DCF 0.6 – Peptide Design by Compatible Functions

DCF 0.6

:: DESCRIPTION

DCF (Design by Compatible Functions) is a peptide design principle for multifunctional peptides based on similarity to a functional reference class.

::DEVELOPER

Christian Diener

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 DCF

:: MORE INFORMATION

Citation

Effective Design of Multifunctional Peptides by Combining Compatible Functions.
Diener C, Garza Ramos Martínez G, Moreno Blas D, Castillo González DA, Corzo G, Castro-Obregon S, Del Rio G.
PLoS Comput Biol. 2016 Apr 20;12(4):e1004786. doi: 10.1371/journal.pcbi.1004786.

PeakLink 1.0 – Peptide Peak Linking method in LC-MS/MS using Wavelet and SVM

PeakLink 1.0

:: DESCRIPTION

PeakLink (PL) uses information in both the time and frequency domain as inputs to a non-linear support vector machine (SVM) classifier. The PL algorithm first uses a threshold on retention time to remove candidate corresponding peaks with excessively large elution time shifts, then PL calculates the correlation between a pair of candidate peaks after removing noise through wavelet transformation. After converting retention time and peak shape correlation to statistical scores, an SVM classifier is trained and applied for differentiating corresponding and non-corresponding peptide peaks.

::DEVELOPER

PeakLink team

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux / Windows / MacOsX
  • MatLab

:: DOWNLOAD

 PeakLink

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Sep 1;30(17):2464-70. doi: 10.1093/bioinformatics/btu299. Epub 2014 May 9.
PeakLink: a new peptide peak linking method in LC-MS/MS using wavelet and SVM.
Ghanat Bari M, Ma X, Zhang J.

MHC-NP – Prediction of Peptides Naturally Processed by the MHC

MHC-NP

:: DESCRIPTION

MHC-NP is a python tool to identify peptides that are naturally processed by the MHC-I pathway.

::DEVELOPER

Machine Learning Research Group at Laval University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Sébastien Giguère, Alexandre Drouin, Alexandre Lacoste, Mario Marchand, Jacques Corbeil, and François Laviolette
MHC-NP: Predicting peptides naturally processed by the MHC.”
Journal of immunological methods 400 (2013): 30-36.

HemoPI – Computing Hemolytic Potency of Peptides

HemoPI

:: DESCRIPTION

HemoPI allow user to predict predict hemolytic or hemotoxic or RBC lysing potential of a peptide. It allow users to perform virious functions that includes virtual screening of peptides, analog-based peptide design.

::DEVELOPER

HemoPI team

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows/ MacOsX/ Android
  • JRE

:: DOWNLOAD

 HemoPI

:: MORE INFORMATION

Citation

A Web Server and Mobile App for Computing Hemolytic Potency of Peptides.
Chaudhary K, Kumar R, Singh S, Tuknait A, Gautam A, Mathur D, Anand P, Varshney GC, Raghava GP.
Sci Rep. 2016 Mar 8;6:22843. doi: 10.1038/srep22843.

PeptideBuilder 1.0.4 – Python Library to Generate Model Peptides

PeptideBuilder 1.0.4

:: DESCRIPTION

PeptideBuilder is a simple Python library to construct models of polypeptides from scratch.

::DEVELOPER

Claus Wilke’s lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Python

:: DOWNLOAD

 PeptideBuilder

:: MORE INFORMATION

Citation

Tien MZ, Sydykova DK, Meyer AG, Wilke CO. (2013)
PeptideBuilder: A simple Python library to generate model peptides.
PeerJ 1:e80

Multi-VORFFIP / VORFFIP – Predicts protein-, peptide-, DNA- and RNA-binding sites in Proteins

Multi-VORFFIP / VORFFIP

:: DESCRIPTION

Multi-VORFFIP is a structure-based, machine learning, computational method designed to predict protein-protein, protein-peptide, protein-DNA and protein-RNA binding sites. M-VORFFIP integrates a wide and heterogeneous set of residue- and environment-based information using a two-step Random Forest ensemble classifier.

