PFP-FunDSeqE – Predicting Protein Fold Pattern

PFP-FunDSeqE

:: DESCRIPTION

PFP-FunDSeqE is a web server of predicting protein fold pattern with functional domain and sequential evolution information

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

J Theor Biol. 2009 Feb 7;256(3):441-6. doi: 10.1016/j.jtbi.2008.10.007. Epub 2008 Oct 19.
Predicting protein fold pattern with functional domain and sequential evolution information.
Shen HB1, Chou KC.

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.

PredCSF – Predicting Conotoxin Superfamily

PredCSF

:: DESCRIPTION

The web server PredCSF was developed by using modified one-versus-rest SVMs. The SVMs’input features are composed of physicochemical properties, evolutionary information, second structure and amino acid composition. Each SVM classifier’s output is the probability assigned to a superfamily.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Protein Pept Lett. 2011 Mar;18(3):261-7.
PredCSF: an integrated feature-based approach for predicting conotoxin superfamily.
Fan YX1, Song J, Shen HB, Kong X.

ProtIdent – Identifying Proteases and their Types

ProtIdent

:: DESCRIPTION

The web server ProtIdent (Protease Identifier) was developed by fusing the functional domain and sequential evolution information.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

ProtIdent: a web server for identifying proteases and their types by fusing functional domain and sequential evolution information.
Chou KC, Shen HB.
Biochem Biophys Res Commun. 2008 Nov 14;376(2):321-5. doi: 10.1016/j.bbrc.2008.08.125.

QuatIdent – identifying the Quaternary Structural Attribute of a Protein Chain

QuatIdent

:: DESCRIPTION

The web server QuatIdent (Quaternary Identifier) was developed by fusing the functional domain and sequential evolution information.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

J Proteome Res. 2009 Mar;8(3):1577-84. doi: 10.1021/pr800957q.
QuatIdent: a web server for identifying protein quaternary structural attribute by fusing functional domain and sequential evolution information.
Shen HB1, Chou KC.

PseAAC – Generating Pseudo Amino Acid Composition

PseAAC

:: DESCRIPTION

PseAAC is a web-server, by which users can generate various kinds of PseAA composition to best fit their need.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition.
Shen HB, Chou KC.
Anal Biochem. 2008 Feb 15;373(2):386-8. Epub 2007 Oct 13.

Signal-3L 3.0 – A 3-layer Approach for Predicting Signal Peptides

Signal-3L 3.0

:: DESCRIPTION

Signal-3L is an automated method for predicting signal peptide sequences and their cleavage sites in eukaryotic and bacterial protein sequences.

::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 Nov 16;363(2):297-303. Epub 2007 Aug 31.
Signal-3L: A 3-layer approach for predicting signal peptides.
Shen HB1, Chou KC.

Signal-CF – Predicting Signal Peptides

Signal-CF

:: DESCRIPTION

Signal-CF is an automated method for predicting signal peptide sequences and their cleavage sites in eukaryotic and bacterial protein sequences

::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 Jun 8;357(3):633-40. Epub 2007 Apr 5.
Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides.
Chou KC1, Shen HB.

TargetS – Predictor for Targeting Protein-ligand Binding Sites

TargetS

:: DESCRIPTION

TargetS is a new ligand-specific template-free predictor for targeting protein-ligand binding sites from primary sequences.

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

IEEE/ACM Trans Comput Biol Bioinform. 2013 Jul-Aug;10(4):994-1008. doi: 10.1109/TCBB.2013.104.
Designing template-free predictor for targeting protein-ligand binding sites with classifier ensemble and spatial clustering.
Dong-Jun Yu, Jun Hu, Jing Yang, Hong-Bin Shen, Jinhui Tang, and Jing-Yu Yang,

OSML – Predicting Protein-Ligand Binding Sites

OSML

:: DESCRIPTION

OSML is a query-driven dynamic machine learning model for predicting protein-ligand binding sites

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Dong-Jun Yu, Jun Hu, Hong-Bin Shen et al.,
Constructing Query-Driven Dynamic Machine Learning Model with Application to Protein-Ligand Binding Sites Prediction,
IEEE Trans Nanobioscience. 2015 Jan;14(1):45-58. doi: 10.1109/TNB.2015.2394328.