Memoir – Membrane Protein Modelling Pipeline

Memoir

:: DESCRIPTION

Memoir (MEMbrane prOteIn modelleR) is a homology modelling algorithm designed for membrane proteins. The inputs are the sequence which is to be modelled, and the 3D structure of a template membrane protein.

::DEVELOPER

Oxford Protein Informatics Group (OPIG)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2013 Jul;41(Web Server issue):W379-83. doi: 10.1093/nar/gkt331. Epub 2013 May 2.
Memoir: template-based structure prediction for membrane proteins.
Ebejer JP1, Hill JR, Kelm S, Shi J, Deane CM.

SRSim 2.0.8 – Rule Based Modelling in 3d Space

SRSim 2.0.8

:: DESCRIPTION

SRSim is constructed from the molecular dynamics simulator LAMMPS and a set of extensions for modeling rule-based reaction systems. The aim of this software is coping with reaction networks that are combinatorially complex as well as spatially inhomogeneous. On the one hand, there is a combinatorial explosion of necessary species and reactions that occurs when complex biomolecules are allowed to interact, e.g. by polymerization or phosphorilation processes. On the other hand, di usion over longer distances in the cell as well as the geometric structures of sophisticated macromolecules can further in uence the dynamic behavior of a system. Addressing the mentioned demands, the SRSim simulation system features a stochastic, particle based, spatial simulation of Brownian Dynamics in three dimensions of a rule-based reaction system.

::DEVELOPER

Gerd Grünert @ Bio Systems Analysis

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

SRSim

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2010 Jun 7;11:307. doi: 10.1186/1471-2105-11-307.
Rule-based spatial modeling with diffusing, geometrically constrained molecules.
Gruenert G1, Ibrahim B, Lenser T, Lohel M, Hinze T, Dittrich P.

AMIGO2 2016a – Advanced Modelling and Identification using Global Optimization

AMIGO2 2016a

:: DESCRIPTION

AMIGO is a multi-platform (Windows and Linux) toolbox which covers all the steps of the iterative identification procedure: local and global sensitivity analysis, local and global ranking of parameters, parameter estimation, identifiability analysis and optimal experimental design.

::DEVELOPER

(Bio)Process Engineering group, IIM-CSIC

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux /MacOsX
  • Matlab

:: DOWNLOAD

 AMIGO

:: MORE INFORMATION

Citation:

AMIGO2, a toolbox for dynamic modeling, optimization and control in systems biology.
Balsa-Canto E, Henriques D, Gabor A, Banga JR.
Bioinformatics. 2016 Jul 4. pii: btw411.

Bioinformatics (2011)doi: 10.1093/bioinformatics/btr370 First published online: June 17, 2011
AMIGO, a toolbox for Advanced Model Identification in systems biology using Global Optimization
Eva Balsa-Canto and Julio R. Banga

MDI-GPU 1.0 – Accelerating integrative modelling for Genomic-scale data using GP-GPU Computing

MDI-GPU 1.0

:: DESCRIPTION

MDI-GPU is an improved implementation of a Bayesian correlated clustering algorithm, that permits integrated clustering to be routinely performed across multiple datasets, each with tens of thousands of items.

::DEVELOPER

Warwick Systems Biology Centre

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 MDI-GPU

:: MORE INFORMATION

Citation

MDI-GPU: accelerating integrative modelling for genomic-scale data using GP-GPU computing.
Mason SA, Sayyid F, Kirk PD, Starr C, Wild DL.
Stat Appl Genet Mol Biol. 2016 Mar 1;15(1):83-6. doi: 10.1515/sagmb-2015-0055.

MTMDAT-HADDOCK 201210 – High-throughput, Data-driven Protein Complex Structure Modelling

MTMDAT-HADDOCK 201210

:: DESCRIPTION

MTMDAT-HADDOCK facilitates fast and detailed evaluation of mass spectrometry data of limited proteolysis experiments to probe the tertiary structure of proteins and even complex formation with other proteins, ligands, and surfaces

::DEVELOPER

the Maria Sunnerhagen group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux

:: DOWNLOAD

 MTMDAT-HADDOCK

:: MORE INFORMATION

Citation

BMC Struct Biol. 2012 Nov 15;12:29. doi: 10.1186/1472-6807-12-29.
MTMDAT-HADDOCK: high-throughput, protein complex structure modeling based on limited proteolysis and mass spectrometry.
Hennig J1, de Vries SJ, Hennig KD, Randles L, Walters KJ, Sunnerhagen M, Bonvin AM.

