PredAlgo 1.0 – Protein Subcellular Localization Prediction in Green Algae

PredAlgo 1.0

:: DESCRIPTION

PredAlgo is a new sequence analysis tool, dedicated to the prediction of protein subcellular localization in green algae. It uses a neural network trained with carefuly curated sets of Chlamydomonas reinhardtii proteins. PredAlgo predicts the localization to one of three compartments: the mitochondrion, the chloroplast, the secretory pathway within the cell.

::DEVELOPER

Nicolas J. Tourasse or Olivier Vallon

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Mol Biol Evol. 2012 Dec;29(12):3625-39. doi: 10.1093/molbev/mss178.
PredAlgo: a new subcellular localization prediction tool dedicated to green algae.
Tardif M, Atteia A, Specht M, Cogne G, Rolland N, Brugière S, Hippler M, Ferro M, Bruley C, Peltier G, Vallon O, Cournac L.

PredBF – Robust Prediction of B-factor Profile from Sequence

PredBF

:: DESCRIPTION

PredBF is a web server for robust prediction of B-factor profile from sequence using two-stage SVR based on random forest feature selection

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Protein Pept Lett. 2009;16(12):1447-54.
Robust prediction of B-factor profile from sequence using two-stage SVR based on random forest feature selection.
Pan XY1, Shen HB.

PFP-Pred 2.0 – Protein Fold Prediction

PFP-Pred 2.0

:: DESCRIPTION

PFP-Pred is an ensemble classifier for protein fold pattern recognition

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Hong-Bin Shen and Kuo-Chen Chou,
Ensemble classifier for protein folding pattern recognition“.
Bioinformatics, 2006, 22: 1717-22.

Cyscon 20150927 – Disulfide Connectivity Prediction Server

Cyscon 20150927

:: DESCRIPTION

Cyscon is a new hierarchical order reduction protocol for disulfide-bonding prediction.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins.
Yang J, He BJ, Jang R, Zhang Y, Shen HB.
Bioinformatics. 2015 Aug 7. pii: btv459.

SPEC – Cell Subset Prediction for Blood Genomic Studies

SPEC

:: DESCRIPTION

SPEC is a computational method to predict the cellular source for a pre-defined list of genes (i.e., a gene signature) using gene expression data from total PBMCs

::DEVELOPER

Kleinstein Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux
  • R

:: DOWNLOAD

SPEC

:: MORE INFORMATION

Citation

Bolen CR, Uduman M, Kleinstein SH.
Cell subset prediction for blood genomic studies.
BMC Bioinformatics. 2011 Jun 24;12:258.

MEMPACK 2.0 – SVM Prediction of Membrane Helix Packing

MEMPACK 2.0

:: DESCRIPTION

MEMPACK allows users to submit a transmembrane protein sequence and returns transmembrane topology, lipid exposure, residue contacts, helix–helix interactions and helical packing arrangement predictions in both plain text and graphical formats using a number of novel machine learning-based algorithms.

MEMPACK Online Version

:DEVELOPER

Bioinformatics Group – University College London

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 MEMPACK

:: MORE INFORMATION

Citation:

Bioinformatics. 2011 May 15;27(10):1438-9. Epub 2011 Feb 23.
The MEMPACK alpha-helical transmembrane protein structure prediction server.
Nugent T, Ward S, Jones DT.

DISOPRED 3.16 – Intrinsic Protein Disorder Prediction

DISOPRED 3.16

:: DESCRIPTION

DISOPRED is a SVM-based predictor of disordered regions in proteins.

:DEVELOPER

Bioinformatics Group – University College London

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/Unix/MacOsX
  • any ANSI C and C++ compiler

:: DOWNLOAD

 DISOPRED

:: MORE INFORMATION

Citation

Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF and Jones DT (2004)
Prediction and functional analysis of native disorder in proteins from the three kingdoms of life.
J. Mol. Biol., 337, 635-645.

HHMMiR 1.2 – Prediction of microRNAs using Hierarchical Hidden Markov models

HHMMiR 1.2

:: DESCRIPTION

HHMMiR is a novel approach for de novo miRNA hairpin prediction in the absence of evolutionary conservation. HHMMiR implements a Hierarchical Hidden Markov Model (HHMM) that utilizes region-based structural as well as sequence information of miRNA precursors.

:: DEVELOPER

Benos Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 HHMMiR

:: MORE INFORMATION

Citation:

S. Kadri, V. Hinman, P.V. Benos,
HHMMiR: Efficient de novo Prediction of MicroRNAs using Hierarchical Hidden Markov Models“,
BMC Bioinformatics (Proc APBC 2009) (2009) 10 (Suppl 1):S35.

SOLart 1.0 – Protein Solubility Prediction

SOLart 1.0

:: DESCRIPTION

SOLart is a fast and accurate method for predicting the protein solubility of a target protein whose experimental or modeled structure is available. It yields a scaled solubility score with values close to zero indicating aggregate-prone proteins, while values close to 130 designate soluble proteins.

::DEVELOPER

Service de Biomodélisation, Bioinformatique et Bioprocédés (3BIO)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics, 36 (5), 1445-1452 2020 Mar 1
SOLart: A Structure-Based Method to Predict Protein Solubility and Aggregation
Qingzhen Hou , Jean Marc Kwasigroch, Marianne Rooman, Fabrizio Pucci

SCooP 1.0 – Prediction of the Stability Curve of Proteins

SCooP 1.0

:: DESCRIPTION

SCooP is a fast and accurate method for predicting the Gibbs-Helmholtz equation associated to the folding transition of a target protein of known (or modeled) structure. In addition, SCooP yields an estimation of the thermodynamic quantities that characterize the folding process, in particular the change in enthalpy and in heat capacity upon folding, the melting temperature and the folding free energy at room temperature.

::DEVELOPER

Service de Biomodélisation, Bioinformatique et Bioprocédés (3BIO)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

F. Pucci, J.M. Kwasigroch, M. Rooman (2017),
SCooP : an accurate and fast predictor of protein stability curves as a function of the temperature,
Bioinformatics 33, 3415-3422.