ANGLOR – Backbone Torsion Angle Predictor

ANGLOR

:: DESCRIPTION

ANGLOR is a machine-learning based algorithm for ab initio prediction of protein backbone torsion angles. For a given amino acid sequence, the real-value backbone torsion angles (phi and psi) for each residue are predicted by the combination of the neural network training and the support vector machine

::DEVELOPER

Yang Zhang’s Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

ANGLOR

:: MORE INFORMATION

Citation

PLoS One. 2008;3(10):e3400. doi: 10.1371/journal.pone.0003400. Epub 2008 Oct 15.
ANGLOR: a composite machine-learning algorithm for protein backbone torsion angle prediction.
Wu S1, Zhang Y.

PREDITOR – Predicting Torsion Angles in Proteins from NMR Chemical Shifts and/or Homology

PREDITOR

:: DESCRIPTION

PREDITOR is a program for PREDIcting φ, ψ, χ1, and ω TORsion angles in proteins from 13C, 15N and 1H chemical shifts and sequential homology. PREDITOR 30o-accuracy of predicting φ and ψ is close to 90%. The average χ1 accuracy is 84% while the ω accuracy is 99.98% for trans peptide bond identification and 93% for cis peptide bond identification.

::DEVELOPER

the Wishart Research Group, University of Alberta

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

PREDITOR: a web server for predicting protein torsion angle restraints.
Berjanskii MV, Neal S, Wishart DS.
Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W63-9.

SHIFTOR – Predicting Torsion Angles in Proteins from NMR Chemical Shifts and Sequence Homology

SHIFTOR

:: DESCRIPTION

SHIFTOR is a program for predicting φ, ψ, χ1, and ω torsion angles in proteins from 13C, 15N and 1H chemical shifts and sequential homology.

::DEVELOPER

the Wishart Research Group, University of Alberta

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Magn Reson Chem. 2006 Jul;44 Spec No:S158-67.
Accurate prediction of protein torsion angles using chemical shifts and sequence homology.
Neal S, Berjanskii M, Zhang H, Wishart DS.