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Dr. G.P.S.Raghava

Scientist and Head of BIC Centre

Institute of Microbial Technology

Sector 39-A, Chandigarh-160036 (India)

Phone: 172-2690557 or 172-2695215;

Fax:172-2690585 or 172-2690632

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Protein Structure Prediction Servers

This server predicts secondary structure of protein's from their amino acid sequence with high accuracy. It uses the multiple alignment, neural network and MBR techniques. This server participates in number of world wide competition like CASP, CAFASP and EVA (Raghava 2002; CASP5 A-31)

Prediction of structural class of proteins such as Alpha or Beta or Alpha+Beta or Alpha/Beta (Raghava 1999; J. Biosciences 24:176)

This server allow user to analysis of protein sequence and present the analysis in Graphical and Textual format. This allows property plots of 36 parameter (like Hydrophobicity Plot, Polarity, Charge) of single sequence and multiple sequence alignment (Raghava 2001; Biotech Software and Internet Report, 2:255)

It allows to predict top 5 similar fold in PDB (Protein DataBank) for a given protein sequence (query)

Benchmarking of Beta Turn prediction methods on-line via Internet (Kaur and Raghava 2002; Bioinformatics 18:1508-14). The user can see the performance of their method or exis ting methods (Kaur and Raghava 2003; Journal of Bioinformatics and Computational Biology 1:495-504)

Prediction of Beta Turns in Proteins using Neural Network and multiple alignment techniques. This is highly accurate method for beta turn prediction (Kaur and Raghava 2003; Protein Science 12:627).

Prediction of Alpha-turns in Proteins using Multiple Alignment and Secondary Structure Information (Kaur & Raghava 2004; Proteins 55:83-90).

A server for predicting Beta Turns in proteins using existing statistical methods. This allows consensus prediction from various methods (Kaur and Raghava 2002; Bioinformatics 18:498)

The CHpredict server predict two types of interactions: C-H...O and C-H...PI interactions. For C-H...O interaction, the server predicts the residues whose backbone Calpha atoms are involved in interaction with backbone oxygen atoms and for C-H...PI interactions, it predicts the residues whose backbone Calpha atoms are involved in interaction with PI ring system of side chain aromatic moieties.

A web server for predicting the aromatic backbone NH interaction in a given amino acid sequence where the pi ring of aromatic residues interact with the backbone NH groups. The method is based on t he neural network training on PSI-BLAST generated position specific matrices and PSIPRED predicted secondary structure (Kaur and Raghava 2004; Febs Lett. 564:47-57)

It predicts the whether a protein is outer membrane betat-barrel protein or not. It also predicts transmembrane Beta barrel regions in a given protein sequence. (Natt et al. 2004; Proteins 56:11-8).

This server predicts the beta turns and their types in a protein from its amino acid sequence (Kaur and Raghava 2004; Bioinformatics 20:2751-8) .

The Pepstr server predicts the tertiary structure of small peptides with sequence length varying between 7 to 25 residues. The prediction strategy is based on the realization that ?-turn is an important and consistent feature of small peptides in addition to regular structures.

Prediction of beta hairpins in proteins using ANN and SVM techniques. In this method secondary structure and multiple sequence alignment are used to predict the beta hairpins (Kumar et al. 2005; Nucleic Acids Res. 33:W154-9)

Prediction of real value of surface accessibility instead of buried or exposed resid ues in proteins from amino acid sequence (Garg et al. 2005; Proteins,

A bench-mark for evaluation of protein multiple sequence alignment accuracy (Raghava et al. 2003; BMC Bioinformatics 4-47).

