Tag: Peptide

PEPstrMOD – Peptide Tertiary Structure Prediction with Natural, Non-natural Modified Residues

PEPstrMOD

:: DESCRIPTION

The PEPstrMOD server predicts the tertiary structure of small peptides with sequence length varying between 7 to 25 residues. It also handles peptides having various modifications like non-natural residues, terminal modifications (Acetylation/Amidation), Cyclization (N-C, disulfide bridges), conversion of L- to D- amino acids, post translational modifications, etc. The prediction strategy is based on the realization that β-turn is an important and consistent feature of small peptides in addition to regular structures. Thus, the method uses both the regular secondary structure information predicted from PSIPRED and β-turns information predicted from BetaTurns. The structure is further refined with energy minimization and molecular dynamic simulations.

::DEVELOPER

PEPstrMOD Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Kaur, H., Garg, A. and Raghava, G. P. S. (2007)
PEPstr: A de novo method for tertiary structure prediction of small bioactive peptides.
Protein Pept Lett. 14:626-30.

PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues.
Singh S, Singh H, Tuknait A, Chaudhary K, Singh B, Kumaran S, Raghava GP.
Biol Direct. 2015 Dec 21;10:73. doi: 10.1186/s13062-015-0103-4.

TAPPred – Predict Peptide TAP Binding Affinity

TAPPred

:: DESCRIPTION

 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

::DEVELOPER

TAPPred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bhasin,M. and Raghava, G.P.S. (2004)
Analysis and prediction of affinity of TAP binding peptides using cascade SVM.
Protein Sci.,13 (3),596-607.

HLADR4Pred – SVM and ANN based HLA-DR Bininding Peptide Prediction

HLADR4Pred

:: DESCRIPTION

The HLA-DR4Pred is an SVM and ANN based HLA-DRB1*0401(MHC class II alleles) binding peptides prediction method. The accuracy of the SVM and ANN based methods is ~86% and ~78% respectively.The performence of the methods was tested through 5 set cross-validation. The training of SVM and ANN was done by using the freely availaible SVM_LIGHT and SNNS packages respectively. The data for training of neural network and SVM model has been extracted from MHCBN database.This method will be useful in cellular immunology, Vaccine design, immunodiagnostics, immunotherapeuatics and molecular understanding of autoimmune susceptibility.

::DEVELOPER

HLADR4Pred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bhasin M, Raghava GPS (2004).
SVM based method for predicting HLA-DRB1 binding peptides in an antigen sequence.
Bioinformatics 20(3): 421-3

nHLAPred – Neural Network based MHC Class-I Binding Peptide Prediction Server

nHLAPred

:: DESCRIPTION

nHLAPred 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).

::DEVELOPER

nHLAPred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bhasin M. and Raghava G P S (2006)
A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes;
J. Biosci. 32:31-42.

ToxinPred – Designing and Prediction of Toxic Peptides

ToxinPred

:: DESCRIPTION

ToxinPred is an in silico method, which is developed to predict and design toxic/non-toxic peptides. The main dataset used in this method consists of 1805 toxic peptides (<=35 residues).

::DEVELOPER

ToxinPred team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

PLoS One. 2013 Sep 13;8(9):e73957. doi: 10.1371/journal.pone.0073957. eCollection 2013.
In silico approach for predicting toxicity of peptides and proteins.
Gupta S1, Kapoor P, Chaudhary K, Gautam A, Kumar R; Open Source Drug Discovery Consortium, Raghava GP.

AntiCP – Prediction and Designing of Anticancer Peptides

AntiCP

:: DESCRIPTION

AntiCP is web based prediction server for Anticancer peptides. SVM models developed are based on amino acid composition and binary profile features. Positive dataset consists of 225 antimicrobial peptides with anticancer properties.

::DEVELOPER

AntiCP team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Sci Rep. 2013 Oct 18;3:2984. doi: 10.1038/srep02984.
In silico models for designing and discovering novel anticancer peptides.
Tyagi A1, Kapoor P, Kumar R, Chaudhary K, Gautam A, Raghava GP.

dPABBs – Predicting and Designing Anti-biofilm Peptides

dPABBs

:: DESCRIPTION

dPABBs  (design Peptides Against Bacterial Biofilms) is a web server that facilitates the prediction and design of anti-biofilm peptides.

::DEVELOPER

Anshu Bhardwaj Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

dPABBs: A Novel in silico Approach for Predicting and Designing Anti-biofilm Peptides.
Sharma A, Gupta P, Kumar R, Bhardwaj A.
Sci Rep. 2016 Feb 25;6:21839. doi: 10.1038/srep21839.

TheorChromo 1.0 – Peptide and Protein Retention Time Prediction in Liquid Chromatography

TheorChromo 1.0

:: DESCRIPTION

TheorChromo is an online tool for peptide and protein retention time prediction in liquid chromatography. It implements the BioLCCC model for the most popular case of linear gradient chromatography.

::DEVELOPER

Laboratory of Physical and Chemical Methods for Structure Analysis @ the Institute for Energy Problems of Chemical Physics, Russian Academy of Sciences

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOSX
  • Python / C++ Compiler

:: DOWNLOAD

  BioLCCC / libBioLCCC

:: MORE INFORMATION

Citation

Anal Chem. 2006 Nov 15;78(22):7770-7.
Liquid chromatography at critical conditions: comprehensive approach to sequence-dependent retention time prediction.
Gorshkov AV, Tarasova IA, Evreinov VV, Savitski MM, Nielsen ML, Zubarev RA, Gorshkov MV.

IRMa 1.31.1 – Validation of MS Peptides Identification

IRMa 1.31.1

:: DESCRIPTION

IRMa ( Interprétation des Résultats Mascot)  toolbox provides an interactive application to assist in the validation of Mascot® search results. IRMa reads MASCOT® result (using Matrix Science® Parser distributed free of charge) and automatically filters identified peptides. All relevant information is displayed in a structured manner, showing “proteins hits” details. User can then manually or automatically confirm or reject individual peptide spectrum matches.

::DEVELOPER

Christophe Bruley

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/Windows/MacOsx
  • Java

:: DOWNLOAD

 IRMa

:: MORE INFORMATION

Citation

Veronique Dupierris; Christophe Masselon; Magali Court; Sylvie Kieffer-Jaquinod; Christophe Bruley
A toolbox for Validation of mass spectrometry peptides identification and Generation of database: IRMa
Bioinformatics 2009;25 (15): 1980-1981. doi: 10.1093/bioinformatics/btp301