Provided by: disulfinder_1.2.11-5_amd64 bug


       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


           default package data directory

           default work directory


       The work directory is not cleaned up automatically.


       Ceroni A, Passerini A, Vullo A, Frasconi P.

       Packaging by Laszlo Kajan <>




       See official web site for help:

       DISULFIND: a disulfide bonding state and cysteine connectivity prediction server: