MemPype – Pipeline for Predicting the Topology and the Localization of Membrane Proteins

MemPype

:: DESCRIPTION

MemPype is a Python-based pipeline that integrates several tools the prediction of topology and subcellular localization of Eukaryotic membrane proteins.

::DEVELOPER

Bologna Biocomputing Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 No. Only Web Service

:: MORE INFORMATION

Citation

Pierleoni A, Indio V, Savojardo C, Fariselli P, Martelli PL, Casadio R.
MemPype: a pipeline for the annotation of eukaryotic membrane proteins.
Nucl. Acids Res. (2011) 39 (suppl 2): W375-W380.

MINNOU – Membrane Protein IdeNtificatioN withOUt explicit use of Hydropathy Profiles and Alignments

MINNOU

:: DESCRIPTION

The MINNOU server can be used for predicting trans-membrane domains.

:: SCREENSHOTS

N/A

::DEVELOPER

Meller Lab

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 MINNOU

:: MORE INFORMATION

Citation

Bioinformatics. 2006 Feb 1;22(3):303-9. Epub 2005 Nov 17.
Enhanced recognition of protein transmembrane domains with prediction-based structural profiles.
Cao B1, Porollo A, Adamczak R, Jarrell M, Meller J.

Mem-ADSVM – Multi-Label Membrane Protein Type Prediction

Mem-ADSVM

:: DESCRIPTION

Mem-ADSVM is a two-layer multi-label membrane-protein functional-type predictor, which can identify membrane proteins (Layer I) and their multi-functional types (Layer II).

::DEVELOPER

Dr. Man-Wai Mak

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Mem-ADSVM: A Two-Layer Multi-Label Predictor for Identifying Multi-Functional Types of Membrane Proteins.
Wan S, Mak MW, Kung SY.
J Theor Biol. 2016 Mar 18. pii: S0022-5193(16)00154-5. doi: 10.1016/j.jtbi.2016.03.013.

AlignMe 1.1 – Alignment of Membrane Proteins

AlignMe 1.1

:: DESCRIPTION

AlignMe is a C++ program to align distantly related membrane proteins by including several input parameters. Alignments based on substitution matrices, Position Specific Matrices (PSSMs), hydrophobicity scales and any kind of profiles (membrane predictions, secondary structure predictions etc.).

::DEVELOPER

Forrest Lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

 AlignMe

:: MORE INFORMATION

Citation

PLoS One. 2013;8(3):e57731. doi: 10.1371/journal.pone.0057731. Epub 2013 Mar 4.
Alignment of helical membrane protein sequences using AlignMe.
Stamm M1, Staritzbichler R, Khafizov K, Forrest LR.

MPRAP – Membrane Protein Residue Accessibility Predictor

MPRAP

:: DESCRIPTION

MPRAP is a novel Membrane Protein Residue Accessibility Predictor, based on sequence derived information. In contrast to previous membrane predictors, MPRAP, performs well both within and outside the membrane regions and outperforms earlier methods in the membrane regions.

::DEVELOPER

Elofsson Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2010 Jun 18;11:333. doi: 10.1186/1471-2105-11-333.
MPRAP: an accessibility predictor for a-helical transmembrane proteins that performs well inside and outside the membrane.
Illergård K1, Callegari S, Elofsson A.

OCTOPUS / SPOCTOPUS – Prediction of Membrane Protein Topology and Signal Peptides

OCTOPUS / SPOCTOPUS

:: DESCRIPTION

OCTOPUS is a new method for predicting transmembrane protein topology is presented and benchmarked using a dataset of 124 sequences with known structures.

SPOCTOPUS is a method for combined prediction of signal peptides and membrane protein topology, suitable for genome-scale studies.

::DEVELOPER

Elofsson Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 OCTOPUS / SPOCTOPUS

:: MORE INFORMATION

Citation

Bioinformatics. 2008 Aug 1;24(15):1662-8. doi: 10.1093/bioinformatics/btn221. Epub 2008 May 12.
OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar.
Viklund H1, Elofsson A.

Bioinformatics. 2008 Dec 15;24(24):2928-9. doi: 10.1093/bioinformatics/btn550. Epub 2008 Oct 22.
SPOCTOPUS: a combined predictor of signal peptides and membrane protein topology.
Viklund H1, Bernsel A, Skwark M, Elofsson A.

iMem-Seq – Predicting Membrane Proteins Types

iMem-Seq

:: DESCRIPTION

iMem-Seq is a multi-label classifier for identifying membrane proteins with single and multiple types via physical-chemical property matrix and grey-PSSM

::DEVELOPER

Xiao Lab

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

J Membr Biol. 2015 Aug;248(4):745-52. doi: 10.1007/s00232-015-9787-8. Epub 2015 Mar 22.
iMem-Seq: A Multi-label Learning Classifier for Predicting Membrane Proteins Types.
Xiao X1, Zou HL, Lin WZ.

MERMAID – Prepare and Run Coarse-Grained Membrane Protein Dynamics

MERMAID

:: DESCRIPTION

MERMAID (Martini coarsE gRained MembrAne proteIn Dynamics) is a publicly available web interface that allows the user to prepare and run coarse-grained molecular dynamics (CGMD) simulations and to analyse the trajectories

::DEVELOPER

MERMAID team, University of Verona

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Nucleic Acids Res, 47 (W1), W456-W461 2019 Jul 2
MERMAID: Dedicated Web Server to Prepare and Run Coarse-Grained Membrane Protein Dynamics
Mangesh Damre , Alessandro Marchetto , Alejandro Giorgetti

MemType-2L – Predicting Membrane Protein Types

MemType-2L

:: DESCRIPTION

MemType-2L is a 2-layer predictor for predicting membrane protein types, the first layer will predict whether the query sequence belongs to membrane proteins or not; and the second layer aims to predict exactly the membrane protein types when the output of the first layer is “membrane proteins”.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Biochem Biophys Res Commun. 2007 Aug 24;360(2):339-45. Epub 2007 Jun 15.
MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM.
Chou KC1, Shen HB.

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