PDBtool v4.80 – Extract Structural Features from original PDB File

PDBtool v4.80

:: DESCRIPTION

PDBtool is an integrated software for handling PDB file. This tool could parse the coordinate, do SEQRES-ATOM mapping, calculate secondary structure and solvent accessibility, compute conformational letter, and reconstruct missing residues.

::DEVELOPER

Sheng Wang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

PDBtool

:: MORE INFORMATION

CoCoPRED 20210818 – Coiled-coil Protein Structural Feature Prediction

CoCoPRED 20210818

:: DESCRIPTION

CoCoPRED is a method of coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks

::DEVELOPER

Computational Systems Biology Group, Shanghai Jiao Tong University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

CoCoPRED

:: MORE INFORMATION

Citation

Feng SH, Xia CQ, Shen HB.
CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks.
Bioinformatics. 2021 Oct 30:btab744. doi: 10.1093/bioinformatics/btab744. Epub ahead of print. PMID: 34718416.

Distill 2.0 – Prediction of Structural Features of Proteins

Distill 2.0

:: DESCRIPTION

Distill is a suite of public servers for the prediction of structural features of proteins. The servers can be accessed all from a single interface which also allows the submission of multiple queries.

::DEVELOPER

Gianluca Pollastri group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2011 Oct 15;27(20):2812-9. doi: 10.1093/bioinformatics/btr494. Epub 2011 Aug 27.
SCLpred: protein subcellular localization prediction by N-to-1 neural networks.
Mooney C1, Wang YH, Pollastri G.

Amino Acids. 2013 Aug;45(2):291-9. doi: 10.1007/s00726-013-1491-3. Epub 2013 Apr 9.
SCL-Epred: a generalised de novo eukaryotic protein subcellular localisation predictor.
Mooney C1, Cessieux A, Shields DC, Pollastri G.

Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins.
Baú D, Martin AJ, Mooney C, Vullo A, Walsh I, Pollastri G.
BMC Bioinformatics. 2006 Sep 5;7:402.

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