OnWorks favicon

disulfinder - Online in the Cloud

Run disulfinder in OnWorks free hosting provider over Ubuntu Online, Fedora Online, Windows online emulator or MAC OS online emulator

This is the command disulfinder that can be run in the OnWorks free hosting provider using one of our multiple free online workstations such as Ubuntu Online, Fedora Online, Windows online emulator or MAC OS online emulator



disulfinder - cysteines disulfide bonding state and connectivity predictor


disulfinder [OPTIONS]


'disulfinder' is for predicting the disulfide bonding state of cysteines and their
disulfide connectivity starting from sequence alone. Disulfide bridges play a major role
in the stabilization of the folding process for several proteins. Prediction of disulfide
bridges from sequence alone is therefore useful for the study of structural and functional
properties of specific proteins. In addition, knowledge about the disulfide bonding state
of cysteines may help the experimental structure determination process and may be useful
in other genomic annotation tasks. 'disulfinder' predicts disulfide patterns in two
computational stages: (1) the disulfide bonding state of each cysteine is predicted by a
BRNN-SVM binary classifier; (2) cysteines that are known to participate in the formation
of bridges are paired by a Recursive Neural Network to obtain a connectivity pattern.


A. Ceroni, A. Passerini, A. Vullo and P. Frasconi. DISULFIND: a Disulfide Bonding State
and Cysteine Connectivity Prediction Server, Nucleic Acids Research, 34(Web Server
issue):W177-W181, 2006.

For the disulphide connectivity predictor see:

A. Vullo and P. Frasconi. Disulfide Connectivity Prediction using Recursive Neural
Networks and Evolutionary Information, Bioinformatics, 20, 653-659, 2004.

For the cystein bonding state predictor see:

P. Frasconi, A. Passerini, and A. Vullo. A Two-Stage SVM Architecture for Predicting the
Disulfide Bonding State of Cysteines, Proc. IEEE Workshop on Neural Networks for Signal
Processing, pp.25-34, 2002.
A.Ceroni, P.Frasconi, A.Passerini and A.Vullo. Predicting the Disulfide Bonding State of
Cysteines with Combinations of Kernel Machines, Journal of VLSI Signal Processing, 35,
287-295, 2003.


-a, --alternatives=NUMBER
alternative connectivity patterns (default=3)

-o, --output=DIR
output dir where predictions will be saved (default=$PWD)

-p, --psi2=FILE|DIR
input in psi2 format (PSI-BLAST Matrix in ASCII), either a single file or a
directory(?). Generate this with "blastpgp -j <N> -Q FILE" where N >= 2.

-r, --rootdir=DIR
work directory (default=~/disulfinder)

-k, --pkgdatadir=DIR
package data directory containing Models (default=/usr/share/disulfinder)

-F, --format={html|ascii}
output format type (default=ascii)

-d --blastdb=DIR
blastpgp -d option (default=/data/sp+trembl)

-c, --cleanpred
cleanup intermediate prediction files (default=false)

-P, --usepssm
use pssm instead of counts for profiles (default=false)

-C, --knownbondingstate
assume bonding state is known (one file for each chain in directory
<rootdir>/Predictions/Bondstate/Viterbi) (default=false)

-v, --version
disulfinder version

-?, --help
help screen


"disulfinder -a 1 -p /usr/share/doc/disulfinder/examples/res_id_41483.blastPsiMatTmb -o

Use disulfinder online using onworks.net services

Free Servers & Workstations

Download Windows & Linux apps

Linux commands