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.

NetMHC 4.0 – predict Binding of Peptides to MHC Class I Alleles

NetMHC 4.0

:: DESCRIPTION

NetMHC predicts binding of peptides to a number of different HLA alleles using artificial neural networks (ANNs) and weight matrices.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

NetMHC

:: MORE INFORMATION

Citation

Gapped sequence alignment using artificial neural networks: application to the MHC class I system.
Andreatta M, Nielsen M.
Bioinformatics. 2015 Oct 29. pii: btv639.

NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11
Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, Nielsen M.
Nucleic Acids Res. 1;36(Web Server issue):W509-12. 2008

NetMHCpan 4.0 – Predicts Binding of Peptides to Known MHC Molecule

NetMHCpan 4.0

:: DESCRIPTION

NetMHCpan predicts binding of peptides to any known MHC molecule using artificial neural networks (ANNs). The method is trained on more than 115,000 quantitative binding data covering more than 120 different MHC molecules. Predictions can be made for HLA-A, B, C, E and G alleles, as well as for non-human primates, mouse, Cattle and pig. Further, the user can upload full length MHC protein sequences, and have the server predict MHC restricted peptides from any given protein of interest.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

NetMHCpan

:: MORE INFORMATION

Citation

NetMHCpan – MHC class I binding prediction beyond humans
Ilka Hoof, Bjoern Peters, John Sidney, Lasse Eggers Pedersen, Ole Lund, Soren Buus, and Morten Nielsen
Immunogenetics. 2009 Jan;61(1):1-13. Epub 2008 Nov 12.

PeptideReranking – Peptide Re-ranking with Protein-peptide Correspondence and Precursor Peak Intensity Information

PeptideReranking

:: DESCRIPTION

PeptideReranking includes three peptide reranking methods: PPMRanker, PPIRanker, and MIRanker. PPMRanker only uses Protein-Peptide Map (PPM) information from the protein database, PPIRanker only uses Precursor Peak Intensity (PPI) information, and MIRanker employs both PPM information and PPI information.

::DEVELOPER

Laboratory for Bioinformatics and Computational Biology, HKUST

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 PeptideReranking

:: MORE INFORMATION

Citation:

IEEE/ACM Trans Comput Biol Bioinform. 2012 Jul-Aug;9(4):1212-9. doi: 10.1109/TCBB.2012.29.
Peptide reranking with protein-peptide correspondence and precursor peak intensity information.
Yang C1, He Z, Yang C, Yu W.

MUSI – MUltiple Specificity Identifier

MUSI

:: DESCRIPTION

MUSI is a tool for uncovering multiple peptides and nucleic acids binding specificities from sequence data. MUSI provides a simple interface for processing short peptides or nucleic acid sequence data. Starting from a set of sequences observed to bind to a given target, it automatically generates an optimal number of motifs based on the different specificity patterns present in the data.

::DEVELOPER

Kim Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX

:: DOWNLOAD

 MUSI

:: MORE INFORMATION

Citation

MUSI: an integrated system for identifying multiple specificity from very large peptide or nucleic acid data sets.
Kim T, Tyndel MS, Huang H, Sidhu SS, Bader GD, Gfeller D, Kim PM.
Nucleic Acids Res. 2012 Mar;40(6):e47. doi: 10.1093/nar/gkr1294