distanceP
Man Page
* The description is given for command line version. However the options are all available on the www interface.

distanceP
NAME
distanceP - distanceP predicts distance constraint probabilities between residues in a protein chain. distanceP is an update of the sowhat server.
SYNOPSIS
distanceP [options] datafile
DESCRIPTION
distanceP takes any file containing the data in any format. All characters that does not match those of the standard 20 letter protein alphabet are discarded. The letter "X" is a wild card though. The output produced by distanceP is generated in the MatrixPlot data format (mp format). distanceP consist of multiple C programs and a C shell script.
OPTIONS
-dmin <number>
Number. Specify the minimum sequence separation (residues) for which the constraint probability should be calculated. Minimum and default value is 2.
-dmax <number>
Number. Specify the maximum sequence separation (residues) for which the constraint probability should be calculated. Maximum and default value is 50.
-seqletters y|n
Show sequence letters along the edges of the distance plot.
-pdbentry <pdbname>
Generates prediction of given PDB entry, and compares to the known 3D coordinates in the database. The default chain is the first one in the PDB entry. Any sequence submitted to the program will with this option be ignored.
-pdbchain <pdbchain>
Works only with the option -pdbentry, and is used to specify the chain identifier in that PDB entry.
-nprof <number>
Number of profile sequences generated from the query sequence. Maximum number of profile sequences is 40.
EXAMPLE
cat datafile | distanceP -dmin 2 -dmax 25 > dist.mp
(You can then use MatrixPlot to generate a postscript file)
AUTHOR
Jan Gorodkin, gorodkin@cbs.dtu.dk, April 1999.
REFERENCES
  • Using Sequence Motifs for Enhanced Neural Network Prediction of Protein Distance Constraints. J. Gorodkin, O. Lund, C. A. Andersen, and S. Brunak. In proceedings of the seventh international conference for molecular biology, eds. T. Lengauer, R. Schneider, P. Bork, D. Brutlag, J. Glasgow, H-W. Mewes, and R. Zimmer, pp 95-105, 1999. (http://www.cbs.dtu.dk/services/distanceP/)
  • Protein distance constraints predicted by neural networks and probability density functions. O. Lund, K. Frimand, J. Gorodkin, H. Bohr, J. Bohr, J. Hansen, and S. Brunak. Protein Engineering, Volume 10, Issue 11: November 1997. 1241-1248. (http://www.cbs.dtu.dk/services/CPHmodels/)

  • Last updated July 28, 1999 Jan Gorodkin *
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