptokinase with short synthetic peptides. peptides. Protein Science, 6:1284-92.Protein Science, 6:1284-92.

Sarin,J., Sarin,J., Raghava, G. P. S. Raghava, G. P. S. and Chakraborti, P. K. (2003) Intrinsic and Chakraborti, P. K. (2003) Intrinsic contributions of polar amino acid residues towards thermal stability of contributions of polar amino acid residues towards thermal stability of an ABC-ATPase of mesophilic origin. an ABC-ATPase of mesophilic origin. Protein Science Protein Science 12:2118-212012:2118-2120

Protein Structure PredictionProtein Structure Prediction Regular Secondary Structure Prediction (Regular Secondary Structure Prediction (-helix -helix -sheet)-sheet)

APSSP2: APSSP2: Highly accurate method for secondary structure predictionHighly accurate method for secondary structure prediction Participate in all competitions like EVA, CAFASP and CASP (In top 5 methods)Participate in all competitions like EVA, CAFASP and CASP (In top 5 methods) Combines memory based reasoning ( MBR) and ANN methodsCombines memory based reasoning ( MBR) and ANN methods

Irregular secondary structure prediction methods (Tight turns)Irregular secondary structure prediction methods (Tight turns) BetatpredBetatpred: : Consensus method for Consensus method for -turns prediction -turns prediction

• Statistical methods combinedStatistical methods combined

• Kaur and Raghava (2001) BioinformaticsKaur and Raghava (2001) Bioinformatics

BtevalBteval : Benchmarking of : Benchmarking of -turns prediction-turns prediction

• Kaur and Raghava (2002) J. Bioinformatics and Computational Biology, 1:495:504Kaur and Raghava (2002) J. Bioinformatics and Computational Biology, 1:495:504 BetaTpred2BetaTpred2: Highly accurate method for predicting : Highly accurate method for predicting -turns (ANN, SS, MA)-turns (ANN, SS, MA)

• Multiple alignment and secondary structure informationMultiple alignment and secondary structure information

• Kaur and Raghava (2003) Kaur and Raghava (2003) Protein Sci 12:627-34Protein Sci 12:627-34 BetaTurns: Prediction of BetaTurns: Prediction of -turn ty-turn types in proteins pes in proteins

• Evolutionary information Evolutionary information

• Kaur and Raghava (2004) Kaur and Raghava (2004) BioinformaticsBioinformatics 20:2751-8. 20:2751-8. AlphaPred: Prediction of AlphaPred: Prediction of -turns i-turns in proteinsn proteins

• Kaur and Raghava (2004) Proteins: Structure, Function, and Genetics 55:83-90Kaur and Raghava (2004) Proteins: Structure, Function, and Genetics 55:83-90 GammaPred: Prediction of GammaPred: Prediction of -turns i-turns in proteinsn proteins

• Kaur and Raghava (2004) Kaur and Raghava (2004) Protein Science; 12:923-929.Protein Science; 12:923-929.

Protein Structure PredictionProtein Structure Prediction

BhairPredBhairPred: Prediction of Supersecondary structure prediction: Prediction of Supersecondary structure prediction Prediction of Beta HairpinsPrediction of Beta Hairpins Utilize ANN and SVM pattern recognition techniquesUtilize ANN and SVM pattern recognition techniques Secondary structure and surface accessibility used as inputSecondary structure and surface accessibility used as input Manish et al. (2005) Nucleic Acids Research (In press)Manish et al. (2005) Nucleic Acids Research (In press)

TBBpred: TBBpred: Prediction of outer membrane proteinsPrediction of outer membrane proteins Prediction of trans membrane beta barrel proteinsPrediction of trans membrane beta barrel proteins Prediction of beta barrel regionsPrediction of beta barrel regions Application of ANN and SVM + Evolutionary informationApplication of ANN and SVM + Evolutionary information Natt et al. (2004) Proteins: 56:11-8

ARNHpred:ARNHpred: Analysis and prediction side chain, backbone interactions Analysis and prediction side chain, backbone interactions Prediction of aromatic NH interactionsPrediction of aromatic NH interactions

Kaur and Raghava (2004) FEBS Letters 564:47-57 . SARpredSARpred: Prediction of surface accessibility (real accessibility): Prediction of surface accessibility (real accessibility)

Multiple alignment (PSIBLAST) and Secondary structure information Multiple alignment (PSIBLAST) and Secondary structure information ANN: Two layered network (sequence-structure-structureANN: Two layered network (sequence-structure-structure)) Garg et al., (2005) Proteins (In Press)Garg et al., (2005) Proteins (In Press)

PepStrPepStr: Prediction of tertiary structure of Bioactive peptides: Prediction of tertiary structure of Bioactive peptidesPerformance of SARpred, Pepstr and BhairPred were checked on CASP6 proteinsPerformance of SARpred, Pepstr and BhairPred were checked on CASP6 proteins

Immunoinformatics: Tools for computer-aided Immunoinformatics: Tools for computer-aided vaccine designvaccine design

Concept of vaccine and DrugConcept of vaccine and Drug Drug:Drug: Kill invaders/pathogens and/or Inhibit the growth of Kill invaders/pathogens and/or Inhibit the growth of

pathogenspathogens Vaccine:Vaccine: Trained immune system to face various existing disease Trained immune system to face various existing disease

agentsagents Type of VaccinesType of Vaccines

Whole Organism of Pathogen (MTb, 4000 proteins)Whole Organism of Pathogen (MTb, 4000 proteins) Target proteins/antigens which can activate immune systemTarget proteins/antigens which can activate immune system Subunit Vaccine: Antigenic regions which can simulate T and B cell Subunit Vaccine: Antigenic regions which can simulate T and B cell

responseresponse Limitations of present methods of subunit vaccine designLimitations of present methods of subunit vaccine design

Developed for one or two MHC alleles (not suitable for large Developed for one or two MHC alleles (not suitable for large population)population)

Do not consider pathways of antigen processingDo not consider pathways of antigen processing No single source of known epitopesNo single source of known epitopes

Initiatives taken by BIC at IMTECHInitiatives taken by BIC at IMTECH In 2000, BIC take initiative to overcome some of limitations In 2000, BIC take initiative to overcome some of limitations To understand complete mechanism of antigen processingTo understand complete mechanism of antigen processing Develop comprehensive databasesDevelop comprehensive databases

Immunoinformatics: ConceptImmunoinformatics: Concept

Immunoinformatics: Databases DevelopedImmunoinformatics: Databases Developed

MHCBN A comprehensive database of mhc binding/

non-binding peptides, TAP binders and T-cell epitopes

Largest database of T-cell epitopes ( > 24,000 peptides)

A set of data analysis tools e.g immunological BLAST, peptide mapping.

Bhasin et al. (2003) Bioinformatics 19:665

Bcipep A database B cell epitopes

Reference database of 3000 B cell epitopes.

Hyperlinked to sequence databases

Facilitate the mapping of T cell epitopes on B cell epitopes.

Saha et al. (2005) BMC Genomics

Both databases distributed by European Bioinformatics Institute (EBI), UK. Only databases from India distributed by EBI

Immunoinformatics: Prediction of CTL EpitopesImmunoinformatics: Prediction of CTL Epitopes

Propred1Propred1: Promiscuous binders for 47 : Promiscuous binders for 47 MHC class I allelesMHC class I alleles

Cleavage site at C-terminalCleavage site at C-terminal Singh and Raghava (2003) Bioinformatics Singh and Raghava (2003) Bioinformatics

19:110919:1109 nHLApred: nHLApred: Promiscuous binders for 67 Promiscuous binders for 67

alleles using ANN and QMalleles using ANN and QM TAPpred: TAPpred: Analysis and prediction of TAP Analysis and prediction of TAP

bindersbinders Bhasin and Raghava (2004) Protein Science 13:596Bhasin and Raghava (2004) Protein Science 13:596

PcleavagePcleavage: Proteasome and Immuno-: Proteasome and Immuno-proteasome cleavage site.proteasome cleavage site.

Trained and test on in vitro and in vivo dataTrained and test on in vitro and in vivo data Bhasin and Raghava (2005) NAR (In Press)Bhasin and Raghava (2005) NAR (In Press)

CTLpred: CTLpred: Direct method for CTL EpitopesDirect method for CTL Epitopes Can discriminate CTL epitopes and Non-Can discriminate CTL epitopes and Non-

epitope MHC class I bindersepitope MHC class I binders Bhasin and Raghava (2004) Vaccine Bhasin and Raghava (2004) Vaccine

22:319522:3195

Immunoinformatics: T Helper EpitopesImmunoinformatics: T Helper Epitopes

PropredPropred: Promiscuous of binders for 51 MHC Class II binders : Promiscuous of binders for 51 MHC Class II binders Virtual matricesVirtual matrices Singh and Raghava (2001) Bioinformatics 17:1236Singh and Raghava (2001) Bioinformatics 17:1236

HLADR4predHLADR4pred: Prediction of : Prediction of HLA-DRB1*0401 binding peptides Dominating MHC class II allele ANN and SVM techniques Bhasin and Raghava (2004) Bioinformatics 12:421.

