Sephiroth 20140618 – Disulfide Connectivity Prediction

Sephiroth 20140618

:: DESCRIPTION

Sephiroth is a disulfide connectivity pattern predictor based on evolutionary information retrieved from Multiple Sequence Alignments (MSAs).

::DEVELOPER

ComplexCorr – Predict the Connectivity of Subunits within large Protein Complexes

ComplexCorr

:: DESCRIPTION

With ComplexCorr you can predict the connectivity of subunits within large protein complexes.

::DEVELOPER

ComplexCorr team

:: SCREENSHOTS

N/a

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

J Mol Biol. 2010 Nov 19;404(1):158-71. doi: 10.1016/j.jmb.2010.09.029. Epub 2010 Sep 22.
Coevolution predicts direct interactions between mtDNA-encoded and nDNA-encoded subunits of oxidative phosphorylation complex i.
Gershoni M1, Fuchs A, Shani N, Fridman Y, Corral-Debrinski M, Aharoni A, Frishman D, Mishmar D.

TargetDisulfide – Disulfide Connectivity Prediction with Modelled Protein 3D Structural Information and Random Forest Regression

TargetDisulfide

:: DESCRIPTION

TargetDisulfide: Disulfide Connectivity Prediction with Modelled Protein 3D Structural Information and Random Forest Regression

::DEVELOPER

Pattern Recognition and Bioinformatics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Yu DJ, Li Y, Hu J, Yang X, Yang JY, Shen HB.
Disulfide Connectivity Prediction Based on Modelled Protein 3D Structural Information and Random Forest Regression.
IEEE/ACM Trans Comput Biol Bioinform. 2015 May-Jun;12(3):611-21. doi: 10.1109/TCBB.2014.2359451. PMID: 26357272.

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.