Identification of Subunit Vaccine Candidates

The aim of this server is to predict MHC Class-I binding regions in an antigen sequence (Singh, H. and Raghava, G.P.S. (2003) Bioinformatics, 19: 1009)

The aim of this server is to predict MHC Class-II binding regions in an antigen sequence (Singh, H. and Raghava, G. P. S. (2001) Bioinformatics 17: 1236)

The BcePred server predict B cell epitope based on physio-chemical properties of amino acids. (Saha,S and Raghava GP (2004) ICARIS, LNCS 3239,197-204)

This server allow to predict binding peptide for 67 MHC Class I alleles. This also allow to predict the proteasome cleavage site and binding peptide that have cleavage site at C terminus (potential T cell epitopes). This uses the hybrid approach for prediction (Neural Network + Quantitative Matrix)

SVM and ANN based methods for predicting HLA-DRB1*0401 binding peptides in an Antigen Sequence (Bhasin, M. and Raghava, G.P.S. (2003) Bioinformatics 20:421).

ABCpred server is to predict linear B cell epitope regions in an antigen sequence, using artificial neural network. This server will assist in locating epitope regions that are useful in selecting synthetic vaccine candidates, disease diagonosis and also in allergy research.

Prediction of of MHC clas s I binders which can bind to wide range of MHC alleles with high affinity. This server has potential to develop sub-unit vaccine for large population (Bhasin, M., and Raghava, G.P.S. (2003) Hybridoma and Hybridomics 22: 229)

Prediction of binders for MHC class II alleles. This allows to predict promiscuous class binders, which can bind to large number of MHC Class II alleles.

Bcipep is a collection of the peptides having the role in Humoral immunity. The peptides in the database has varying measure of immunogenicity.This database can assist in the development of method for predicting B cell epitopes, desiginin g synthetic vaccines and in disease diagnosis. This databse is also launched by European Bioinformatics Institute (EBI) Hinxton, Cambridge, UK. (Saha S, Bhasin M, Raghava GP. (2005) BMC Genomics. 6(1):79)

This server allows server to predict proten (Immuno and standard) (Bhasin M, Raghava GP. (2005) Nucleic Acids Res.33(Web Server issue):W202-7)

Matrix Optimization Technique for Predicting MHC binding Core (Singh, H. and Raghava, G. P. S. (2002) Biotech Software and Internet Report, 3:146)

A server for predicting binders for MHC class I and II alleles. It also search these predicted binders in various genomes.

TAPPred is an on-line service for predicting binding affinity of peptides toward the TAP transporter. The Prediction is based on cascade SVM, using sequence and properties of the the amino acids (Bhasin, M. and Raghava, G. P. S. (2004) Protein Science 13:596-607).

It is an interface developed for evaluating the Major Histocompatibility Complex (MHC) binding peptide prediction algorithms.

The MHCBN is a curated database consisting of detailed information about Major Histocompatibility Complex (MHC) Binding,Non-binding peptides and T-cell epitopes.The version 3.1 of database provides information about peptides interacting with TAP and MHC linked autoimmune diseases This databse is also launched by European Bioinformatics Institute (EBI) Hinxton, Cambridge, UK. (Bhasin, M., Singh, H. and Raghava, G. P. S. (2003) Bioinformatics 19: 665).

Direct method of prediction of CTL Epitopes in an antigen sequence. This server utlize the machine learning techniques Support Vector Machine(SVM) and Aritificial Neural Network (ANN) for prediction (Bhasin, M. and Raghava, G. P. S. (2004) Vaccine 22:3195-204 )

This web-server allow to compute the endpoint titer and concentration of Antibody(Ab) or Antigen(Ag) from ELISA data(Raghava, G. P. S. and Agrewala, J. N. (2001) Biotech Software and Internet Report, 2:196). This server is based on graphical method developed for calculating Ab/Ag concentration (Raghava, G.P.S., Joshi, A.K. and Agrewala, J.N. (1992) J. Immunol. Methods 153, 263-264).

A database of hapten molecules which can not activate immune system but can stimulate immune response if attach with the carrier proteins.

This is a web server for analysis and comparison of two-dimensional electrophoresis (2-DE) Gel images. It helps in annotating the virual 2-D gel image proteins on the basis of known molecular weight andpH scales of the markers.

This is a SVM based method for predicting subcellular localization of Eukaryotic proteins using dipeptide composition and PSIBLAST generated pfofile Using this server user may know the function of their protein based on its location in cell. (Bhasin, M. and Raghava, G. P. S., (2004) Nucleic Acid Res. 32(Web Server issue):W414-9).