MHC2Pred: MHC2Pred: Prediction of MHC class II binders for 41 allelesPrediction of MHC class II binders for 41 alleles Human and mouseHuman and mouse Support vector machine (SVM) techniqueSupport vector machine (SVM) technique Extension of HLADR4predExtension of HLADR4pred

MMBpredMMBpred: Prediction pf Mutated MHC Binder : Prediction pf Mutated MHC Binder MutationsMutations required to increase affinity required to increase affinity Mutation required for make a binder promiscuousMutation required for make a binder promiscuous Bhasin and Raghava (2003) Bhasin and Raghava (2003) Hybrid Hybridomics, 22:229

MOT : Matrix optimization technique for binding core MHCBench: Benchmarting of methods for MHC binders

Immunoinformatics: B-cell EpitopesImmunoinformatics: B-cell Epitopes

BCEpred: BCEpred: Prediction of Continuous B-cell epitopesPrediction of Continuous B-cell epitopes Benchmarking of existing methodsBenchmarking of existing methods Evaluation of Physico-chemical properties Evaluation of Physico-chemical properties Poor performance slightly better than randomPoor performance slightly better than random Combine all properties and achieve accuracy around 58%Combine all properties and achieve accuracy around 58% Saha and Raghava (2004) ICARIS 197-204Saha and Raghava (2004) ICARIS 197-204..

ABCpred: ANN based method for B-cell epitope predictionABCpred: ANN based method for B-cell epitope prediction Extract all epitopes from BCIPEP (around 2400)Extract all epitopes from BCIPEP (around 2400) 700 non-redundant epitopes used for testing and training700 non-redundant epitopes used for testing and training Recurrent neural networkRecurrent neural network Accuracy 66% achievedAccuracy 66% achieved

Genome annotation: Gene/Repeat predictionGenome annotation: Gene/Repeat prediction

FTGpred: FTGpred: Prediction of Prokaryotic genesPrediction of Prokaryotic genes

Ab initio method for gene predictionAb initio method for gene prediction Based on FFT techniqueBased on FFT technique Issac et al. (2002) Bioinformatics 18:197Issac et al. (2002) Bioinformatics 18:197

EGpred: EGpred: Prediction of eukaryotic genesPrediction of eukaryotic genes BLASTX search against RefSeq database BLASTN search against intron database probable intron and exon regions are compared to filter/remove wrong exons; NNSPLICE program is used to reassign splicing signal site positions finally ab initio predictions are combined with exons derived Issac and Raghava (2004) Genome Research 14:1756

GeneBench: Benchmarking of gene finders Collection of different datasets Tools for evaluating a method Creation of own datasets

SRF: SRF: Spectral Repeat finderSpectral Repeat finder FFT based repeat finderFFT based repeat finder Sharma et al. (2004) Bioinformatics 20: 1405Sharma et al. (2004) Bioinformatics 20: 1405

Genome annotationGenome annotation: Comparative genomics: Comparative genomics

GWFASTAGWFASTA:: Genome Wide FASTA Search Genome Wide FASTA Search

Standard FASTA search against nucleotide and protein sequences databasesStandard FASTA search against nucleotide and protein sequences databases Search against nucleotide sequences of genomes (finished/unfinished)Search against nucleotide sequences of genomes (finished/unfinished) Search against protein sequences of proteomes (annotated only)Search against protein sequences of proteomes (annotated only) Issac and Raghava (2002) Biotechniques 33:548Issac and Raghava (2002) Biotechniques 33:548

GWBLAST: GWBLAST: Genome wideGenome wideBLAST searchBLAST search

Functional annotation of proteins: Functional annotation of proteins: Subcellular localizationSubcellular localization

PSLpred: PSLpred: Sub cellular localization of prokaryotic proteinsSub cellular localization of prokaryotic proteins

5 major sub cellular localization SVM based method Accuracy of classification of final model 91% Bhasin and Raghava (2005) Bioinformatics 21: 2522

ESLpred: ESLpred: Subcellular localization of Eukaryotic proteinsSubcellular localization of Eukaryotic proteins SVM based method Amino acid, Dipetide and properties composition Sequence profile (PSIBLAST) Bhasin and Raghava (2004) Bhasin and Raghava (2004) Nucleic Acids Research 32:W414.

HSLpred: Sub cellular localization of Human proteinsHSLpred: Sub cellular localization of Human proteins Need to develop organism specific methods Proteins belongs to same location have same type of composition Higher eukaryote proteins are different than lower eukaryote in same location 84% accuracy for human proteins Garg et al. (2005) Journal of Biological Chemistry 280:14427-

Functional annotation of proteins: Classification Functional annotation of proteins: Classification of receptorsof receptors

NrpredNrpred: Classification of nuclear receptors: Classification of nuclear receptors BLAST can easily identify the NR proteins (6 conserved domains) BLAST fails in classification of NR proteins SVM based method developed to identify four class of NR proteins Uses composition of amino acids Bhasin and Raghava (2004) Bhasin and Raghava (2004) Journal of Biological Chemistry 279: 23262

GPCRpredGPCRpred: : Prediction of Families and Subfamilies of G-protein-coupled Prediction of Families and Subfamilies of G-protein-coupled receptorsreceptors

