Tag: Disulfide

Disulfide by Design 2.12 – Disulfide Engineering in Proteins

Disulfide by Design 2.12

:: DESCRIPTION

Disulfide by Design (DbD2) is a redesigned and enhanced version of original DbD application for the rational design of disulfide bonds in proteins. For a given protein structural model, all residue pairs are rapidly assessed for proximity and geometry consistent with disulfide formation, assuming the residues were mutated to cysteines.

::DEVELOPER

Dombkowski Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Craig DB, Dombkowski AA.
Disulfide by Design 2.0: a web-based tool for disulfide engineering in proteins.
BMC Bioinformatics. 2013 Dec 1;14:346. doi: 10.1186/1471-2105-14-346. PMID: 24289175; PMCID: PMC3898251.

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

pLink-SS – Identification of Disulfide Bond Peptides

pLink-SS

:: DESCRIPTION

pLink-SS is a high-throughput mass spectrometry method for precise identification of disulfide-linked peptides.

::DEVELOPER

pLink-SS team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux
  • Python

:: DOWNLOAD

 pLink-SS

:: MORE INFORMATION

Citation

Mapping native disulfide bonds at a proteome scale.
Lu S, Fan SB, Yang B, Li YX, Meng JM, Wu L, Li P, Zhang K, Zhang MJ, Fu Y, Luo J, Sun RX, He SM, Dong MQ.
Nat Methods. 2015 Feb 9. doi: 10.1038/nmeth.3283.

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.