TargetP 2.0 – Predict Protein Subcellular Localisation

TargetP 2.0

:: DESCRIPTION

TargetP predicts the subcellular location of eukaryotic proteins. The location assignment is based on the predicted presence of any of the N-terminal presequences: chloroplast transit peptide (cTP), mitochondrial targeting peptide (mTP) or secretory pathway signal peptide (SP).

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

NO

:: MORE INFORMATION

Citation:

Predicting subcellular localization of proteins based on their N-terminal amino acid sequence.
Olof Emanuelsson, Henrik Nielsen, Søren Brunak and Gunnar von Heijne.
J. Mol. Biol., 300: 1005-1016, 2000.

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.

SigniSite 2.1 – Residue level Genotype Phenotype Correlation in Protein Multiple Sequence Alignments

SigniSite 2.1

:: DESCRIPTION

SigniSite performs residue level genotype phenotype correlation in protein multiple sequence alignments by identifying amino acid residues significantly associated with the phenotype of the data set.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2013 Jul;41(Web Server issue):W286-91. doi: 10.1093/nar/gkt497. Epub 2013 Jun 12.
SigniSite: Identification of residue-level genotype-phenotype correlations in protein multiple sequence alignments.
Jessen LE1, Hoof I, Lund O, Nielsen M.

CPHmodels 3.2 – Protein Homology Modeling server

CPHmodels 3.2

:: DESCRIPTION

CPHmodels is a protein homology modeling server. The template recognition is based on profile-profile alignment guided by secondary structure and exposure predictions.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 Send inquiries by e-mail to software@cbs.dtu.dk.

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2010 Jul;38(Web Server issue):W576-81. doi: 10.1093/nar/gkq535. Epub 2010 Jun 11.
CPHmodels-3.0–remote homology modeling using structure-guided sequence profiles.
Nielsen M1, Lundegaard C, Lund O, Petersen TN.

PocketAnalyzerPCA 1.30 – Pocket-space Maps to Identify novel Binding-site Conformations in Proteins

PocketAnalyzerPCA 1.30

:: DESCRIPTION

PocketAnalyzer(PCA) combines a geometric algorithm for detecting pockets in proteins with Principal Component Analysis and clustering. This enables visualization and analysis of pocket conformational distributions of large sets of protein structures.

::DEVELOPER

the Gohlke Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  PocketAnalyzerPCA

:: MORE INFORMATION

Citation

J Chem Inf Model. 2011 Oct 24;51(10):2666-79. doi: 10.1021/ci200168b. Epub 2011 Sep 30.
Pocket-space maps to identify novel binding-site conformations in proteins.
Craig IR1, Pfleger C, Gohlke H, Essex JW, Spiegel K.

CellOrganizer 2.8.1 – Image-derived Models of Subcellular Organization and Protein Distribution

CellOrganizer 2.8.1

:: DESCRIPTION

The CellOrganizer project provides tools for :learning generative models of cell organization directly from images/ storing and retrieving those models in XML files/ synthesizing cell images (or other representations) from one or more models

::DEVELOPER

CellOrganizer TEam

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Mac /  Linux
  • MatLab

:: DOWNLOAD

  CellOrganizer

:: MORE INFORMATION

Citation

Methods Cell Biol. 2012;110:179-93.
CellOrganizer: Image-derived models of subcellular organization and protein distribution.
Murphy RF.

MolIDE 1.7 – Protein 3D Homology Modeling

MolIDE 1.7

:: DESCRIPTION

MolIDE is an open-source cross-platform graphical environment for homology modeling. It implements the most frequently used steps involved in modeling: sequence search, secondary structure prediction, multiple-round psiblast alignments, assisted alignment editing (integrating a template viewer and secondary structure prediction), side chain replacement and loop building. MolIDE takes an input target sequence and uses PSIBLAST to identify and align templates for comparative modeling of the target. The sequence alignment to any template can be manually modified within a graphical window of the target–template alignment and visualization of the alignment on the template structure. MolIDE builds the model of the target structure on the basis of the template backbone, predicted side.

::DEVELOPER

Dunbrack Lab

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Mac OsX /  Linux
  • SCWRL

:: DOWNLOAD

MolIDE

:: MORE INFORMATION

Citation

Qiang Wang, Adrian A Canutescu & Roland L Dunbrack Jr
SCWRL and MolIDE: computer programs for side-chain conformation prediction and homology modeling
Nature Protocols 3:1832-1847 (2008)

HMMSTR 20120205 – Protein Secondary Structure Prediction

HMMSTR 20120205

:: DESCRIPTION

HMMSTR ( Hidden Markov Model for Local Sequence-Structur) is a hidden Markov model for protein structure prediction. The program takes as input an amino acid probability distribution (or profile) for each residue position.  A profile may be derived from a multiple sequence alignment, or by running the database search program such as PSI_BLAST. It contains the programs needed to predict secondary structure starting with a sequence profile. The sequence profile (a vector of 20 probabilities for each residue in the sequence) can be the output of a profile HMM such as HMMer. It may also be the output of Psi-Blast, which uses profiles internally, or may be generated from a multiple sequence alignment. The programs in this package, HMMSTR and associated format converters, will give you a probabilistic prediction of each of the six DSSP symbols: H,E,G,S,T and _. For now, this is a bare-bones package.

HMMSTR Online Version

::DEVELOPER

Chris Bystroff

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

HMMSTR

:: MORE INFORMATION

Citaiton

BMC Bioinformatics. 2008 Oct 10;9:429. doi: 10.1186/1471-2105-9-429.
Pairwise covariance adds little to secondary structure prediction but improves the prediction of non-canonical local structure.
Bystroff C, Webb-Robertson BJ.

Bystroff C, Thorsson V & Baker D. (2000).
HMMSTR: A hidden markov model for local sequence-structure correlations in proteins.
Journal of Molecular Biology 301, 173-90.

InteractiveROSETTA 2.3.0 – GUI for PyRosetta Protein Modeling Suite

InteractiveROSETTA 2.3.0

:: DESCRIPTION

InteractiveROSETTA is a wxPython graphical interface for the PyRosetta and Rosetta protein modeling suites

::DEVELOPER

Chris Bystroff

:: SCREENSHOTS

InteractiveROSETTA

:: REQUIREMENTS

:: DOWNLOAD

 InteractiveROSETTA

:: MORE INFORMATION

Citation

InteractiveROSETTA: a graphical user interface for the PyRosetta protein modeling suite.
Schenkelberg CD, Bystroff C.
Bioinformatics. 2015 Aug 26. pii: btv492