Phos3D – Prediction of Phosphorylation Sites (P-sites) in Proteins

Phos3D

:: DESCRIPTION

Phos3D is a web server for the prediction of phosphorylation sites (P-sites) in proteins. The approach is based on Support Vector Machines trained on sequence profiles enhanced by information from the spatial context of experimentally identified P-sites.

::DEVELOPER

Max Planck Institute for Molecular Plant Physiology

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2009 Apr 21;10:117.
Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction in proteins.
Durek P, Schudoma C, Weckwerth W, Selbig J, Walther D.

CPSP Tools 4.8.0 – Constraint-based Protein Structure Prediction

CPSP Tools 4.8.0

:: DESCRIPTION

CPSP-tools package provides programs to solve exactly and completely the problems typical of studies using 3D lattice protein models. Among the tasks addressed are the prediction of globally optimal and/or suboptimal structures as well as sequence design and neutral network exploration.

::DEVELOPER

Bioinformatics Group
Albert-Ludwigs-University Freiburg

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 CPSP Tools

:: MORE INFORMATION

Citation

Martin Mann, Sebastian Will, and Rolf Backofen.
CPSP-tools – Exact and Complete Algorithms for High-throughput 3D Lattice Protein Studies.
BMC Bioinformatics, 9, 230, 2008.

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.

CopraRNA 2.1.3 – sRNA Target Prediction utilizing Homology

CopraRNA 2.1.3

:: DESCRIPTION

CopraRNA (Comparative Prediction Algorithm for sRNA Targets) is a tool for sRNA target prediction. It computes whole genome predictions by combination of distinct whole genome IntaRNA predictions. As input, CopraRNA requires at least 3 homologous sRNA sequences from 3 distinct organisms in FASTA format.

::DEVELOPER

Chair for Bioinformatics Freiburg

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl

:: DOWNLOAD

 CopraRNA 

:: MORE INFORMATION

Citation

Proc Natl Acad Sci U S A. 2013 Sep 10;110(37):E3487-96. doi: 10.1073/pnas.1303248110. Epub 2013 Aug 26.
Comparative genomics boosts target prediction for bacterial small RNAs.
Wright PR1, Richter AS, Papenfort K, Mann M, Vogel J, Hess WR, Backofen R, Georg J.

MultiTF-PPI – Competitive Transcription Factor Binding Prediction

MultiTF-PPI

:: DESCRIPTION

MultiTF-PPI is a probabilistic protein-protein interaction guided method for competitive transcription factor binding prediction.

::DEVELOPER

Computational systems biology group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX / Windows
  • Matlab

:: DOWNLOAD

MultiTF-PPI

:: MORE INFORMATION

EasyGene 1.2c – Prediction of Genes in Prokaryotes

EasyGene 1.2c

:: DESCRIPTION

EasyGene estimates the statistical significance of a predicted gene. The gene finder is based on a hidden Markov model (HMM) that is automatically estimated for a new genome. Using extensions of similarities in Swiss-Prot, a high quality training set of genes is automatically extracted from the genome and used to estimate the HMM. Putative genes are then scored with the HMM, and based on score and length of an ORF, the statistical significance is calculated. The measure of statistical significance for an ORF is the expected number of ORFs in one megabase of random sequence at the same significance level or better, where the random sequence has the same statistics as the genome in the sense of a third order Markov chain.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

EasyGene

:: MORE INFORMATION

Citation

Large-scale prokaryotic gene prediction and comparison to genome annotation.
P. Nielsen and A. Krogh.
Bioinformatics: 21:4322-4329, 2005.

EasyGene – a prokaryotic gene finder that ranks ORFs by statistical significance.
Thomas Schou Larsen and Anders Krogh.
BMC Bioinformatics: 4:21, 2003

LipoP 1.0a – Prediction of Lipoproteins & Signal Peptides in Gram Negative Bacteria

LipoP 1.0a

:: DESCRIPTION

LipoP produces predictions of lipoproteins and discriminates between lipoprotein signal peptides, other signal peptides and n-terminal membrane helices in Gram-negative bacteria.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

LipoP

:: MORE INFORMATION

Citation

Methods for the bioinformatic identification of bacterial lipoproteins encoded in the genomes of Gram-positive bacteria
O. Rahman, S. P. Cummings, D. J. Harrington and I. C. Sutcliffe
World Journal of Microbiology and Biotechnology 24(11):2377-2382 (2008)

SecretomeP 2.0 – Prediction of Non-classical Protein Secretion

SecretomeP 2.0

:: DESCRIPTION

SecretomeP server produces ab initio predictions of non-classical i.e. not signal peptide triggered protein secretion. The method queries a large number of other feature prediction servers to obtain information on various post-translational and localizational aspects of the protein, which are integrated into the final secretion prediction.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

SecretomeP

:: MORE INFORMATION

Citation

Feature based prediction of non-classical and leaderless protein secretion
J. Dyrløv Bendtsen, L. Juhl Jensen, N. Blom, G. von Heijne and S. Brunak
Protein Eng. Des. Sel., 17(4):349-356, 2004

NetSurfP 2.0 – Protein Surface Accessibility & Secondary Structure Predictions

NetSurfP 2.0

:: DESCRIPTION

NetSurfP predicts the surface accessibility and secondary structure of amino acids in an amino acid sequence. The method also simultaneously predicts the reliability for each prediction, in the form of a Z-score. The Z-score is related to the surface prediction, and not the secondary structure.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

NetSurfP

:: MORE INFORMATION

Citation

A generic method for assignment of reliability scores applied to solvent accessibility predictions.
Bent Petersen, Thomas Nordahl Petersen, Pernille Andersen, Morten Nielsen and Claus Lundegaard1.
BMC Structural Biology 2009, 9:51 doi:10.1186/1472-6807-9-51.

NetUTR 1.0b – Web server for Prediction of Splice Site in 5′ UTRs

NetUTR 1.0b

:: DESCRIPTION

The NetUTR method presented here performs 2-3-fold better compared with NetGene2 and GenScan in 5′ UTRs (5′ untranslated regions).

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Analysis and recognition of 5′ UTR intron splice sites in human pre-mRNA.
Eden E, Brunak S.
Nucleic Acids Res. 2004 Feb 11;32(3):1131-42.