VORFFIP (Voronoi Random Forest Feedback Interface Predictor) is structure-based computational method for prediction of protein binding sites.

::DEVELOPER

 Bioinformatics Lab :: IBERS :: Aberystwyth University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Server

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Jul 15;28(14):1845-50. doi: 10.1093/bioinformatics/bts269. Epub 2012 May 4.
A holistic in silico approach to predict functional sites in protein structures.
Segura J1, Jones PF, Fernandez-Fuentes N.

BMC Bioinformatics. 2011 Aug 23;12:352. doi: 10.1186/1471-2105-12-352.
Improving the prediction of protein binding sites by combining heterogeneous data and Voronoi diagrams.
Segura J1, Jones PF, Fernandez-Fuentes N.

MoDPepInt 4.8.0 – Prediction of Modular Domain-peptide Interactions

MoDPepInt 4.8.0

:: DESCRIPTION

MoDPepInt (Modular Domain Peptide Interaction) is a new, easy-to-use webserver for the prediction of binding partners for modular protein domains. The server comprises three different tools, i.e. SH2PepInt, SH3PepInt and PDZPepInt, for predicting the binding partners of three different modular protein domains, i.e. SH2, SH3 and PDZ domains, respectively.

::DEVELOPER

Chair for Bioinformatics Freiburg

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Server

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bioinformatics. 2014 May 28. pii: btu350. [Epub ahead of print]
MoDPepInt: An interactive webserver for prediction of modular domain-peptide interactions.
Kundu K1, Mann M1, Costa F1, Backofen R2.

ChloroP 1.1 – Predict Chloroplast Transit Peptides

ChloroP 1.1

:: DESCRIPTION

ChloroP predicts the presence of chloroplast transit peptides (cTP) in protein sequences and the location of potential cTP cleavage sites.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

ChloroP

:: MORE INFORMATION

Citation:

ChloroP, a neural network-based method for predicting chloroplast transit peptides and their cleavage sites
Emanuelsson O, Nielsen H, von Heijne G
Protein Science., 8, 978-984, 1999

NetMHCII 2.3 – Predict Binding of Peptides to MHC class II Alleles

NetMHCII 2.3

:: DESCRIPTION

NetMHCII predicts binding of peptides to HLA-DR, HLA-DQ, HLA-DP and mouse MHC class II alleles using articial neuron networks.
Predictions can be obtained for 14 HLA-DR alleles covering the 9 HLA-DR supertypes, six HLA-DQ, six HLA-DP, and two mouse H2 class II alleles.
The prediction values are given in nM IC50 values, and as a %-Rank to a set of 1,000,000 random natural peptides. Strong and weak binding peptides are indicated in the output.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

NetMHCII

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2009 Sep 18;10:296.
NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction.
Nielsen M, Lund O.

NetMHCIIpan 3.2 – predict Pan-specific Binding of Peptides to MHC class II HLA-DR Alleles

NetMHCIIpan 3.2

:: DESCRIPTION

NetMHCIIpan predicts binding of peptides to more than 500 HLA-DR alleles using artificial neural networks (ANNs). The prediction values are given in nM IC50 values and as %-Rank to a set of 200.000 random natural peptides.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

NetMHCIIpan

:: MORE INFORMATION

Citation

Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.
Andreatta M, Karosiene E, Rasmussen M, Stryhn A, Buus S, Nielsen M.
Immunogenetics. 2015 Sep 29.

NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ
Karosiene E, Rasmussen M, Blicher T, Lund O, Buus S, and Nielsen M
Immunogenetics, 2013

NetMHCIIpan-2.0 – Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure
Nielsen M1, Lundegaard C1, Justesen S2, Lund O1, and Buus S2
Immunome Res. 2010 Nov 13;6(1):9.