LYRA 1.0 – Lymphocyte Receptor Automated Modelling

LYRA 1.0

:: DESCRIPTION

The LYRA server predicts structures for either T-Cell Receptors (TCR) or B-Cell Receptors (BCR) using homology modelling.

::DEVELOPER

Center for Biological Sequence Analysis, Technical University of Denmark

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

LYRA, a webserver for lymphocyte receptor structural modeling.
Klausen MS, Anderson MV, Jespersen MC, Nielsen M, Marcatili P.
Nucleic Acids Res. 2015 May 24. pii: gkv535

3D-DART – DNA Structure Modelling Server

3D-DART

:: DESCRIPTION

The 3D-DART server (3DNA-Driven DNA Analysis and Rebuilding Tool) provides a convenient means of generating custom 3D structural models of DNA with control over the local and global conformation.

::DEVELOPER

BONVIN LAB

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

M. van Dijk and A.M.J.J. Bonvin (2009)
3D-DART: a DNA structure modelling server
Nucl. Acids Res., 37 (Web Server Issue):W235-W239 doi:10.1093/nar/gkp287

scLVM 0.1 – Modelling framework for Single-cell RNA-seq data

scLVM 0.1

:: DESCRIPTION

scLVM (single-cell Latent Variable Model) is a modelling framework for single-cell RNA-seq data that can be used to dissect the observed heterogeneity into different sources, thereby allowing for the correction of confounding sources of variation.

::DEVELOPER

Institute of Computational Biology, German Research Center for Environmental Health (GmbH)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • Python
  • R

:: DOWNLOAD

 scLVM

:: MORE INFORMATION

Citation

Nat Biotechnol. 2015 Feb;33(2):155-60. doi: 10.1038/nbt.3102. Epub 2015 Jan 19.
Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.
Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A, Theis FJ, Teichmann SA, Marioni JC, Stegle O

DockStar – Modelling of multimolecular Protein Complexes

DockStar

:: DESCRIPTION

DockStar is an algorithm for modeling of multimolecular protein complexes. It integrates both high resolution data of the individual subunits and low resolution data, such as the complex interaction graph and chemical cross-links.

::DEVELOPER

 the BioInfo3D group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

DockStar: a Novel ILP based Integrative Method for Structural Modeling of Multimolecular Protein Complexes.
Amir N, Cohen D, Wolfson H.
Bioinformatics. 2015 Apr 25. pii: btv270.

GNA 8.6.1 – Modelling & Simulation of Genetic Regulatory Networks

GNA 8.6.1

:: DESCRIPTION

GNA (Genetic Network Analyzer) is a computer tool for the qualitative modeling and simulation of genetic regulatory networks. GNA assists you in constructing a model of a genetic regulatory network using knowledge about regulatory interactions in combination with gene expression data. Moreover, it allows you to simulate the qualitative behavior of the network in response to external perturbations. The mathematical models created with GNA can be tested against observed properties of the network.

::DEVELOPER

Bruno Besson, Hidde de Jong, Pedro Monteiro, and Michel Page.

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows/MacOsx/Linux
  • Java

:: DOWNLOAD

 GNA

:: MORE INFORMATION

Citation

Methods Mol Biol. 2012;804:439-62. doi: 10.1007/978-1-61779-361-5_22.
Genetic network analyzer: a tool for the qualitative modeling and simulation of bacterial regulatory networks.
Batt G1, Besson B, Ciron PE, de Jong H, Dumas E, Geiselmann J, Monte R, Monteiro PT, Page M, Rechenmann F, Ropers D.

H. de Jong, J. Geiselmann, C. Hernandez, M. Page (2003),
Genetic Network Analyzer : Qualitative simulation of genetic regulatory networks,
Bioinformatics, 19(3):336-344