This is a SVM based tool for the classification of nuclear receptors on the basis of amino acid composition or dipeptide composition. The overall prediction accuracy of amino acid composition and dipeptide composition based methods is 82.6% and 97.2% (Bhasin, M. and Raghava, G. P. S., (2004) Journal of Biological Chemistry 279(22):23262-6)

This is a server forpredicting G-protein-coupled receptors and for classifying them in families and sub-families. This server can play vital role in drug design, as GPCR are commonly used as drug targets (Bhasin, M. and Raghava, G. P. S., (2004) Nucleic Acid Res. 32(Web Server issue):W383-9)

This is a dipeptide composition based method for predicting Amine Type of G-protein-coupled receptors. In this method type amine is predicted from dipeptide composition of proteins using SVM. (Bhasin M, Raghava GP. (2005) 33(Web Server issue):W143-7)

Comparison, management and access of 2D gel electrophoresis

This web-server allow to compute the length of DNA or protein fragments from its electropheric mobility using a graphical method (Raghava, G. P. S. (2001) Biotech Software and Internet Report, 2:198).

This server allows predicting the subcellulare localization of human proteins. This is based on various type of residue composition of proteins using SVM technique. (Garg A, Bhasin M, Raghava GP. J Biol Chem. (2005) 280(15):14427-32)

A method for subcellular localization proteins belongs to prokaryotic genomes. The pathogen play an important role in our life. (Bhasin M, Garg A, Raghava GP. Bioinformatics. (2005) 21(10):2522-4)

Prediction of manually annotated proteins in Genome Ontology (GO). This server is based on nea rest neighbor method (NNM).

The aim of BTXpred server is to predict bacterial toxins and its function from primary amino acid sequence.

This server predicts mitochondril proteins

This server classifies protein sequence as secretory or non-secretory proteins

It allows users to predic t hemoglobin protein

The aim of this server is to predict voltage gated ion-channels and classify them into sodium, potassium, calcium and chloride ion channels from primary amino sequences.

This server allows user to identify and visulaze the genes which have different expression level in normal and disease conditions.

This server allows user to analsis the expresion data (Microarray Data) where it calculate correlation coefficient between amino acid residue and gene expression level.

The aim of this server is to predict neurotoxins and it source and probable functions from primary amino acid sequences

This server aids in broad functional classification of bacterial proteins into virulence factors, information molecule, cellular process and metabolism molecule.

A web server for locating probable pro tein coding region in nucleotide sequence using fourier tranform approach (Issac, B., Singh, H., Kaur, H. and Raghava, G.P.S. (2002) Bioinformatics 18:196).

This server allows to predict gene (protein coding regions) in eukaryote genomes that includes introns and exons, using similarity aided (double) and consensus Ab Intion methods. (Issac B, Raghava GP. (2004) Genome Res. 14(9):1756-66)

A web server for predicting genes in a DNAsequence

A genome wide blast server. It allow user to search ther sequence against sequenced genomes and annonated proteomes. This integrate various tools which allows analysys of BLAST SEARCH

It is a support vector based approach to identify the protein coding regions in human genomic DNA

Spectral Repeat Finder (SRF) is a program to find repeats through an analysis of the power spectrum of a given DNA sequence. By repeat we mean the repeated occurrence of a segment of N nucleotides within a DNA sequence. SRF is an ab initio technique as no prior assumptions need to be made regarding either the repeat length, its fidelity, or whether the repeats are in tandem or no t (Sharma D, Issac B, Raghava GP, Ramaswamy R. (2004) Bioinformatics. 20(9):1405-12)

Genome Wise Sequence Similarity Search using FASTA. It allow user to search their sequence against sequenced genomes and their product proteome. This integrate various tools which allows analysys of FASTA search (Issac, B. and Ragha33:548-56).

A suite of datasets and tools for evaluating gene prediction methods.


MyPattern Finder is a program for detection of a 'motif' in DNA sequence by using an exact search method (Opti on A (1.0)) or an alignment technique (Option B (1.0)).