distanceP Manual
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
Ma/trixPlot 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/)