BTEVAL – Evaluation of beta-turn Prediction

BTEVAL

:: DESCRIPTION

The aim of BTEVAL server is to evaluate beta turn prediction algorithms on a uniform data set of 426 proteins or subsets of these proteins. It is the new data set in which no two protein chains have more that 25% sequence identity and each chain contains minimum one beta turn.

::DEVELOPER

Dr. G P S Raghava

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Kaur, H. and Raghava, G.P.S. (2003)
BTEVAL: A server for evaluation of beta-turn prediction methods.
Journal of Bioinformatics and Computational Biology 3(1), 495-504.

AlphaPred – Prediction of Alpha-turns in Proteins using Multiple Alignment and Secondary Structure Information

AlphaPred

:: DESCRIPTION

The AlphaPred server predicts the alpha turn residues in the given protein sequence. The method is based on the neural network training on PSI-BLAST generated position specific matrices and PSIPRED predicted secondary structure.

::DEVELOPER

Dr. G P S Raghava

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Kaur H & Raghava GP. (2004).
Prediction of alpha-turns in proteins using PSI-BLAST profiles and secondary structure information.
Proteins. 55: 83-90

GammaPred – Prediction of Gamma-turns in Proteins using Multiple Alignment and Secondary Structure Information

GammaPred

:: DESCRIPTION

The GammaPred server predicts the gamma turn residues in the given protein sequence. The method is based on the neural network training on PSI-BLAST generated position specific matrices and PSIPRED predicted secondary structure. Two neural networks with a single hidden layer have been used where the first sequence-to-structure network is trained on PSI-BLAST obtained position specific matrices. The filtering has been done by second structure-to-structure network trained on output of first net and PSIPRED predicted secondary structure. The training has been carried out using error backpropagation with a sum of square error function(SSE).

::DEVELOPER

Dr. G P S Raghava

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Kaur, H. and Raghava, G.P.S. (2003)
A neural network based method for prediction of gamma-turns in proteins from multiple sequence alignment.
Protein Science 12: 923-929.

APSSP2 – Advanced Protein Secondary Structure Prediction Server

APSSP2

:: DESCRIPTION

APSSP2 allows to predict the secondary structure of protein’s from their amino acid sequence.

::DEVELOPER

Dr. G P S Raghava

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Raghava, G. P. S. (2002)
APSSP2 : A combination method for protein secondary structure prediction based on neural network and example based learning.
CASP5. A-132.

SARpred – Prediction of real value of Surface Accessibility in proteins from Amino Acid Sequence

SARpred

:: DESCRIPTION

SARpred, a neural network based method predicts the real value of surface acessibility (SA) by using multiple sequence alignment. In this method, two feed forward, back-propagation networks are used. The first sequence-to-structure network is trained with PSI-BLAST generated position specific scoring matrices. Further, the initial predictions from the first network and PSIPRED predicted secondary structure are used as input to the second structure-to-structure network. The input is a single letter-code amino acid sequence in free format and output is a real value of surface accessiblity corresponding to the amino acid sequence.

::DEVELOPER

SARpred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Garg A, Kaur H & Raghava GP. (2005).
Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure information.
Proteins. 61: 318-24

BhairPred – Prediction of beta Hairpins in Proteins using ANN and SVM Techniques

BhairPred

:: DESCRIPTION

 BhairPred Predicts 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

::DEVELOPER

BhairPred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W154-9.
BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques.
Kumar M, Bhasin M, Natt NK, Raghava GP.

SRTpred – SVM based method for Prediction of Secrteory Proteins

SRTpred

:: DESCRIPTION

SRTpred classifies protein sequence as secretory or non-secretory proteins. It consists of different SVM modules based on different features of proteins such as compositions(Amino acid, physicochemical prperties, and dipeptide). In addition PSI-BLAST was also used to carry out similarity-based search. Finally a hybrid approach based SVM module was developed that encapsulates complete information of a protein sequence that is amino acid and dipeptide composiiton and PSI-BLAST. This module can classify the protein sequence between secretory and non-secrtory protein with an accuracy of 83%. Users have a choice to use any of these module for predcition of their query sequence.

::DEVELOPER

SRTpred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Garg, A. and Raghava, G. P. S. (2008)
A machine learning based method for the prediction of secretory proteins using amino acid composition, their order and similarity-search.
In Silico Biology 8:129-140.

BTXpred – Prediction of Bacterial Toxins

BTXpred

:: DESCRIPTION

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

::DEVELOPER

 BTXpred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

BTXpred: prediction of bacterial toxins.
Saha S, Raghava GP.
In Silico Biol. 2007;7(4-5):405-12.

MANGO – Prediction of Protein Function from Manually Annotated proteins based on GO (Gene Ontology)

MANGO

:: DESCRIPTION

MANGO is a server for predicting functional class of a protein. It predict function according to GO categories. The method is developed on protein in UNIPROT database whoes function have been assigned manually according to GO criteria.

::DEVELOPER

Dr. G P S Raghava,

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Raghava, G. P. S. (2006)
MANGO: prediction of Genome Ontology (GO) class of a protein from its amino acid and dipeptide composition using nearest neighbor approach.
CASP7: 93

DNAbinder – Prediction of DNA-binding Proteins

DNAbinder

:: DESCRIPTION

DNAbinder is a webserver developed for predicting DNA-binding proteins from their amino acid sequence using various compositional features of proteins.

::DEVELOPER

DNAbinder Team .

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2007 Nov 27;8:463.
Identification of DNA-binding proteins using support vector machines and evolutionary profiles.
Kumar M, Gromiha MM, Raghava GP.