Predict GPCR proteins & class > 80% in Class A, further classify

Bhasin and Raghava(2004) Bhasin and Raghava(2004) Nucleic Acids Research 32:W383

GPCRsclassGPCRsclass: : Amine type of GPCR Major drug targets, 4 classes, Accuracy 96.4%Major drug targets, 4 classes, Accuracy 96.4%

Acetylcholine; adrenoceptor; dopamine; serotoninAcetylcholine; adrenoceptor; dopamine; serotonin Bhasin and Raghava(2005) Nucleic Acids Research (In press)

Functional annotation of proteins: Analysis of Functional annotation of proteins: Analysis of Microarray dataMicroarray data

LGEpred: Prediction of gene expression from amino acid composition of its LGEpred: Prediction of gene expression from amino acid composition of its proteinsproteins

Analyze gene expression of Saccharomyces cerevisiae Positive correlation between composition (Ala, Gly, Arg & Val) gene expression Negative correlation for Asp, Leu, Asn & Ser SVM based method for prediction of gene expression Correlation 0,72, between predicted and actual expression Amino acid composition with expression profile improves accuracy of function prediction Membrane proteins have poor correlation between A.A. composition and expression Raghava and Han (2005) BMC Bioinformatics 6:1057Raghava and Han (2005) BMC Bioinformatics 6:1057

Correlation and prediction of gene expression from its nucleotide compositionCorrelation and prediction of gene expression from its nucleotide composition Composition of G, C and G+C shows positive correlation with gene expressionComposition of G, C and G+C shows positive correlation with gene expression Negative correlation for A, T and A+TNegative correlation for A, T and A+T Inverse correlation between composition of a nucleotide at genome level Inverse correlation between composition of a nucleotide at genome level Correlation 0.87, between predicted and experimentallyCorrelation 0.87, between predicted and experimentally

Gene expression from codon biasness in gene and genomeGene expression from codon biasness in gene and genome Major codon shows positive correlationMajor codon shows positive correlation Correlation 0.85 between predicted and actual expressionCorrelation 0.85 between predicted and actual expression

Limitations: Only predict gene expression in a given condition, trained on one condition will not work in other condition

Summary of Major PublicationsNumber of PublicationName of Journal Impact Factor of

Journal (ISI 2003) In Last 5Years

Total

Genome Research 9.6 1 1Bioinformatics 6.7 10 10Nucleic Acids Res. 6.6 9 9Journal Biol. Chemistry 6.5 2 2BMC Bioinformatics 4.9* 2 2Proteins 4.3 3 3Protein Science 3.8 4 5FEBS Lett. 3.6 1 1Vaccine 3.0 1 1BMC Genomics 3.0* 1 1J. Immuno. Methods 2.8 0 1Biotechniques 2.4 2 6Others - 5 11

* Unofficial Impact factor of 2003

Work in ProgressWork in Progress BTXpred: Prediction of bacterial toxinsBTXpred: Prediction of bacterial toxins NTXpred: Classification of neurotoxinsNTXpred: Classification of neurotoxins Mitpred: Prediction of mitochondrial proteinsMitpred: Prediction of mitochondrial proteins SRTpred: Identification of classical and non-classical secretory SRTpred: Identification of classical and non-classical secretory

proteinsproteins AC2Dgel: Analysis and comparison of 2D gelsAC2Dgel: Analysis and comparison of 2D gels VICMPred: Prediction of gram negative bacterial functional proteins VICMPred: Prediction of gram negative bacterial functional proteins HLA_Affi: Prediction affinity (real value) of HLA-A2 bindersHLA_Affi: Prediction affinity (real value) of HLA-A2 binders HaptenDB: Database of HaptensHaptenDB: Database of Haptens Functional annotation of Malaria Functional annotation of Malaria

AcknowledgementAcknowledgement Colleagues & CollobratorsColleagues & Collobrators

G. C. VarshneyG. C. Varshney Girish SahniGirish Sahni J. N. AgrewalaJ. N. Agrewala Amit GhoshAmit Ghosh Chetan PremaniChetan Premani Balvinder SinghBalvinder Singh Pradip ChakrabortiPradip Chakraborti Pushpa AgrawalPushpa Agrawal G. C. MishraG. C. Mishra Anish JoshiAnish Joshi

PhD StudentsPhD Students Harpreet SinghHarpreet Singh Harpreert KaurHarpreert Kaur Manoj BhasinManoj Bhasin Sudipto SahaSudipto Saha Manish KumarManish Kumar Sneh LataSneh Lata

Project assistants and StaffProject assistants and Staff Aarti GargAarti Garg Navjyot K. NattNavjyot K. Natt Amit KushAmit Kush Rajesh SolankiRajesh Solanki Mahender SinghMahender Singh Ruchi VermaRuchi Verma